In particular , the codebooks required for the frequencies above 1 kHz are very small . Also , by subsampling , the large low frequency ( 0 - 1 kHz ) codebook can be searched very rapidly resulting in a significant complexity advantage . The proposed CT-DSM mitigates the effects of subsampling by modifying the feedback path of the modulator to accommodate the RF replica of the feedback signal and by enhancing the quality factor of the loop filter . The novel modulator has significant improvements over previously published subsampling modulators as it provides jitter and image suppression and excess loop delay compensation to improve the dynamic range , thus enabling the modulator to be utilized in subsampling receivers when a relatively low sampling rate is desired . It can be used to detect the most important instances and thus help ensemble classifiers to avoid the negative effects of the class noise and class imbalance . To obtain accurate classification results , this paper proposes the Ensemble-Margin Based Random Forests ( EMRFs ) method , which combines RFs and a new subsampling iterative technique making use of computed ensemble margin values . As for comparative analysis , the learning techniques considered are : SVM , AdaBoost , RFs and the Subsample based Random Forests ( SubRFs ) . Flexibility is further increased by cascadability and/or the possibility of parallel operation . Hardware overhead for particular algorithmic requirements , such as variable pixel resolution , subsampling with offset , and subpixel accuracy , is discussed in detail . A full-custom ( CMOS ) implementation for the architecture is described . We show that using a linear model to remove the effect of age on the brain signals ( “ deconfounding ” ) leads to pessimistic scores , as previously reported . Anti mutual-information subsampling does not require to remove from the brain signals the shared variance between aging and fluid intelligence , and hence does not display this pessimistic behavior . In addition , it is non-parametric and hence robust to violations of the linear hypothesis . In this thesis , we address this problem by applying parallel processing techniques . A subsampling stochastic gradient descent algorithm has been proposed to accelerate the training of recurrent neural network language models . The paper proposes three modifications to the 3-step hierarchical search block-matching algorithm for video coding : a multiple-winner search that improves the estimation accuracy , a method of subsampling that reduces computation and input data amount , and an overlapping strategy that improves the accuracy of large-area search . Experimental results show that combining these techniques provides high-speed and high-precision motion estimators with reduced on-chip buffers and lowered input bandwidth requirements.<<ETX > > Yet , these dependencies pose statistical challenges for analyzing such data , especially when the data set comprises only a single observation of one network , often leading to intractable likelihoods regardless of the modeling paradigm and limiting the application of existing statistical methods for networks . We explore a subsampling bootstrap procedure to serve as the basis for goodness of fit and model selection with a single observed network that circumvents the intractability of such likelihoods . Our approach is based on flexible resampling distributions formed from the single observed network , allowing for more nuanced and higher dimensional comparisons than point estimates of quantities of interest . Various spectral and spatial coding strategies for structured illumination are explored . Subsampling in CSXI is accomplished by either view angle spectral subsampling , spatial subsampling enabled by block-unblock coded apertures placed at the source or detector side , or both . The careful design of subsampling strategies , spectral filters , coded apertures , and their placement , are shown to be critical for the quality of tomographic image reconstruction . The input image is first divided into its three color opponent components with each axis further decomposed into a set of band pass spatial frequency filters with different center frequencies and orientations . The spatial frequency decomposition is accomplished by an efficient encoding algorithm incorporating a hierarchical cascading Gaussian pyramid algorithm which is an alternating sequence of image output passing through Nyquist low pass spatial filter and subsampling local operators for image encoding . This paper examines the limitations of earlier target acquisition models and describes a computational model which starts with actual stimulus images as an input . Both learning and inference are performed whole - image - at - a - time by dense feedforward computation and backpropagation . In - network upsampling layers enable pixelwise prediction and learning in nets with subsampling . This method is efficient , both asymptotically and absolutely , and precludes the need for the complications in other works . Convolutional neural networks ( CNNs ) were initially proposed by LeCun for the recognition of hand - written digits . A CNN consists of two layers : a convolutional layer , followed by a subsampling layer . Inspired by biological processes and exploiting the fact that nearby pixels are strongly correlated , CNNs are relatively insensitive to small translations or rotations of the image input . subsection : Results of Hierarchical Subsampling We first demonstrate the results of the hierarchical subsampling recurrent network , which is the key to speed up our experiments . We set the size of the subsampling window to be 2 , therefore each subsampling layer reduced the time resolution by a factor of 2 . In this paper , we present the segmental RNN — a novel acoustic model that combines the segmental CRF with an encoder RNN for end - to - end speech recognition . We discuss the practical training and decoding algorithms of this model for speech recognition , and the subsampling network to reduce the computational cost . Our experiments were performed on the TIMIT dataset , and we achieved strong recognition accuracy using zeroth - order CRF , and without using any language model . It can be described as a 3 - dimensional array of size