The number of input and output nodes of MLPs are generally constrained by given problems , but the number of hidden nodes can be optimized by user experiences . Thus our design method is using optimized number of hidden nodes in order to improve the processing speed , especially in field of a repeated processing such as image processing , pattern recognition , etc . Cryptography is the method to protect information from undesirable attackers while transmitted or stored . KeywordsSecurity , Image Processing , AES , Encryption and Decryption Pavement cracks as a common highway damage , the complex road condition and the underdeveloped image acquisition software and hardware technology make the achieved pavement images with high noise , it brings difficult image processing and low crack detection . Therefore , it is significant to study a crack detection system processing high noisy images . To reduce this problem , we propose to cut the surface into smaller parts , where a ridge detector acts as guideline , and then unfold each patch individually , so as to obtain low distortions . Having a solely surface based representation of the cortex and expressing the image operations using multi-scale differential invariants in terms of scale-space derivatives as done in this work is a natural choice both in terms of conceptual and algorithmic simplicity . Moreover , explicitly handling the multi-scale nature of the data is necessary to obtain robust results . Image segmentation based on contour extraction usually involves three stages of image operations : feature extraction , edge detection and edge linking . This paper is devoted to the first stage : a method to design feature extractors used to detect edges from noisy and/or blurred images . For the target video stream 1280 χ 720 @ 50 fps ( 74.25 MHz pixel clock ) it reached only 2 fps . Therefore , to speed-up the application , it was decided to move some of the image processing and analysis operations to the programmable logic . This allowed to obtain real-time image processing i.e. 50 fps , with power consumption of less than 4W . Shadow is an important concern in aerial and satellite remotely sensed imagery . Consequently , shadow detection is important for many image processing and analysis operations . Among shadow detection methods , there exist methods that apply a single threshold to a shadow index to determine the shadow cover . The resulting images contains enhanced information as compared to individual images and also apply Contrast Enhancement technique to improve visibility of medical image details without introducing unrealistic visual appearances and/or unwanted artefacts . It also gives the quality comparison study of original medical images before fusion , after applying PCA and various existing techniques for contrast enhancement for those medical images . In local contrast image enhancement method we are using unsharp masking technique to enhance the local detail of an image . The main aim of using this combination of local and global method is to preserve the brightness of an image when contrast image enhancement is done . In this paper , some morphological transformations are used to detect the background in images characterized by poor lighting . Lately , contrast image enhancement has been carried out by the application of two operators based on the Weber 's law notion . The first operator employs information from block analysis , while the second transformation utilizes the opening by reconstruction , which is employed to define the multibackground notion . Contrast enhancement uses scaling to remove internal noise of dark images using DCT technique . Here in this paper we are presenting some techniques used for contrast enhancement of image . However , the over enhancement of noise the commonly used enhancement method produces in relatively homogeneous regions is still a challenging problem . This paper proposes a low-complexity gray-level transformation method for this contrast enhancement problem . Firstly , we calculate a grid size based on the image ’s dimension and extract the high frequency from each patch as a weighting matrix . 50 typical images are simulated . The power reduction , contrast ratio enhancement , and MSSIM are explored to evaluate the 3 above dimming algorithms , the simulation results show that : IMF method does not apply to the low average luminance images ; error correction method is suitable for a wide range of images , but the energy-saving rate is not high ; though the energy-saving rate of the CDF method is higher than the other two kinds of algorithms , the image quality has a some kind of distortion . The parameters of the sigmoid function were determined by using three different methods . The effect of gain ’s value on the contrast enhancement process was studied . The new enhancing approach has been successfully , applied in several gray scale images . These algorithms are tested on various types of fingerprint images containing low-quality unrecoverable region and the results obtained from the proposed method were compared with those obtained from well-known gradient-based and PCMs methods . The proposed method was also used to study the contrast enhancement process with our previously developed modified histogram equalisation MHE based on adaptive inverse hyperbolic tangent AIHT method . The MPCMs method exhibits better segmentation than the traditional methods in both normal and low-contrast image segmentations , as evident from the estimated matching scores as well as ROC graph . The system is minimizing X-ray interference with optical components , and offers improved contrast between internal tissues to be used as prior information for the FMT inversion procedure . For the contrast enhancement goal , a dual-energy approach has been investigated and evaluated . System configuration , acquisition protocol and processing algorithms are described in the following sections . Investigates the dynamic range compression and contrast enhancement properties of shunting inhibition cellular neural networks ( SICNN ) used for colour image enhancement . First , the SICNN is formulated according to its structure and then the formulation is expressed in a digital format so that simulations can be performed . Earlier implementations of partially negative filters have concentrated on spectral reconstruction . Here we show a novel method for implementing partially negative optical filters for contrast-enhancement purposes in imaging applications . We describe the method and its requirements , and show its feasibility with color chart and dental imaging examples . The four basic analogue computer operations are identified and utilized in a vector-matrix format for the computer equations . This format , which is reminiscent of the state equation model for a dynamical system , displays clearly the task of analogue computer programming and scaling for a given system model , the first as a transformation on the structure of the initial system model , the latter as a linear transformation on the system variables in the restructured form to computer variables . Relating to scaling , the formulation leads directly to a simple and easily used scaling procedure which is a variant of the ' ' normalised variable ' ' or ' ' ratio of scale-factor ' ' method . Scaling supply voltage to near-threshold is a very effective approach in reducing the energy consumption of computer systems . However , executing below the safe operation margin of supply voltage introduces high number of persistent failures , especially in memory structures . In practice , different NFs carry out similar processing steps on the same packet , and our experiments indeed show a significant improvement of the network performance when using OpenBox . Moreover , OpenBox readily supports smart NF placement , NF scaling , and multi-tenancy through its controller . This paper addresses the problem of scaling interconnects to nanometric dimensions in future VLSI applications . Traditional copper interconnects are compared to innovative ones made by bundles of metallic carbon nanotubes . One of the major issues concerning both the cloud service providers and consumers is the automatic scalability of resources ( i.e. , compute , storage and bandwidth ) in response to the highly unpredictable demands . To this end , an opportunity exists to harness the predictive and diagnostic capabilities of machine learning approaches to incorporate dynamic scaling up and scaling down of resources without violating the Service Level Agreements ( SLA ) and simultaneously ensuring adequate revenue to the providers . This paper proposes , implements and evaluates a Bayesian Networks based predictive modeling framework to provide for an autonomic scaling of utility computing resources in the Cloud Computing scenario . Abstract A software tool named ionoScaler ( ionogram Scaler ) was developed to carry out manual and automatic scaling of vertical incidence ionograms . Additionally , ionosonde Total Electron Content ( ITEC ) can be estimated by this tool through reconstructing the bottomside and topside electron density profiles of ionograms . The findings are used to arrive at a chunking and compression approach which maximizes deduplication savings