where and are the width and height and is the number of channels ( in case is the output of a convolutional layer , is the number of filters in this layer ) . Then p - norm subsampling ( or pooling ) with pooling size ( or half - length ) and stride applied to the feature map is a 3 - dimensional array with the following entries : where is the function mapping from positions in to positions in respecting the stride , is the order of the p - norm ( for , it becomes the commonly used max pooling ) . If , pooling regions do not overlap ; however , current CNN architectures typically include overlapping pooling with and . The global SR problem assumes LR data to be a low - pass filtered ( blurred ) , downsampled and noisy version of HR data . It is a highly ill - posed problem , due to the loss of high - frequency information that occurs during the non - invertible low - pass filtering and subsampling operations . Furthermore , the SR operation is effectively a one - to - many mapping from LR to HR space which can have multiple solutions , of which determining the correct solution is non - trivial . Can dedicated modules designed specifically for dense prediction improve accuracy further ? Modern image classification networks integrate multi - scale contextual information via successive pooling and subsampling layers that reduce resolution until a global prediction is obtained . In contrast , dense prediction calls for multi - scale contextual reasoning in combination with full - resolution output . Collaborative denoising autoencoder ( cdae ) [ reference ] : augments the standard denoising autoencoder by adding a per - user latent factor to the input . We change the ( user , item ) entry subsampling strategy in SGD training in the original paper to the user - level subsampling as we did with Mult - vae pr and Mult - dae . We generally find that this leads to more stable convergence and better performance . After converting the network to a fully convolutional one , the first fully connected layer has 4 , 096 filters of large spatial size and becomes the computational bottleneck in our dense score map computation . We have addressed this practical problem by spatially subsampling ( by simple decimation ) the first FC layer to ( or ) spatial size . This has reduced the receptive field of the network down to ( with zero - padding ) or ( in convolutional mode ) and has reduced computation time for the first FC layer by times . A delta-sigma , or sigma-delta , analog-to-digital converter ( ADC ) comprises both a modulator , which implements oversampling and noise shaping , and a decimator , which implements low-pass filtering and downsampling . Whereas these ADCs are ubiquitous in audio applications , their usage in video applications is emerging . In this work , a new , simple infra-frame EC technique based on downsampling and side-information aided upsampling is developed . Through a study of a family of downsampling schemes , an optimal one is found and analyzed for EC . This downsampling scheme gives birth to the new EC technique . In this work , we introduce a novel personalized emotion prediction model validated in different noisy environments . It is performed by a three-level noise reduction algorithm : ( i ) data downsampling , ( ii ) feature synchronization , and ( iii ) a modified version of graph total variation . The approach has been validated on the broadly used RECOLA database with different types of noises , including convolutive and additive noise with different SNRs . Approximation and details are not any more orthogonal as needed in the reconstruct of the MS high-resolution image based on DWT . For that , we propose in this paper a new fusion schema ( coupled multiresolution decomposition model ( CMD ) ) allowing the reconstruction of a high-resolution MS given its approximation and details obtained by MTF-tailored downsampling and wavelet decomposition , respectively . For validation , CMD is applied to Pleiades , GeoEye-1 , and SPOT 6 images . Through applying image descriptors to the odd-even image tree , we get the relative multi-resolution image descriptors . Multi-resolution analysis is based on downsampling expansion with maximum energy extraction followed by upsampling reconstruction . Since the maximum energy usually retained in the lowest frequency coefficients ; we do maximum energy extraction through keeping the lowest P coefficients from each resolution level . On the one hand , we propose an effective end-to-end model , called FedNet , to predict traffic flow of each region in a city . First , for the temporal trend , period , closeness properties , we obtain low-dimensional features by downsampling high-dimensional input features . Then we perform temporal fusion to get temporal aggregations of different spatial levels . The invention discloses a method for generating and playing a network video , which comprises the following steps : when a streaming media server receives a video command , storing an original code stream file from a front end encoder by the streaming media server ; decoding the original code stream file stored by the streaming media server by a transcription server to obtain an original image sequence , performing N times downsampling on the original image sequence to obtain a subsequence of a corresponding frame rate , and encoding the subsequence to obtain a corresponding code stream file ; and when a client requests multiple-speed playing of the network video , sending a code stream file corresponding to the requested multiple-speed to the client at a normal speed by the streaming media server . The invention also discloses a system for generating and playing the network video . We refer to the network as Pulmonary Lobe Segmentation Network ( PLS-Net ) , which is designed to efficiently exploit 3D spatial and contextual information from high-resolution volumetric CT images for effective volume-to-volume learning and inference . The PLS-Net is based on an asymmetric encoder-decoder architecture with three novel components : ( i ) 3D depthwise separable convolutions to improve the network efficiency by factorising each regular 3D convolution into two simpler operations ; ( ii ) dilated residual dense blocks to efficiently expand the receptive field of the network and aggregate