while minimizing the generated metadata and producing a uniform chunk size distribution . Scaling of deduplication processing with data size is achieved using a RAM frugal chunk hash index and data partitioning - so that memory , CPU , and disk seek resources remain available to fulfill the primary workload of serving IO . We present the architecture of a new primary data deduplication system and evaluate the deduplication performance and chunking aspects of the system . Certain topographic features , such as undercut banks and porous structures not represented in the DEM , resulted in modeled values that failed to reflect accurate velocities but were explained by the presence of these features . By utilizing a rectilinear grid , scaling grid spacing to computational resource limitations , leveraging a cloud computing system , and selecting simplified rules for discharge distribution for boundary conditions and model run times , we were able to successfully automate the hydraulic modeling process . Overall , automation of hydraulic model generation met precision and accuracy needs of habitat condition models , lowered labor costs , and standardized the modeling workflow , and enabled high survey volume processing needs . A simple connection is shown to exist between the Hadamard transforms of a discrete signal A and a signal B obtained from A in three different ways : a ) time scaling of A ; b ) expanding A by means of zero insertion ; and c ) letting A repeat itself . Both one-dimensional and two-dimensional cases are investigated . We study continuous time job scheduling and server speed scaling problems with focus on minimizing service and job waiting costs . Jobs arrive over time and are to be served by a server . The computational efficiency of SAM opens up the possibility of training on tasks that require storing a large amount of information over long sequences . Here we show this is possible in practice , by scaling tasks to a large scale via an exponentially increasing curriculum . We parametrized three of the tasks described in Section [ reference ] : associative recall , copy , and priority sort , with a progressively increasing difficulty level which characterises the length of the sequence and number of entries to store in memory . section : Discussion Scaling memory systems is a pressing research direction due to potential for compelling applications with large amounts of memory . We have demonstrated that you can train neural networks with large memories via a sparse read and write scheme that makes use of efficient data structures within the network , and obtain significant speedups during training . The DenseNet block contains n layers of convolution layer with growth rate of g . The transition layer has a convolution layer with kernel for scaling down purpose , followed by a max pooling layer with stride . The transition scale down ratio in transition layer is . subsection : Meta - transfer learning ( MTL ) As shown in Figure [ reference ] ( b ) , our proposed meta - transfer learning ( MTL ) method optimizes the meta operations Scaling and Shifting ( SS ) through HT meta - batch training ( Section [ reference ] ) . Figure [ reference ] visualizes the difference of updating through SS and FT . In general , we found the shared - norm approach to be the most effective way to resolve this problem . The no - answer and merge approaches were moderately effective , but we note that they do not resolve the scaling problem inherent to the softmax objective we discussed in Section [ reference ] , which might be why they lagged behind . The sigmoid objective function reduces the paragraph - level performance considerably , especially on the TriviaQA datasets . The main challenge when is very large ( in the order of one million in this paper ) is the fact that computing all inner products between and all embeddings becomes prohibitively slow during training ( even when exploiting matrix - matrix multiplications and modern GPUs ) . Several approaches have been proposed to cope with the scaling issue : importance sampling , Noise Contrastive Estimation ( NCE ) , self normalizing partition functions or Hierarchical Softmax – they all offer good solutions to this problem . Similarly , even for this tiny problem size , the recent analysis that proves a near optimal sample complexity in fixed horizon problems only guarantees that we will have fewer than suboptimal episodes . While these bounds may be acceptable in worst case scaling , they are not of much practical use . subsection : One - hot features The character recognition is the application of image processing used to identify the character . This application is area needs a series of image processing operations , which help us in our research work . The invention provides a multi-point touch-based image operation interaction system , which comprises touch screen equipment , a kernel driving module and an image processing module , wherein the touch screen equipment is used for displaying an image , receiving touch instructions of a plurality of touch points of a user , acquiring physical coordinates of each touch point , generating corresponding codes and transmitting the generated codes at a certain time interval ; the kernel driving module is used for acquiring the codes generated by the touch screen equipment according to the time interval at which the touch screen equipment transmits the codes , performing operational analysis , generating corresponding message codes according to the results of the operational analysis and transmitting the message codes to the image processing module ; and the image processing module is used for performing corresponding image processing operations on a currently displayed image of the touch screen equipment according to the received message codes . The invention also provides a multi-point touch-based image operation interaction method . Such biometric technology is defined as an automated method of identifying and authenticating a person 's identity based on the user 's physiological and behavioral characteristics . The purpose of this research is to know the accuracy level of iris pattern identification using Hidden Markov Model with Gabor wavelet method , to know the iris feature extraction process as part of image processing process using Gabor wavelet method . The method used for data processing is by using Hidden Markov Model , Gabor Wavelet and 2D Order Statistic Filter . Navigation device ( 200 , 300 ) , the image recording apparatus for recording image data and ( 300 ) , receives the incident signal indicating the occurrence of incidents , processing resources for performing image data processing operation in response to the incident signal ( 202 ) with a door . A display device and a method for displaying a broadcasting signal are provided to display a predetermined image unless a viewing rate of a received program corresponds to a user-specific viewable rate , thereby automatically displaying programs and contents appropriate to a viewable rate of a user . A display device comprises a display unit(20 ) , a receiving unit(10 ) for receiving a broadcasting signal including a program having a viewing rate and extracting the view rating of the program , an image processor(60 ) for performing an image process operation on the received program , and a controller(70 ) for comparing the viewing rate of the received program with a user-specific viewable rate . The controller controls the image processor to display a predetermined image unless the viewing rate of the received program corresponds to the user-specific viewable rate . And , a first camera ( 1 ) , the water meter ( 2 ) , the elasticity measuring stick ( 5 ) , oil measuring stick ( 7 ) and keratin control information input from the measurement stick ( 8) a system for collecting information on the skin a central processing unit ( 9 ) for transmitting to and ; The central processing unit 9 the image signals transmitted to the computer 10 or computer 10 , the image analysis calculation process the image and numeric signals back to the central processor from the device 9 , the interface unit ( 11 to transmit a ) Wow ; And a computer 10 for image processing and operation , and outputs the number signal to analyze a video signal transmitted through the interface unit 11 ; And the LCD display unit 12 , which via the central processing unit 9 the information obtained by the means for collecting the information of the skin to the output value ; It is made of a monitor 13 for outputting the video signal transmitted from said central processing unit ( 9 ) . The present invention as above is the human body ( usually the face ) comprehensive information of the pore distribution of the skin and pores , the size of the skin , pigmentation , keratin and other atopy and accurately measure the moisture and elasticity , oil and dead skin cells in scientific and diagnose it yourself of cosmetics you can prescribe for your skin type and to be able to scientifically yet affordable skin care . Three base classifiers ( K-Nearest Neighbors ( KNN ) , Support Vector Machine ( SVM ) , and Decision Tree ( DT ) ) and two ensemble methods ( Bootstrap Aggregating ( Bagging ) and Boosting ) were employed and compared . Results showed that : ( 1 ) the simulated Gaussian noise and EMG noise had an impact on accuracy , while simulated spike noise did not , which is of great significance for the future application of driver fatigue detection ; ( 2 ) the influence on noise performance was different based on each classifier , for example , the robust effect of classifier DT was the best and classifier SVM was the weakest ; ( 3 ) the influence on noise performance was also different with each feature set where the robustness of feature set FE and the combined feature set were the best ; and ( 4 ) while the Bagging method could not significantly improve performance against noise addition , the Boosting method may significantly improve performance against superimposed Gaussian and EMG noise . The entropy feature extraction method could not only identify driver fatigue , but also effectively resist noise , which is of great significance in future applications of an EEG-based driver fatigue detection system . The main technical challenge of this transition lies in the relatively lower x-ray flux obtained from conventional sources , leading to photon-limited data contaminated by Poisson noise . Several issues that must be understood in order to design and optimize DEI imaging systems with respect to noise performance are addressed . Specifically , we : ( a ) develop equations describing the noise properties of DEI images , ( b ) derive the conditions under which the DEI algorithm is statistically optimal , ( c ) characterize the imaging performance that can be obtained as measured by task-based metrics , and ( d ) consider image-processing steps that may be employed to mitigate noise effects . On this set , the algorithm extracts significant signatures by means of statistical correlations ; the experimental results and the analysis of computational complexity have proved that the algorithm presents the best performance at the highest level of the indexing hierarchy , where the computational complexity is the lowest . Our method has been evaluated by the following methodologies : ( a ) the study of the computational complexity for signature generation ; ( b ) the comparison with analogous methods based on texture analysis by reporting the performance obtained on the same database ( Brodatz ) ; and ( c ) the evaluation of the robustness of the hierarchical indexing in different color spaces , by querying the database with different versions of the original images obtained by noise addition ( gaussian and scanner acquisition noise and lossy compression distortion ) , brightness and contrast enhancement , color and scale adjustment and rotation . Even if our method is designed for texture databases , experiments show satisfactory results also on a real heterogeneous photographic database . There are practical demands to reuse this abundant data in different acoustic environments . To transform such source data for a target domain , developers can use channel mapping and noise addition . However , channel mapping of the data is difficult without stereo mapping data or impulse response data . Several standard images were tested and the experimental results were compared with those of other published schemes . The results demonstrate that our proposed scheme can achieve not only better imperceptibility , but also stronger robustness against common signal processing operations and lossy compressions , such as filtering , noise addition , scaling , sharpening , rotation , cropping , and JPEG/JPEG2000 compression . Watermark detection does not require the original carrier image , that is , the detection is more convenient when digital image is posted and propagated on the Internet , by implementing a blind detection of hidden information . The experiments show that the algorithm has invisibility and strong robustness , effectively resisting noise adding , cropping , filtering , and other common attacks . First the host audio is divided into several segments which are simultaneously converted to DCT domain , then the DC coefficients which is insensitivity to the human ear hearing and has the biggest hearing margin are selected . Based on the stability of SVD , the watermark is embedded by changing SVD.Experimental results show that the watermarked audio has good imperceptibility and is robust against different kinds of attacks , such as noise adding , low-pass filtering , re-sampling , echo and Mp3 compression . The robustness and the imperceptibility of watermarked audio reach a good balance . In this paper , we propose a privacy-preserving semi-generative adversarial network ( PPSGAN ) that selectively adds noise to class-independent features of each image to enable the processed image to maintain its original class label . Our experiments on training classifiers with synthetic datasets anonymized with various methods confirm that PPSGAN shows better utility than other conventional methods , including blurring , noise-adding , filtering , and generation using GANs . Ergonomics 51 , 719 - 736 ] . This exemplary study promises various options for research on noise effects by the use of virtual built environments which are of high plausibility and unlimited variability . ( 3 ) The gray image is embedded to enlarge the watermark volume . ( 4 ) The algorithm can extract the watermark without the help from the original digital audio signal .Experimental results show that the proposed watermarking scheme is inaudible and robust against various signal processing such as re-sampling , re-quantifying , low pass filtering , MP3 compression , and noising adding . SVD(Singular Value Decomposition ) is used to extract singular value which is embedded copyright protection into digital audio based on modular arithmetic . Experimental results show that the proposed watermarking scheme is inaudible and sensitive to several operations , such as re-sampling , re-quantifying , low pass filtering , noising adding , and lossy compression . 3 . A probabilistic approach to noise-added systems . Part 2 : Application . In this paper , we propose a novel method , which uses layout extracted parameters of coupling interconnect and pre-characterized parameters of driver to calculate an effective holding resistance . The noise-aware effective holding resistance dramatically improves the accuracy for noise magnitude and energy estimation . The proposed method is simple and efficient . A self-recovering algorithm is employed , that hides the image digest into some Wavelet subbands to detect possible malevolent object manipulation undergone by the image ( object replacing and/or deletion ) . The Semi-fragility makes the scheme tolerant for JPEG lossy compression as low as quality of 70 % , and locate the tempered area accurately . In addition , the system ensures more security because the embedded watermarks are protected with private keys . At last the watermarking image is embedded into the region of the DHT coefficient block . The experimental results show that the scheme is easy to be realized and the embedded watermark is effectively robust against various image processing operations such as JPEG lossy compression , noise , cropping , scaling , filtering etc . Both the newly projected points and the watermark table are used to reconstruct the watermark . In our experiments , we have tested our scheme to see how it works on original images modified by JPEG lossy compression , blurring , cropping , rotating and sharpening , and the experimental results show that our method is very robust and indeed workable . The algorithm first partitioned an image into many small blocks with appropriate size , employed TSVD to transform and described these blocks on low-dimension representation , and then detected the similarity between these blocks . Experimental results also demonstrate the validity of the proposed approach on several forged images undergone retouching or modifications to conceal traces of forgery , such as lossy JPEG compression , Gaussian blur filter , Gaussian white noise contamination and so on . One part was transformed by DCT ( discrete cosine transform ) , and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA ; the other part was by DWT ( discrete wavelet transform ) , in which the key fusion was achieved by an ergodic matrix to embed the watermark . Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression , image darken , Gaussian low-pass filtering , contrast decreasing , salt-pepper noise , and geometric attacks such as rotation and cropping . The introduced redundancy brings simplicity and accuracy for feature calculation . We also describe the applied postprocessing steps with the aim of stabilizing those features under acceptable image manipulations , namely lossy JPEG compression and additive noise corruption . Many experiments and comparative studies are performed to show the effectiveness of our technique in generating content signatures based on invariant image features , as well as to demonstrate its superiority when compared with a redundant wavelet approach Research was conducted in support of One World Terrain through development of best practices for the delivery of a raster mosaic via cloud hosting service , created using OptimizeRasters Geoprocoessing Toolbox and the Mosaic Dataset Configuration Script . Though ultimately successful in developing the raster mosaic and hosting it online ; JPEG compression lossiness was a key issue with the larger Rose Bowl dataset . Additionally , hosting the imagery via ArcGIS Online was found to increase the compressed file size ; making it comparable to the original file size of the data . Although the method renews repeatedly assignment of indices , the introduction of a two-dimensional histogram of the indices reduces remarkably the amount of computation required for the repetition . Simulation results show that the application of the method to JPEG lossless coding decreases the coding rates of 8-bit color quantized images by 0.39 - 0.80 bits/pel and that these coding rates are 0.78 - 1.81 bits/pel lower than those of GIF . In light of poor performance in lossless compression of JPEG , combined with the reality , a new improved idea of the lossless compression patent algorithm which using shuffle is proposed . The correctness of simulation result is verified by experiment . DPCM ( differential pulse code modulation ) coding is widely used in many applications including lossless JPEG compression . DPCM decoding is inherently a 1-indexed or 2-indexed recurrence relation . Experimental results show that invisibility and robustness are ensured . Performance in terms of invisibility and robustness of the proposed algorithm is better than that applying on DCT-DWT combined domain for the lossy JPEG compression attacks , speckle and Gaussian noise . The result is a set of 2D objects , which can be used as input for classical computer vision applications , in particular for object recognition . Our approach makes it possible to apply classical tools of 2D image processing to solve problems of 3D robot mapping , e.g. landmark recognition In this paper , we present two ASIPs . PXL ASIP , which has a reconfigurable multi bank memory module and an SIMD type computation pipeline , is designed for pixel level image processing , while 2D ASIP , which has slide register module and reconfigurable ALU modules , is designed for 2D image processing . PXL ASIP can perform 4 to 10 times faster compared to its base processor , and 2D ASIP can perform 5 to 43 times faster compared to its base processor . Context has been successfully applied to 2D image processing in the tasks of object detection , object recognition , etc . However , it is usually ignored in 3D object tracking . The supervised learning algorithm is then executed to obtain a compact CNN architecture , called as Wave-CNN . The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications . Existing techniques for texturing curved surfaces are either only applicable for a limited subsetof surface representations ( 3d painting of parametric patches or dense polygonal meshes for example ) , or do not lend themselvesnaturally to interactive texture editing ( e.g. procedural and solid textures ) . Although such methods have been used to produce stunning effects , there is a lack of flexible and general purpose tools , such as those provided by 2d image processing applications . This work argues that interactive texturing could benefit from a more cohesive paradigm built around a kernel of powerful and general operations . Segmentation is the process of dividing an image into segments that have similar attributes . There are a variety of traditional segmentation technique in the literature , mainly for 2D image processing applications . 3D segmentation is a much more complex problem . A CMOS fingerprint recognition SoC with embedded column-parallel processors is optimized for 2D digital image processing . The processor employs self-configuration features for adaptive filter operations and the pixel includes a sensing block , ADC and frame memory without area penalty . Making such adjustments to video footage is harder still . We describe and study a set of 2D image operations , based on multiscale image analysis , that are easy and straightforward and that can consistently modify perceived material properties . These operators first build a subband decomposition of the image and then selectively modify the coefficients within the subbands . With the ability to perform up to 16 ALU operations and 12 texture accesses in a single pass , Direct3D ’s pixel pipeline has begun to look like a powerful programmable 2D image processing machine . In this chapter , we will outline several techniques which utilize DirectX 8.1 ’s 1.4 pixel shaders to perform 2D image processing effects such as blurs , edge detection , transfer functions and morphology . Along the way , we will point out applications of these 2D image operations to 3D game graphics . The nonlocal means ( NL-Means ) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods . Although NL-Means is very popular in the field of 2D image signal processing , it has not received enough attention in the field of 1D signal processing . This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum . Realizations using analog circuits for the two-dimensional ( 2-D ) image processing operations , such as correlation and mathematical morphology , are presented . These analog realizations can operate directly on raster scanned images in real-time . However , an important class of global operations , namely those that are reversible-that can be computed in forward or reverse order on a data structure-can be computed in parallel using a restricted form of divide and conquer called split and merge . These reversible operations include the global operations mentioned above , and many more besides-even such non-image processing operations as parsing , string search , and sorting . The split and merge method will be illustrated by application of it to these algorithms .
The number of input and output nodes of MLPs are generally constrained by given problems , but the number of hidden nodes can be optimized by user experiences . Thus our design method is using optimized number of hidden nodes in order to improve the processing speed , especially in field of a repeated processing such as image processing , pattern recognition , etc .
Cryptography is the method to protect information from undesirable attackers while transmitted or stored . KeywordsSecurity , Image Processing , AES , Encryption and Decryption
Pavement cracks as a common highway damage , the complex road condition and the underdeveloped image acquisition software and hardware technology make the achieved pavement images with high noise , it brings difficult image processing and low crack detection . Therefore , it is significant to study a crack detection system processing high noisy images .
To reduce this problem , we propose to cut the surface into smaller parts , where a ridge detector acts as guideline , and then unfold each patch individually , so as to obtain low distortions . Having a solely surface based representation of the cortex and expressing the image operations using multi-scale differential invariants in terms of scale-space derivatives as done in this work is a natural choice both in terms of conceptual and algorithmic simplicity . Moreover , explicitly handling the multi-scale nature of the data is necessary to obtain robust results .
Image segmentation based on contour extraction usually involves three stages of image operations : feature extraction , edge detection and edge linking . This paper is devoted to the first stage : a method to design feature extractors used to detect edges from noisy and/or blurred images .
For the target video stream 1280 χ 720 @ 50 fps ( 74.25 MHz pixel clock ) it reached only 2 fps . Therefore , to speed-up the application , it was decided to move some of the image processing and analysis operations to the programmable logic . This allowed to obtain real-time image processing i.e. 50 fps , with power consumption of less than 4W .
Shadow is an important concern in aerial and satellite remotely sensed imagery . Consequently , shadow detection is important for many image processing and analysis operations . Among shadow detection methods , there exist methods that apply a single threshold to a shadow index to determine the shadow cover .
The resulting images contains enhanced information as compared to individual images and also apply Contrast Enhancement technique to improve visibility of medical image details without introducing unrealistic visual appearances and/or unwanted artefacts . It also gives the quality comparison study of original medical images before fusion , after applying PCA and various existing techniques for contrast enhancement for those medical images .
In local contrast image enhancement method we are using unsharp masking technique to enhance the local detail of an image . The main aim of using this combination of local and global method is to preserve the brightness of an image when contrast image enhancement is done .
In this paper , some morphological transformations are used to detect the background in images characterized by poor lighting . Lately , contrast image enhancement has been carried out by the application of two operators based on the Weber 's law notion . The first operator employs information from block analysis , while the second transformation utilizes the opening by reconstruction , which is employed to define the multibackground notion .