multi-scale contextual information for segmentation ; and ( iii ) input reinforcement at each downsampled resolution to compensate for the loss of spatial information due to convolutional and downsampling operations . We evaluated the proposed PLS-Net on a multi-institutional dataset that consists of 210 CT images acquired from patients with a wide range of lung abnormalities . An appropriate image quality assessment scheme is a useful tool for evaluating image processing algorithms , especially algorithms designed to leave the image visually unchanged . In this contribution , we present and conduct an investigation aimed at locating a robust threshold for downsampling BTF images without loosing perceptual quality . To this end , an experimental study on how decreasing the texture resolution influences perceived quality of the rendered images has been presented and discussed . We studied a variety of similarity measurements with different pre-processing techniques to find which registration similarity metrics were best suited for HFUS vessel tracking . The overall best performance was obtained with a normalized correlation metric coupled with HFUS downsampling and a one-plus-one evolutionary optimizer , yielding a mean registration error of 0.05 mm . We also used HFUS to study how finger tissue deforms under an ultrasound transducer , comparing internal motion vs. transducer motion . In this work , we examine two such systems which utilize subsampled representations of the sequence and residual data . In the first system , we consider a method for reorganizing , downsampling and interpolating the residual data . In the second system , we propose a new method that employs lower resolution intensity values for spatial and motion-compensated prediction . First , the images of cells on blood smears were collected with a microscope ( MshOt model ) , and using a threshold segmentation algorithm to divide it to 80 × 80 high-resolution ( HR ) reference image for training . After segmentation , the HR cell image is downsampled by the bicubic downsampling algorithm to obtain a 20 × 20 low resolution ( LR ) cell image . Training the CSRNet reconstruction network with a training set consisting of HR and LR cell images . Third , obtain the final matting result . For characteristics of the system , Grabcut algorithm is optimized with downsampling technology to accelerate the segmentation speed and concurrently accelerates trimap generating algorithm and Shared Matting to further improve the processing speed . It is proved by experiment that the system has a strong applicability and can produce an ideal matting effect with less interaction in a short time . The classic downsampling methods , such as farthest point sampling and Poisson disk sampling , thoughwidely used for decades , are independent of the subsequent applications , since the downsampled point cloud may compromisetheir performance severely . This paper explores the problem oftask-oriented downsampling , which aims to downsample a pointcloud and maintain the performance of subsequent applications asmuch as possible . We propose MOPS-Net , a novel end-to-end deepneural network which is designed from the perspective of matrixoptimization , making it fundamentally different from the existing deep learning-based methods . The techniques we propose are located in the stages of multi-resolution generation , transfer function design and the GPU ray casting algorithm itself . More precisely , the contributions achieved during this thesis are : * A feature-preserving filter for the downsampling of scalar volume data . * A downsampling filter and an efficient storage scheme for gradient volume data . For instance , it achieves 99.45 % attack success rate on the IEMOCAP dataset against the ResNet18 model , while the generated adversarial audios are also misinterpreted by multiple popular ASR platforms , including Google Cloud Speech , Microsoft Bing Voice , and IBM Speech-to-Text . We further evaluate three potential defense methods to mitigate such attacks , including adversarial training , audio downsampling , and moving average filtering , which leads to promising directions for further research . In an MPEG ( Motion Pictures Experts Group ) video coding system , a color component downsampling from 1050 2:1 60 Hz to 525 1:1 30 Hz line scan formats must be carried out on the U and V color component source signals . One scheme presently used to accomplish the required U and V downsampling , called frame-based color subsampling , is thoroughly analyzed . By downsampling block bodies by $ M$ times , only about $ 1/M$ of the block bodies with high information entropy are downloaded and stored . Performance analysis demonstrates that the proposed downsampling nodes and full nodes have similar broadcast accuracy with appropriate $ M$. The simulation results show that the proposed algorithm can provide a better cost-effective choice for nodes between broadcast accuracy and storage . The proposed filter design utilizes time multiplexing of sampled data to capacitors , and it is useful in implementing a dual-band ( low-pass and high-pass ) delta-sigma converter , or a single-band band-pass delta-sigma converter . Either of these two configurations can be selected in real time by changing clock signals and output downsampling filters . The basic reconfigurable triple-band filter can be applied directly to many higher-order delta-sigma architectures . The contracting path follows the typical architecture of a convolutional network . It consists of the repeated application of two 3x3 convolutions ( unpadded convolutions ) , each followed by a rectified linear unit ( ReLU ) and a 2x2 max pooling operation with stride 2 for downsampling . At each downsampling step we double the number of feature channels . Many methods assume multiple images are available as LR instances of the same scene with different perspectives , i.e. with unique prior affine transformations . These can be categorised as multi - image SR methods and exploit explicit redundancy by constraining the ill - posed problem with additional information and attempting to invert the downsampling process . However , these methods usually require computationally complex image registration and fusion stages , the accuracy of which directly impacts the quality of the result . The task of SISR is to estimate a HR image given a LR image downscaled from the corresponding original HR image . The downsampling operation is deterministic and known : to produce from , we first convolve using a Gaussian filter - thus simulating the camera ’s point spread function - then downsample the image by a factor of . We will refer to as the upscaling ratio . With the Gaussian bumps encoded in y cij , the ( 1 − y cij ) term reduces the penalty around the ground truth locations . Many networks [ reference ] ) involve downsampling layers to gather global information and to reduce memory usage . When they are applied to an image fully convolutionally , the size of the output is usually smaller than the image . The ImageNet images are less blurry , which means neighboring pixels are less correlated to each other and thus less predictable . Because the downsampling method can influence the compression performance , we have made the used downsampled images available . Figure [ reference ] ( right ) shows samples drawn from our model trained on ImageNet . We demonstrate an application of super - resolving historical photographs with JPEG compression artifacts . In these cases , neither the ground - truth images nor the downsampling kernels are available . As shown in Figure [ reference ] , our method can reconstruct sharper and more accurate images than the state - of - the - art approaches . Because the feature map dimension increases linearly in the case of additive PyramidNets , the feature map dimensions of the input - side layers tend to be larger , and those of the output - side layers tend to be smaller , compared with multiplicative PyramidNets as illustrated in Figure [ reference ] . Previous works typically set multiplicative scaling of feature map dimension for downsampling modules , which is implemented to give a larger degree of freedom to the classification part by increasing the feature map dimension of the output - side layers . However , for our PyramidNet , the results in Figure [ reference ] implies that increasing the model capacity of the input - side layers would lead to a better performance improvement than using a conventional way of multiplicative scaling of feature map dimension . One approach involves repeated up - convolutions that aim to recover lost resolution while carrying over the global perspective from downsampled layers . This leaves open the question of whether severe intermediate downsampling was truly necessary . Another approach involves providing multiple rescaled versions of the image as input to the network and combining the predictions obtained for these multiple inputs . The feature maps are obtained from the backbone network in CornerNet - Saccade , which is an hourglass network . Each hourglass module in the network applies several convolution and downsampling layers to downsize the input feature maps . It then upsamples the feature maps back to the original input resolution by multiple convolution and upsampling layers . We do not explore the third idea in SqueezeNet . Since the hourglass network has a symmetrical structure , delayed downsampling results in higher resolution feature maps during the upsampling . Performing convolution on high resolution feature maps is computationally expensive , which would prevent us from achieving real - time detection . subsection : Spatial Sampling Let be the downsampling function that decimates an image to the corresponding image of size . Let be the reverse operation that upsamples images . While this invariance is clearly desirable for high - level vision tasks , it can hamper low - level tasks , such as pose estimation chen2014articulated , tompson2014joint and semantic segmentation - where we want precise localization , rather than abstraction of spatial details . There are two technical hurdles in the application of DCNNs to image labeling tasks : signal downsampling , and spatial ‘ In this case , with SNIPER , we generate million chips of size x using scales of ( / ms , 1 ) . Note that SNIPER also performs adaptive downsampling . Since the scales are smaller , chips would cover more background , due to which the impact of negative sampling is diminished . The second one is the volumetric residual block extended from the 2D residual block of option B in [ reference ] . The third one is the volumetric downsampling block that is identical to a volumetric max pooling layer . The last one is the volumetric upsampling block , which consists of a volumetric deconvolution layer , volumetric batch normalization layer , and the activation function ( i.e. , ReLU ) .
In particular , the codebooks required for the frequencies above 1 kHz are very small . Also , by subsampling , the large low frequency ( 0 - 1 kHz ) codebook can be searched very rapidly resulting in a significant complexity advantage .
The proposed CT-DSM mitigates the effects of subsampling by modifying the feedback path of the modulator to accommodate the RF replica of the feedback signal and by enhancing the quality factor of the loop filter . The novel modulator has significant improvements over previously published subsampling modulators as it provides jitter and image suppression and excess loop delay compensation to improve the dynamic range , thus enabling the modulator to be utilized in subsampling receivers when a relatively low sampling rate is desired .
It can be used to detect the most important instances and thus help ensemble classifiers to avoid the negative effects of the class noise and class imbalance . To obtain accurate classification results , this paper proposes the Ensemble-Margin Based Random Forests ( EMRFs ) method , which combines RFs and a new subsampling iterative technique making use of computed ensemble margin values . As for comparative analysis , the learning techniques considered are : SVM , AdaBoost , RFs and the Subsample based Random Forests ( SubRFs ) .
Flexibility is further increased by cascadability and/or the possibility of parallel operation . Hardware overhead for particular algorithmic requirements , such as variable pixel resolution , subsampling with offset , and subpixel accuracy , is discussed in detail . A full-custom ( CMOS ) implementation for the architecture is described .