Contrast enhancement uses scaling to remove internal noise of dark images using DCT technique . Here in this paper we are presenting some techniques used for contrast enhancement of image .
However , the over enhancement of noise the commonly used enhancement method produces in relatively homogeneous regions is still a challenging problem . This paper proposes a low-complexity gray-level transformation method for this contrast enhancement problem . Firstly , we calculate a grid size based on the image ’s dimension and extract the high frequency from each patch as a weighting matrix .
50 typical images are simulated . The power reduction , contrast ratio enhancement , and MSSIM are explored to evaluate the 3 above dimming algorithms , the simulation results show that : IMF method does not apply to the low average luminance images ; error correction method is suitable for a wide range of images , but the energy-saving rate is not high ; though the energy-saving rate of the CDF method is higher than the other two kinds of algorithms , the image quality has a some kind of distortion .
The parameters of the sigmoid function were determined by using three different methods . The effect of gain ’s value on the contrast enhancement process was studied . The new enhancing approach has been successfully , applied in several gray scale images .
These algorithms are tested on various types of fingerprint images containing low-quality unrecoverable region and the results obtained from the proposed method were compared with those obtained from well-known gradient-based and PCMs methods . The proposed method was also used to study the contrast enhancement process with our previously developed modified histogram equalisation MHE based on adaptive inverse hyperbolic tangent AIHT method . The MPCMs method exhibits better segmentation than the traditional methods in both normal and low-contrast image segmentations , as evident from the estimated matching scores as well as ROC graph .
The system is minimizing X-ray interference with optical components , and offers improved contrast between internal tissues to be used as prior information for the FMT inversion procedure . For the contrast enhancement goal , a dual-energy approach has been investigated and evaluated . System configuration , acquisition protocol and processing algorithms are described in the following sections .
Investigates the dynamic range compression and contrast enhancement properties of shunting inhibition cellular neural networks ( SICNN ) used for colour image enhancement . First , the SICNN is formulated according to its structure and then the formulation is expressed in a digital format so that simulations can be performed .
Earlier implementations of partially negative filters have concentrated on spectral reconstruction . Here we show a novel method for implementing partially negative optical filters for contrast-enhancement purposes in imaging applications . We describe the method and its requirements , and show its feasibility with color chart and dental imaging examples .
The four basic analogue computer operations are identified and utilized in a vector-matrix format for the computer equations . This format , which is reminiscent of the state equation model for a dynamical system , displays clearly the task of analogue computer programming and scaling for a given system model , the first as a transformation on the structure of the initial system model , the latter as a linear transformation on the system variables in the restructured form to computer variables . Relating to scaling , the formulation leads directly to a simple and easily used scaling procedure which is a variant of the ' ' normalised variable ' ' or ' ' ratio of scale-factor ' ' method .
Scaling supply voltage to near-threshold is a very effective approach in reducing the energy consumption of computer systems . However , executing below the safe operation margin of supply voltage introduces high number of persistent failures , especially in memory structures .
In practice , different NFs carry out similar processing steps on the same packet , and our experiments indeed show a significant improvement of the network performance when using OpenBox . Moreover , OpenBox readily supports smart NF placement , NF scaling , and multi-tenancy through its controller .
This paper addresses the problem of scaling interconnects to nanometric dimensions in future VLSI applications . Traditional copper interconnects are compared to innovative ones made by bundles of metallic carbon nanotubes .
One of the major issues concerning both the cloud service providers and consumers is the automatic scalability of resources ( i.e. , compute , storage and bandwidth ) in response to the highly unpredictable demands . To this end , an opportunity exists to harness the predictive and diagnostic capabilities of machine learning approaches to incorporate dynamic scaling up and scaling down of resources without violating the Service Level Agreements ( SLA ) and simultaneously ensuring adequate revenue to the providers . This paper proposes , implements and evaluates a Bayesian Networks based predictive modeling framework to provide for an autonomic scaling of utility computing resources in the Cloud Computing scenario .
Abstract A software tool named ionoScaler ( ionogram Scaler ) was developed to carry out manual and automatic scaling of vertical incidence ionograms . Additionally , ionosonde Total Electron Content ( ITEC ) can be estimated by this tool through reconstructing the bottomside and topside electron density profiles of ionograms .
The findings are used to arrive at a chunking and compression approach which maximizes deduplication savings while minimizing the generated metadata and producing a uniform chunk size distribution . Scaling of deduplication processing with data size is achieved using a RAM frugal chunk hash index and data partitioning - so that memory , CPU , and disk seek resources remain available to fulfill the primary workload of serving IO . We present the architecture of a new primary data deduplication system and evaluate the deduplication performance and chunking aspects of the system .
Certain topographic features , such as undercut banks and porous structures not represented in the DEM , resulted in modeled values that failed to reflect accurate velocities but were explained by the presence of these features . By utilizing a rectilinear grid , scaling grid spacing to computational resource limitations , leveraging a cloud computing system , and selecting simplified rules for discharge distribution for boundary conditions and model run times , we were able to successfully automate the hydraulic modeling process . Overall , automation of hydraulic model generation met precision and accuracy needs of habitat condition models , lowered labor costs , and standardized the modeling workflow , and enabled high survey volume processing needs .
A simple connection is shown to exist between the Hadamard transforms of a discrete signal A and a signal B obtained from A in three different ways : a ) time scaling of A ; b ) expanding A by means of zero insertion ; and c ) letting A repeat itself . Both one-dimensional and two-dimensional cases are investigated .
We study continuous time job scheduling and server speed scaling problems with focus on minimizing service and job waiting costs . Jobs arrive over time and are to be served by a server .
The computational efficiency of SAM opens up the possibility of training on tasks that require storing a large amount of information over long sequences . Here we show this is possible in practice , by scaling tasks to a large scale via an exponentially increasing curriculum . We parametrized three of the tasks described in Section [ reference ] : associative recall , copy , and priority sort , with a progressively increasing difficulty level which characterises the length of the sequence and number of entries to store in memory .
section : Discussion Scaling memory systems is a pressing research direction due to potential for compelling applications with large amounts of memory . We have demonstrated that you can train neural networks with large memories via a sparse read and write scheme that makes use of efficient data structures within the network , and obtain significant speedups during training .
The DenseNet block contains n layers of convolution layer with growth rate of g . The transition layer has a convolution layer with kernel for scaling down purpose , followed by a max pooling layer with stride . The transition scale down ratio in transition layer is .
subsection : Meta - transfer learning ( MTL ) As shown in Figure [ reference ] ( b ) , our proposed meta - transfer learning ( MTL ) method optimizes the meta operations Scaling and Shifting ( SS ) through HT meta - batch training ( Section [ reference ] ) . Figure [ reference ] visualizes the difference of updating through SS and FT .
In general , we found the shared - norm approach to be the most effective way to resolve this problem . The no - answer and merge approaches were moderately effective , but we note that they do not resolve the scaling problem inherent to the softmax objective we discussed in Section [ reference ] , which might be why they lagged behind . The sigmoid objective function reduces the paragraph - level performance considerably , especially on the TriviaQA datasets .