We show that using a linear model to remove the effect of age on the brain signals ( “ deconfounding ” ) leads to pessimistic scores , as previously reported . Anti mutual-information subsampling does not require to remove from the brain signals the shared variance between aging and fluid intelligence , and hence does not display this pessimistic behavior . In addition , it is non-parametric and hence robust to violations of the linear hypothesis .
In this thesis , we address this problem by applying parallel processing techniques . A subsampling stochastic gradient descent algorithm has been proposed to accelerate the training of recurrent neural network language models .
The paper proposes three modifications to the 3-step hierarchical search block-matching algorithm for video coding : a multiple-winner search that improves the estimation accuracy , a method of subsampling that reduces computation and input data amount , and an overlapping strategy that improves the accuracy of large-area search . Experimental results show that combining these techniques provides high-speed and high-precision motion estimators with reduced on-chip buffers and lowered input bandwidth requirements.<<ETX > >
Yet , these dependencies pose statistical challenges for analyzing such data , especially when the data set comprises only a single observation of one network , often leading to intractable likelihoods regardless of the modeling paradigm and limiting the application of existing statistical methods for networks . We explore a subsampling bootstrap procedure to serve as the basis for goodness of fit and model selection with a single observed network that circumvents the intractability of such likelihoods . Our approach is based on flexible resampling distributions formed from the single observed network , allowing for more nuanced and higher dimensional comparisons than point estimates of quantities of interest .
Various spectral and spatial coding strategies for structured illumination are explored . Subsampling in CSXI is accomplished by either view angle spectral subsampling , spatial subsampling enabled by block-unblock coded apertures placed at the source or detector side , or both . The careful design of subsampling strategies , spectral filters , coded apertures , and their placement , are shown to be critical for the quality of tomographic image reconstruction .
The input image is first divided into its three color opponent components with each axis further decomposed into a set of band pass spatial frequency filters with different center frequencies and orientations . The spatial frequency decomposition is accomplished by an efficient encoding algorithm incorporating a hierarchical cascading Gaussian pyramid algorithm which is an alternating sequence of image output passing through Nyquist low pass spatial filter and subsampling local operators for image encoding . This paper examines the limitations of earlier target acquisition models and describes a computational model which starts with actual stimulus images as an input .
Both learning and inference are performed whole - image - at - a - time by dense feedforward computation and backpropagation . In - network upsampling layers enable pixelwise prediction and learning in nets with subsampling . This method is efficient , both asymptotically and absolutely , and precludes the need for the complications in other works .
Convolutional neural networks ( CNNs ) were initially proposed by LeCun for the recognition of hand - written digits . A CNN consists of two layers : a convolutional layer , followed by a subsampling layer . Inspired by biological processes and exploiting the fact that nearby pixels are strongly correlated , CNNs are relatively insensitive to small translations or rotations of the image input .
subsection : Results of Hierarchical Subsampling We first demonstrate the results of the hierarchical subsampling recurrent network , which is the key to speed up our experiments . We set the size of the subsampling window to be 2 , therefore each subsampling layer reduced the time resolution by a factor of 2 .
In this paper , we present the segmental RNN — a novel acoustic model that combines the segmental CRF with an encoder RNN for end - to - end speech recognition . We discuss the practical training and decoding algorithms of this model for speech recognition , and the subsampling network to reduce the computational cost . Our experiments were performed on the TIMIT dataset , and we achieved strong recognition accuracy using zeroth - order CRF , and without using any language model .
It can be described as a 3 - dimensional array of size where and are the width and height and is the number of channels ( in case is the output of a convolutional layer , is the number of filters in this layer ) . Then p - norm subsampling ( or pooling ) with pooling size ( or half - length ) and stride applied to the feature map is a 3 - dimensional array with the following entries : where is the function mapping from positions in to positions in respecting the stride , is the order of the p - norm ( for , it becomes the commonly used max pooling ) . If , pooling regions do not overlap ; however , current CNN architectures typically include overlapping pooling with and .
The global SR problem assumes LR data to be a low - pass filtered ( blurred ) , downsampled and noisy version of HR data . It is a highly ill - posed problem , due to the loss of high - frequency information that occurs during the non - invertible low - pass filtering and subsampling operations . Furthermore , the SR operation is effectively a one - to - many mapping from LR to HR space which can have multiple solutions , of which determining the correct solution is non - trivial .
Can dedicated modules designed specifically for dense prediction improve accuracy further ? Modern image classification networks integrate multi - scale contextual information via successive pooling and subsampling layers that reduce resolution until a global prediction is obtained . In contrast , dense prediction calls for multi - scale contextual reasoning in combination with full - resolution output .
Collaborative denoising autoencoder ( cdae ) [ reference ] : augments the standard denoising autoencoder by adding a per - user latent factor to the input . We change the ( user , item ) entry subsampling strategy in SGD training in the original paper to the user - level subsampling as we did with Mult - vae pr and Mult - dae . We generally find that this leads to more stable convergence and better performance .