The main challenge when is very large ( in the order of one million in this paper ) is the fact that computing all inner products between and all embeddings becomes prohibitively slow during training ( even when exploiting matrix - matrix multiplications and modern GPUs ) . Several approaches have been proposed to cope with the scaling issue : importance sampling , Noise Contrastive Estimation ( NCE ) , self normalizing partition functions or Hierarchical Softmax – they all offer good solutions to this problem .
Similarly , even for this tiny problem size , the recent analysis that proves a near optimal sample complexity in fixed horizon problems only guarantees that we will have fewer than suboptimal episodes . While these bounds may be acceptable in worst case scaling , they are not of much practical use . subsection : One - hot features
The character recognition is the application of image processing used to identify the character . This application is area needs a series of image processing operations , which help us in our research work .
The invention provides a multi-point touch-based image operation interaction system , which comprises touch screen equipment , a kernel driving module and an image processing module , wherein the touch screen equipment is used for displaying an image , receiving touch instructions of a plurality of touch points of a user , acquiring physical coordinates of each touch point , generating corresponding codes and transmitting the generated codes at a certain time interval ; the kernel driving module is used for acquiring the codes generated by the touch screen equipment according to the time interval at which the touch screen equipment transmits the codes , performing operational analysis , generating corresponding message codes according to the results of the operational analysis and transmitting the message codes to the image processing module ; and the image processing module is used for performing corresponding image processing operations on a currently displayed image of the touch screen equipment according to the received message codes . The invention also provides a multi-point touch-based image operation interaction method .
Such biometric technology is defined as an automated method of identifying and authenticating a person 's identity based on the user 's physiological and behavioral characteristics . The purpose of this research is to know the accuracy level of iris pattern identification using Hidden Markov Model with Gabor wavelet method , to know the iris feature extraction process as part of image processing process using Gabor wavelet method . The method used for data processing is by using Hidden Markov Model , Gabor Wavelet and 2D Order Statistic Filter .
Navigation device ( 200 , 300 ) , the image recording apparatus for recording image data and ( 300 ) , receives the incident signal indicating the occurrence of incidents , processing resources for performing image data processing operation in response to the incident signal ( 202 ) with a door .
A display device and a method for displaying a broadcasting signal are provided to display a predetermined image unless a viewing rate of a received program corresponds to a user-specific viewable rate , thereby automatically displaying programs and contents appropriate to a viewable rate of a user . A display device comprises a display unit(20 ) , a receiving unit(10 ) for receiving a broadcasting signal including a program having a viewing rate and extracting the view rating of the program , an image processor(60 ) for performing an image process operation on the received program , and a controller(70 ) for comparing the viewing rate of the received program with a user-specific viewable rate . The controller controls the image processor to display a predetermined image unless the viewing rate of the received program corresponds to the user-specific viewable rate .
And , a first camera ( 1 ) , the water meter ( 2 ) , the elasticity measuring stick ( 5 ) , oil measuring stick ( 7 ) and keratin control information input from the measurement stick ( 8) a system for collecting information on the skin a central processing unit ( 9 ) for transmitting to and ; The central processing unit 9 the image signals transmitted to the computer 10 or computer 10 , the image analysis calculation process the image and numeric signals back to the central processor from the device 9 , the interface unit ( 11 to transmit a ) Wow ; And a computer 10 for image processing and operation , and outputs the number signal to analyze a video signal transmitted through the interface unit 11 ; And the LCD display unit 12 , which via the central processing unit 9 the information obtained by the means for collecting the information of the skin to the output value ; It is made of a monitor 13 for outputting the video signal transmitted from said central processing unit ( 9 ) . The present invention as above is the human body ( usually the face ) comprehensive information of the pore distribution of the skin and pores , the size of the skin , pigmentation , keratin and other atopy and accurately measure the moisture and elasticity , oil and dead skin cells in scientific and diagnose it yourself of cosmetics you can prescribe for your skin type and to be able to scientifically yet affordable skin care .
Three base classifiers ( K-Nearest Neighbors ( KNN ) , Support Vector Machine ( SVM ) , and Decision Tree ( DT ) ) and two ensemble methods ( Bootstrap Aggregating ( Bagging ) and Boosting ) were employed and compared . Results showed that : ( 1 ) the simulated Gaussian noise and EMG noise had an impact on accuracy , while simulated spike noise did not , which is of great significance for the future application of driver fatigue detection ; ( 2 ) the influence on noise performance was different based on each classifier , for example , the robust effect of classifier DT was the best and classifier SVM was the weakest ; ( 3 ) the influence on noise performance was also different with each feature set where the robustness of feature set FE and the combined feature set were the best ; and ( 4 ) while the Bagging method could not significantly improve performance against noise addition , the Boosting method may significantly improve performance against superimposed Gaussian and EMG noise . The entropy feature extraction method could not only identify driver fatigue , but also effectively resist noise , which is of great significance in future applications of an EEG-based driver fatigue detection system .
The main technical challenge of this transition lies in the relatively lower x-ray flux obtained from conventional sources , leading to photon-limited data contaminated by Poisson noise . Several issues that must be understood in order to design and optimize DEI imaging systems with respect to noise performance are addressed . Specifically , we : ( a ) develop equations describing the noise properties of DEI images , ( b ) derive the conditions under which the DEI algorithm is statistically optimal , ( c ) characterize the imaging performance that can be obtained as measured by task-based metrics , and ( d ) consider image-processing steps that may be employed to mitigate noise effects .
On this set , the algorithm extracts significant signatures by means of statistical correlations ; the experimental results and the analysis of computational complexity have proved that the algorithm presents the best performance at the highest level of the indexing hierarchy , where the computational complexity is the lowest . Our method has been evaluated by the following methodologies : ( a ) the study of the computational complexity for signature generation ; ( b ) the comparison with analogous methods based on texture analysis by reporting the performance obtained on the same database ( Brodatz ) ; and ( c ) the evaluation of the robustness of the hierarchical indexing in different color spaces , by querying the database with different versions of the original images obtained by noise addition ( gaussian and scanner acquisition noise and lossy compression distortion ) , brightness and contrast enhancement , color and scale adjustment and rotation . Even if our method is designed for texture databases , experiments show satisfactory results also on a real heterogeneous photographic database .
There are practical demands to reuse this abundant data in different acoustic environments . To transform such source data for a target domain , developers can use channel mapping and noise addition . However , channel mapping of the data is difficult without stereo mapping data or impulse response data .
Several standard images were tested and the experimental results were compared with those of other published schemes . The results demonstrate that our proposed scheme can achieve not only better imperceptibility , but also stronger robustness against common signal processing operations and lossy compressions , such as filtering , noise addition , scaling , sharpening , rotation , cropping , and JPEG/JPEG2000 compression .