After converting the network to a fully convolutional one , the first fully connected layer has 4 , 096 filters of large spatial size and becomes the computational bottleneck in our dense score map computation . We have addressed this practical problem by spatially subsampling ( by simple decimation ) the first FC layer to ( or ) spatial size . This has reduced the receptive field of the network down to ( with zero - padding ) or ( in convolutional mode ) and has reduced computation time for the first FC layer by times .
A delta-sigma , or sigma-delta , analog-to-digital converter ( ADC ) comprises both a modulator , which implements oversampling and noise shaping , and a decimator , which implements low-pass filtering and downsampling . Whereas these ADCs are ubiquitous in audio applications , their usage in video applications is emerging .
In this work , a new , simple infra-frame EC technique based on downsampling and side-information aided upsampling is developed . Through a study of a family of downsampling schemes , an optimal one is found and analyzed for EC . This downsampling scheme gives birth to the new EC technique .
In this work , we introduce a novel personalized emotion prediction model validated in different noisy environments . It is performed by a three-level noise reduction algorithm : ( i ) data downsampling , ( ii ) feature synchronization , and ( iii ) a modified version of graph total variation . The approach has been validated on the broadly used RECOLA database with different types of noises , including convolutive and additive noise with different SNRs .
Approximation and details are not any more orthogonal as needed in the reconstruct of the MS high-resolution image based on DWT . For that , we propose in this paper a new fusion schema ( coupled multiresolution decomposition model ( CMD ) ) allowing the reconstruction of a high-resolution MS given its approximation and details obtained by MTF-tailored downsampling and wavelet decomposition , respectively . For validation , CMD is applied to Pleiades , GeoEye-1 , and SPOT 6 images .
Through applying image descriptors to the odd-even image tree , we get the relative multi-resolution image descriptors . Multi-resolution analysis is based on downsampling expansion with maximum energy extraction followed by upsampling reconstruction . Since the maximum energy usually retained in the lowest frequency coefficients ; we do maximum energy extraction through keeping the lowest P coefficients from each resolution level .
On the one hand , we propose an effective end-to-end model , called FedNet , to predict traffic flow of each region in a city . First , for the temporal trend , period , closeness properties , we obtain low-dimensional features by downsampling high-dimensional input features . Then we perform temporal fusion to get temporal aggregations of different spatial levels .
The invention discloses a method for generating and playing a network video , which comprises the following steps : when a streaming media server receives a video command , storing an original code stream file from a front end encoder by the streaming media server ; decoding the original code stream file stored by the streaming media server by a transcription server to obtain an original image sequence , performing N times downsampling on the original image sequence to obtain a subsequence of a corresponding frame rate , and encoding the subsequence to obtain a corresponding code stream file ; and when a client requests multiple-speed playing of the network video , sending a code stream file corresponding to the requested multiple-speed to the client at a normal speed by the streaming media server . The invention also discloses a system for generating and playing the network video .
We refer to the network as Pulmonary Lobe Segmentation Network ( PLS-Net ) , which is designed to efficiently exploit 3D spatial and contextual information from high-resolution volumetric CT images for effective volume-to-volume learning and inference . The PLS-Net is based on an asymmetric encoder-decoder architecture with three novel components : ( i ) 3D depthwise separable convolutions to improve the network efficiency by factorising each regular 3D convolution into two simpler operations ; ( ii ) dilated residual dense blocks to efficiently expand the receptive field of the network and aggregate multi-scale contextual information for segmentation ; and ( iii ) input reinforcement at each downsampled resolution to compensate for the loss of spatial information due to convolutional and downsampling operations . We evaluated the proposed PLS-Net on a multi-institutional dataset that consists of 210 CT images acquired from patients with a wide range of lung abnormalities .
An appropriate image quality assessment scheme is a useful tool for evaluating image processing algorithms , especially algorithms designed to leave the image visually unchanged . In this contribution , we present and conduct an investigation aimed at locating a robust threshold for downsampling BTF images without loosing perceptual quality . To this end , an experimental study on how decreasing the texture resolution influences perceived quality of the rendered images has been presented and discussed .
We studied a variety of similarity measurements with different pre-processing techniques to find which registration similarity metrics were best suited for HFUS vessel tracking . The overall best performance was obtained with a normalized correlation metric coupled with HFUS downsampling and a one-plus-one evolutionary optimizer , yielding a mean registration error of 0.05 mm . We also used HFUS to study how finger tissue deforms under an ultrasound transducer , comparing internal motion vs. transducer motion .
In this work , we examine two such systems which utilize subsampled representations of the sequence and residual data . In the first system , we consider a method for reorganizing , downsampling and interpolating the residual data . In the second system , we propose a new method that employs lower resolution intensity values for spatial and motion-compensated prediction .