Watermark detection does not require the original carrier image , that is , the detection is more convenient when digital image is posted and propagated on the Internet , by implementing a blind detection of hidden information . The experiments show that the algorithm has invisibility and strong robustness , effectively resisting noise adding , cropping , filtering , and other common attacks .
First the host audio is divided into several segments which are simultaneously converted to DCT domain , then the DC coefficients which is insensitivity to the human ear hearing and has the biggest hearing margin are selected . Based on the stability of SVD , the watermark is embedded by changing SVD.Experimental results show that the watermarked audio has good imperceptibility and is robust against different kinds of attacks , such as noise adding , low-pass filtering , re-sampling , echo and Mp3 compression . The robustness and the imperceptibility of watermarked audio reach a good balance .
In this paper , we propose a privacy-preserving semi-generative adversarial network ( PPSGAN ) that selectively adds noise to class-independent features of each image to enable the processed image to maintain its original class label . Our experiments on training classifiers with synthetic datasets anonymized with various methods confirm that PPSGAN shows better utility than other conventional methods , including blurring , noise-adding , filtering , and generation using GANs .
Ergonomics 51 , 719 - 736 ] . This exemplary study promises various options for research on noise effects by the use of virtual built environments which are of high plausibility and unlimited variability .
( 3 ) The gray image is embedded to enlarge the watermark volume . ( 4 ) The algorithm can extract the watermark without the help from the original digital audio signal .Experimental results show that the proposed watermarking scheme is inaudible and robust against various signal processing such as re-sampling , re-quantifying , low pass filtering , MP3 compression , and noising adding .
SVD(Singular Value Decomposition ) is used to extract singular value which is embedded copyright protection into digital audio based on modular arithmetic . Experimental results show that the proposed watermarking scheme is inaudible and sensitive to several operations , such as re-sampling , re-quantifying , low pass filtering , noising adding , and lossy compression .
3 . A probabilistic approach to noise-added systems . Part 2 : Application .
In this paper , we propose a novel method , which uses layout extracted parameters of coupling interconnect and pre-characterized parameters of driver to calculate an effective holding resistance . The noise-aware effective holding resistance dramatically improves the accuracy for noise magnitude and energy estimation . The proposed method is simple and efficient .
A self-recovering algorithm is employed , that hides the image digest into some Wavelet subbands to detect possible malevolent object manipulation undergone by the image ( object replacing and/or deletion ) . The Semi-fragility makes the scheme tolerant for JPEG lossy compression as low as quality of 70 % , and locate the tempered area accurately . In addition , the system ensures more security because the embedded watermarks are protected with private keys .
At last the watermarking image is embedded into the region of the DHT coefficient block . The experimental results show that the scheme is easy to be realized and the embedded watermark is effectively robust against various image processing operations such as JPEG lossy compression , noise , cropping , scaling , filtering etc .
Both the newly projected points and the watermark table are used to reconstruct the watermark . In our experiments , we have tested our scheme to see how it works on original images modified by JPEG lossy compression , blurring , cropping , rotating and sharpening , and the experimental results show that our method is very robust and indeed workable .
The algorithm first partitioned an image into many small blocks with appropriate size , employed TSVD to transform and described these blocks on low-dimension representation , and then detected the similarity between these blocks . Experimental results also demonstrate the validity of the proposed approach on several forged images undergone retouching or modifications to conceal traces of forgery , such as lossy JPEG compression , Gaussian blur filter , Gaussian white noise contamination and so on .
One part was transformed by DCT ( discrete cosine transform ) , and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA ; the other part was by DWT ( discrete wavelet transform ) , in which the key fusion was achieved by an ergodic matrix to embed the watermark . Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression , image darken , Gaussian low-pass filtering , contrast decreasing , salt-pepper noise , and geometric attacks such as rotation and cropping .
The introduced redundancy brings simplicity and accuracy for feature calculation . We also describe the applied postprocessing steps with the aim of stabilizing those features under acceptable image manipulations , namely lossy JPEG compression and additive noise corruption . Many experiments and comparative studies are performed to show the effectiveness of our technique in generating content signatures based on invariant image features , as well as to demonstrate its superiority when compared with a redundant wavelet approach
Research was conducted in support of One World Terrain through development of best practices for the delivery of a raster mosaic via cloud hosting service , created using OptimizeRasters Geoprocoessing Toolbox and the Mosaic Dataset Configuration Script . Though ultimately successful in developing the raster mosaic and hosting it online ; JPEG compression lossiness was a key issue with the larger Rose Bowl dataset . Additionally , hosting the imagery via ArcGIS Online was found to increase the compressed file size ; making it comparable to the original file size of the data .
Although the method renews repeatedly assignment of indices , the introduction of a two-dimensional histogram of the indices reduces remarkably the amount of computation required for the repetition . Simulation results show that the application of the method to JPEG lossless coding decreases the coding rates of 8-bit color quantized images by 0.39 - 0.80 bits/pel and that these coding rates are 0.78 - 1.81 bits/pel lower than those of GIF .
In light of poor performance in lossless compression of JPEG , combined with the reality , a new improved idea of the lossless compression patent algorithm which using shuffle is proposed . The correctness of simulation result is verified by experiment .
DPCM ( differential pulse code modulation ) coding is widely used in many applications including lossless JPEG compression . DPCM decoding is inherently a 1-indexed or 2-indexed recurrence relation .
Experimental results show that invisibility and robustness are ensured . Performance in terms of invisibility and robustness of the proposed algorithm is better than that applying on DCT-DWT combined domain for the lossy JPEG compression attacks , speckle and Gaussian noise .
The result is a set of 2D objects , which can be used as input for classical computer vision applications , in particular for object recognition . Our approach makes it possible to apply classical tools of 2D image processing to solve problems of 3D robot mapping , e.g. landmark recognition
In this paper , we present two ASIPs . PXL ASIP , which has a reconfigurable multi bank memory module and an SIMD type computation pipeline , is designed for pixel level image processing , while 2D ASIP , which has slide register module and reconfigurable ALU modules , is designed for 2D image processing . PXL ASIP can perform 4 to 10 times faster compared to its base processor , and 2D ASIP can perform 5 to 43 times faster compared to its base processor .
Context has been successfully applied to 2D image processing in the tasks of object detection , object recognition , etc . However , it is usually ignored in 3D object tracking .
The supervised learning algorithm is then executed to obtain a compact CNN architecture , called as Wave-CNN . The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications .
Existing techniques for texturing curved surfaces are either only applicable for a limited subsetof surface representations ( 3d painting of parametric patches or dense polygonal meshes for example ) , or do not lend themselvesnaturally to interactive texture editing ( e.g. procedural and solid textures ) . Although such methods have been used to produce stunning effects , there is a lack of flexible and general purpose tools , such as those provided by 2d image processing applications . This work argues that interactive texturing could benefit from a more cohesive paradigm built around a kernel of powerful and general operations .