First , the images of cells on blood smears were collected with a microscope ( MshOt model ) , and using a threshold segmentation algorithm to divide it to 80 × 80 high-resolution ( HR ) reference image for training . After segmentation , the HR cell image is downsampled by the bicubic downsampling algorithm to obtain a 20 × 20 low resolution ( LR ) cell image . Training the CSRNet reconstruction network with a training set consisting of HR and LR cell images .
Third , obtain the final matting result . For characteristics of the system , Grabcut algorithm is optimized with downsampling technology to accelerate the segmentation speed and concurrently accelerates trimap generating algorithm and Shared Matting to further improve the processing speed . It is proved by experiment that the system has a strong applicability and can produce an ideal matting effect with less interaction in a short time .
The classic downsampling methods , such as farthest point sampling and Poisson disk sampling , thoughwidely used for decades , are independent of the subsequent applications , since the downsampled point cloud may compromisetheir performance severely . This paper explores the problem oftask-oriented downsampling , which aims to downsample a pointcloud and maintain the performance of subsequent applications asmuch as possible . We propose MOPS-Net , a novel end-to-end deepneural network which is designed from the perspective of matrixoptimization , making it fundamentally different from the existing deep learning-based methods .
The techniques we propose are located in the stages of multi-resolution generation , transfer function design and the GPU ray casting algorithm itself . More precisely , the contributions achieved during this thesis are : * A feature-preserving filter for the downsampling of scalar volume data . * A downsampling filter and an efficient storage scheme for gradient volume data .
For instance , it achieves 99.45 % attack success rate on the IEMOCAP dataset against the ResNet18 model , while the generated adversarial audios are also misinterpreted by multiple popular ASR platforms , including Google Cloud Speech , Microsoft Bing Voice , and IBM Speech-to-Text . We further evaluate three potential defense methods to mitigate such attacks , including adversarial training , audio downsampling , and moving average filtering , which leads to promising directions for further research .
In an MPEG ( Motion Pictures Experts Group ) video coding system , a color component downsampling from 1050 2:1 60 Hz to 525 1:1 30 Hz line scan formats must be carried out on the U and V color component source signals . One scheme presently used to accomplish the required U and V downsampling , called frame-based color subsampling , is thoroughly analyzed .
By downsampling block bodies by $ M$ times , only about $ 1/M$ of the block bodies with high information entropy are downloaded and stored . Performance analysis demonstrates that the proposed downsampling nodes and full nodes have similar broadcast accuracy with appropriate $ M$. The simulation results show that the proposed algorithm can provide a better cost-effective choice for nodes between broadcast accuracy and storage .
The proposed filter design utilizes time multiplexing of sampled data to capacitors , and it is useful in implementing a dual-band ( low-pass and high-pass ) delta-sigma converter , or a single-band band-pass delta-sigma converter . Either of these two configurations can be selected in real time by changing clock signals and output downsampling filters . The basic reconfigurable triple-band filter can be applied directly to many higher-order delta-sigma architectures .
The contracting path follows the typical architecture of a convolutional network . It consists of the repeated application of two 3x3 convolutions ( unpadded convolutions ) , each followed by a rectified linear unit ( ReLU ) and a 2x2 max pooling operation with stride 2 for downsampling . At each downsampling step we double the number of feature channels .
Many methods assume multiple images are available as LR instances of the same scene with different perspectives , i.e. with unique prior affine transformations . These can be categorised as multi - image SR methods and exploit explicit redundancy by constraining the ill - posed problem with additional information and attempting to invert the downsampling process . However , these methods usually require computationally complex image registration and fusion stages , the accuracy of which directly impacts the quality of the result .
The task of SISR is to estimate a HR image given a LR image downscaled from the corresponding original HR image . The downsampling operation is deterministic and known : to produce from , we first convolve using a Gaussian filter - thus simulating the camera ’s point spread function - then downsample the image by a factor of . We will refer to as the upscaling ratio .
With the Gaussian bumps encoded in y cij , the ( 1 − y cij ) term reduces the penalty around the ground truth locations . Many networks [ reference ] ) involve downsampling layers to gather global information and to reduce memory usage . When they are applied to an image fully convolutionally , the size of the output is usually smaller than the image .
The ImageNet images are less blurry , which means neighboring pixels are less correlated to each other and thus less predictable . Because the downsampling method can influence the compression performance , we have made the used downsampled images available . Figure [ reference ] ( right ) shows samples drawn from our model trained on ImageNet .
We demonstrate an application of super - resolving historical photographs with JPEG compression artifacts . In these cases , neither the ground - truth images nor the downsampling kernels are available . As shown in Figure [ reference ] , our method can reconstruct sharper and more accurate images than the state - of - the - art approaches .
Because the feature map dimension increases linearly in the case of additive PyramidNets , the feature map dimensions of the input - side layers tend to be larger , and those of the output - side layers tend to be smaller , compared with multiplicative PyramidNets as illustrated in Figure [ reference ] . Previous works typically set multiplicative scaling of feature map dimension for downsampling modules , which is implemented to give a larger degree of freedom to the classification part by increasing the feature map dimension of the output - side layers . However , for our PyramidNet , the results in Figure [ reference ] implies that increasing the model capacity of the input - side layers would lead to a better performance improvement than using a conventional way of multiplicative scaling of feature map dimension .
One approach involves repeated up - convolutions that aim to recover lost resolution while carrying over the global perspective from downsampled layers . This leaves open the question of whether severe intermediate downsampling was truly necessary . Another approach involves providing multiple rescaled versions of the image as input to the network and combining the predictions obtained for these multiple inputs .
The feature maps are obtained from the backbone network in CornerNet - Saccade , which is an hourglass network . Each hourglass module in the network applies several convolution and downsampling layers to downsize the input feature maps . It then upsamples the feature maps back to the original input resolution by multiple convolution and upsampling layers .
We do not explore the third idea in SqueezeNet . Since the hourglass network has a symmetrical structure , delayed downsampling results in higher resolution feature maps during the upsampling . Performing convolution on high resolution feature maps is computationally expensive , which would prevent us from achieving real - time detection .
subsection : Spatial Sampling Let be the downsampling function that decimates an image to the corresponding image of size . Let be the reverse operation that upsamples images .
While this invariance is clearly desirable for high - level vision tasks , it can hamper low - level tasks , such as pose estimation chen2014articulated , tompson2014joint and semantic segmentation - where we want precise localization , rather than abstraction of spatial details . There are two technical hurdles in the application of DCNNs to image labeling tasks : signal downsampling , and spatial ‘
In this case , with SNIPER , we generate million chips of size x using scales of ( / ms , 1 ) . Note that SNIPER also performs adaptive downsampling . Since the scales are smaller , chips would cover more background , due to which the impact of negative sampling is diminished .
The second one is the volumetric residual block extended from the 2D residual block of option B in [ reference ] . The third one is the volumetric downsampling block that is identical to a volumetric max pooling layer . The last one is the volumetric upsampling block , which consists of a volumetric deconvolution layer , volumetric batch normalization layer , and the activation function ( i.e. , ReLU ) .
On the Use of Band-Passed Excitation Codebooks for CELP Coding of Speech A Continuous-Time Delta-Sigma Modulator for RF Subsampling Receivers New margin-based subsampling iterative technique in modified random forests for classification VLSI architecture for a flexible block matching processor Controlling a confound in predictive models with a test set minimizing its effect Language Models With Meta-information Modifications and performance improvements of 3-step search block-matching algorithm for video coding A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network Compressive spectral X-ray tomography based on spatial and spectral coded illumination. Computational vision models of early vision for target acquisition Fully Convolutional Networks for Semantic Segmentation Deep Learning For Smile Recognition Segmental Recurrent Neural Networks for End-to-End Speech Recognition Striving for Simplicity: The All Convolutional Net Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Multi-Scale Context Aggregation by Dilated Convolutions Variational Autoencoders for Collaborative Filtering Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Small-area decimators for delta-sigma video sensors Down-sampling based embedded compression in video systems A closed-form solution to the graph total variation problem for continuous emotion profiling in noisy environment MTF-Adjusted Pansharpening Approach Based on Maximum Energy Subsampling: A General Scheme For Multi-resolution Image Representation And Analysis Predicting Traffic Flow Based on Encoder-Decoder Framework Method and system for generating and playing network video Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images Bidirectional Texture Functions: Acquisition, Rendering and Quality Evaluation High frequency ultrasound in-plane registration of deformable finger vessels A Resolution Adaptive Video Compression System Research on Single-Frame Super-Resolution Reconstruction Algorithm for Low Resolution Cell Images Based on Convolutional Neural Network Research on Fast Image Segmentation and Matting Technology MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling Real-time quality visualization of medical models on commodity and mobile devices SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems Scan conversion between 1050 2:1 60 Hz and 525 1:1 30 Hz U and V color components Downsampling Blockchain Algorithm Time-interleaved switched-capacitor filter for reconfigurable triple-band delta-sigma converter U-Net: Convolutional Networks for Biomedical Image Segmentation Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network CornerNet: Detecting Objects as Paired Keypoints Pixel Recurrent Neural Networks Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Deep Pyramidal Residual Networks Multi-Scale Context Aggregation by Dilated Convolutions CornerNet-Lite: Efficient Keypoint Based Object Detection Optical Flow Estimation Using a Spatial Pyramid Network Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs SNIPER: Efficient Multi-Scale Training V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
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PRNI IEEE Transactions on Pattern Analysis and Machine Intelligence ArXiv INTERSPEECH ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR WWW ICLR ISCAS CollaborateCom Medical Imaging: Image Processing Intelligent Multimedia Communication INFOCOM Workshops ISCAS (2) MICCAI 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR NeurIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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