Segmentation is the process of dividing an image into segments that have similar attributes . There are a variety of traditional segmentation technique in the literature , mainly for 2D image processing applications . 3D segmentation is a much more complex problem .
A CMOS fingerprint recognition SoC with embedded column-parallel processors is optimized for 2D digital image processing . The processor employs self-configuration features for adaptive filter operations and the pixel includes a sensing block , ADC and frame memory without area penalty .
Making such adjustments to video footage is harder still . We describe and study a set of 2D image operations , based on multiscale image analysis , that are easy and straightforward and that can consistently modify perceived material properties . These operators first build a subband decomposition of the image and then selectively modify the coefficients within the subbands .
With the ability to perform up to 16 ALU operations and 12 texture accesses in a single pass , Direct3D ’s pixel pipeline has begun to look like a powerful programmable 2D image processing machine . In this chapter , we will outline several techniques which utilize DirectX 8.1 ’s 1.4 pixel shaders to perform 2D image processing effects such as blurs , edge detection , transfer functions and morphology . Along the way , we will point out applications of these 2D image operations to 3D game graphics .
The nonlocal means ( NL-Means ) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods . Although NL-Means is very popular in the field of 2D image signal processing , it has not received enough attention in the field of 1D signal processing . This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum .
Realizations using analog circuits for the two-dimensional ( 2-D ) image processing operations , such as correlation and mathematical morphology , are presented . These analog realizations can operate directly on raster scanned images in real-time .
However , an important class of global operations , namely those that are reversible-that can be computed in forward or reverse order on a data structure-can be computed in parallel using a restricted form of divide and conquer called split and merge . These reversible operations include the global operations mentioned above , and many more besides-even such non-image processing operations as parsing , string search , and sorting . The split and merge method will be illustrated by application of it to these algorithms .
MLP Design Method Optimized for Hidden Neurons on FPGA Increasing Security by Implementing Image Encryption using AES Algorithm Research on vehicle-mounted pavement crack detection system based on image processing Surface model generation and segmentation of the human cerebral cortex for the construction of unfolded cortical maps Edge and Line Feature Extraction Based on Covariance Models Hardware-software abandoned object detection vision system in heterogeneous zynq device Study of the dependency of spectral shadow indices on the land cover/use and shadow strength in color aerial imagery CONTRAST ENHANCEMENT FOR PCA FUSION OF MEDICAL IMAGES A combined effect of local and global method for contrast image enhancement Morphological Background Detection and Enhancement of Images With Poor Lighting A Review on Various contrast enhancement scheme for Dark Images An Adaptive Contrast Enhancement Method for Palm Vein Image Research and Evaluation of Local Dimming Algorithm for LED Backlight LCD Display A New Approach for Contrast Enhancement Using Sigmoid Function Implementation of modified polar complex moments-based fingerprint orientation estimation for effective segmentation Optimization of a contrast enhanced micro-CT in a hybrid fluorescence / x-ray tomography system for small animal imaging Properties of shunting inhibitory cellular neural networks for colour image enhancement Optical implementation of partially negative filters using a spectrally tunable light source, and its application to contrast enhanced oral and dental imaging. A general formulation of analogue computer programming and scaling Exploiting a fast and simple ECC for scaling supply voltage in level-1 caches OpenBox: A Software-Defined Framework for Developing, Deploying, and Managing Network Functions Comparison between metallic carbon nanotube and copper future VLSI nano-interconnects Autonomic scaling of Cloud Computing resources using BN-based prediction models Software for scaling and analysis of vertical incidence ionograms-ionoScaler Primary Data Deduplication - Large Scale Study and System Design How do we efficiently generate high-resolution hydraulic models at large numbers of riverine reaches? Hadamard Transforms of Magnified, Expanded, and Periodic Signals Offline and Online Scheduling of Jobs with Service and Delay Costs Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Natural Language Inference over Interaction Space Meta-Transfer Learning for Few-Shot Learning Simple and Effective Multi-Paragraph Reading Comprehension Exploring the Limits of Language Modeling Deep Exploration via Bootstrapped DQN Review Paper on Feature Vector Based Distance Analysis Approach for Braille Character Identification Multi-point touch-based image operation interaction system and multi-point touch-based image operation interaction method Biometric Patterns Of Eye Iris By Using Hidden Markov Model Method of recording navigation device and an image data Display apparatus and broadcast signal display method thereof Skin diagnosis system Noise Robustness Analysis of Performance for EEG-Based Driver Fatigue Detection Using Different Entropy Feature Sets Noise properties and task-based evaluation of diffraction-enhanced imaging Robust Hierarchical Indexing based on Texture Features Channel-mapping for speech corpus recycling A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique Adaptive Watermarking Algorithm for Digital Image Based on Discrete Cosine Transform Audio Watermarking Algorithm Based on SVD in DCT Domain Selective Feature Anonymization for Privacy-Preserving Image Data Publishing Audio-video virtual reality environments in building acoustics: An exemplary study reproducing performance results and subjective ratings of a laboratory listening experiment. New Large-Capacity Digital Audio Watermarking Algorithm Digital Audio Fragile Watermarking Algorithm Based on SVD Stochastic Resonance: Theory and Applications Noise-aware driver modeling for nanometer technology A Secure Semi-Fragile Watermarking Scheme for Authentication and Recovery of Images based on Wavelet Transform Watermarking Scheme Based on DCT and DHT A Watermarking Scheme Based on Principal Component Analysis Technique Copy-region forgery detection based on TSVD in digital image forensics Adaptive Robust Blind Watermarking Scheme Improved by Entropy-Based SVM and Optimized Quantum Genetic Algorithm Contourlet Domain Feature Extraction for Image Content Authentication Forward Support—Cloud Based Mosaic Imagery for Emergency Operations Assignment of Indices Suitable for the Predictive Coding of a Color Quantized Image. Improved Lossless Compression Patent Method for JPEG Parallel Implementation of DPCM Decoding for SMP Systems An improved image watermarking scheme using selective curvelet scales Using Extended EM to Segment Planar Structures in 3D Flexible and High Performance ASIPs for Pixel Level Image Processing and Two Dimensional Image Processing Stereo Context for Robust Object Tracking Wavelet-cellular neural network architecture and learning algorithm A framework for interactive texturing on curved surfaces A Segmentation Methodology for Real 3D Images A 200/spl times/160 pixel CMOS fingerprint recognition SoC with adaptable column-parallel processors Band-Sifting Decomposition for Image-Based Material Editing Image Processing with 1.4 Pixel Shaders in Direct3D Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum Realizations of 2-D operators using analog circuitry Global image processing operations on parallel architectures
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DASIP CCBR IOLTS SIGCOMM CLOUDNET USENIX Annual Technical Conference Allerton NIPS ICLR 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ACL ArXiv NIPS ICASSP ISQED MMSP SERA ICPR (3) ICIMCS SIGGRAPH NMBIA
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