A new polar-exponential grid technique for object recognition is proposed in this paper . An object in the rectangular coordinate is mapped into the polar-log coordinate , and the closed profile of the object is transformed a one dimension curve . Images that have multiple labels are cues to what objects co-occur and what do not ; but they are also cues to what image features are stable and what are not . Linguistic phenomena can give strong information about what it means to do object recognition . For example , sentences written about pictures can reveal spatial relations between objects . However real-world scenarios remain a challenge , because face acquisition process can undergo to a wide range of variations . Hence the AFR can be thought as a very complex object recognition problem , where the object to be recognized is the face . This problem becomes even more difficult because the search is done among objects belonging to the same class and very few images of each class are available to train the system . The authors have extended the evidence-based object recognition system of Jain and Hoffman ( 1988 ) to include some new view-independent features , a new optimized rule generation procedure based upon minimum entropy clustering and a neural network which estimates optimal evidence weights and provides an associated matching procedure . This approach provides an objective definition of the difficulty of an object recognition problem . The authors also evaluate the procedures and performance of the system with two sets of CAD ( range ) models.<<ETX > > In the development of technology in the current era is one of the most rapid progress , it is marked by almost all managers of data and information has been done with the computer because of the increasingly diverse information issues handled , Detection of objects is one of the most important early stages before Done object recognition process . Eye blink detection can be used to open a new file or an application . The DDMCMC combines reversible jump and diffusion dynamics to enable the generative models to explain the input images in a competitive and cooperative manner . Our experiments illustrate the advantages and importance of combining bottom-up and top-down models and of performing segmentation and object detection/recognition simultaneously . Abstract Since the features in object-images such as color and shape can not clearly express the characteristic of objects , those features lead to vagueness of object-image recognition . Recently there have been studied on object-image recognition based on knowledge base in order to reduce the vagueness . Applications of Algebraic Geometry to Object/Image Recognition . ( August 2007 ) We felt that the time was ripe for a special issue in this area , covering the full range of probabilistic image models . Topics solicited included : low level models ( image patches , random fields ) ; object recognition/detection ; structural models/image parsing ; structured models of human motion ; probabilistic frameworks for image representation ; efficient algorithms for learning such models ; and frameworks and datasets for evaluating such models . We were particularly interested in approaches that exploit rich structure ( deep , graphical , hierarchical , compositional , etc . ) or that minimize the amount of labelled data required to learn new classes , e.g. by exploiting latent structure or reusing components or priors . Detection of object tracking is an important part of object recognition analysis . In object tracking applications , object detection is the first step of video surveillance , where accurate object detection becomes important and difficult because there are still problems that arise like the shadow of the detected object ( false detection ) . In creating this web-based software , Darkflow and Tensorflow libraries are used which are based on deep learning based Python programming language and allow the design of interactive web based applications . While performing object recognition analysis in the developed software , CNN ( Convolutional Neural Networks ) multiple convolution layers are uncovered hidden and useful features obtained by various calculation methods . With CNN , objects are classified , objects are detected , and objects are determned by image segmentation . Object recognition is obtained by matching local histograms of color invariant features against a learned database . We effectively decompose object appearance recognition into a view based ( learned ) part and an appearance ( invariant ) part . Invariance deals with lighting conditions , color constancy , and robustness against shading effects and cast shadows . In this framework , the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner . Object detection is performed to check existence of objects in video and to precisely locate that object Object recognition in computer vision is the task of finding a given object in an image or video sequence Object detection and tracking is the critical task in many computer vision applications such as video surveillance , driver assistance system , person identification , behavior analysis . Object detection and tracking especially for human and vehicle is currently most active research topic . Matching is achieved using template matching and normalized correlation techniques between the secondary representations . We demonstrate the power of this new technique with several examples of object recognition of models derived from actively sensed range data . subsection : Multi - scale Context - aware Network Visual context is an important component to assist visual - related tasks , such as object recognition and object detection . Typical convolutional neural networks model context information through hierarchical convolution and pooling . document : Multiple Instance Detection Network with Online Instance Classifier Refinement Of late , weakly supervised object detection is with great importance in object recognition . Based on deep learning , weakly supervised detectors have achieved many promising results . In this task , shape category label is not known during the test phase and for each point the network needs to select one of the part label from all possible part labels in all categories . Cross - category setting introduces larger geometric and topological variance among shapes , thus could help examining the spectral CNN ’s ability of recognizing objects . At the same time the impact of spectral domain misalignment becomes stronger , providing a better testbed for validating the effectiveness of SpecTN . section : Introduction Recognizing objects at vastly different scales is a fundamental challenge in computer vision . Feature pyramids built upon image pyramids ( for short we call these featurized image pyramids ) form the basis of a standard solution [ reference ] ( Fig . 1 ( a ) ) . section : INTRODUCTION Recognizing objects and estimating their poses in 3D has a wide range of applications in robotic tasks . For instance , recognizing the 3D location and orientation of objects is important for robot manipulation . section : Introduction Deep Neural Networks ( DNNs ) are extremely powerful machine learning models that achieve excellent performance on difficult problems such as speech recognition and visual object recognition . DNNs are powerful because they can perform arbitrary parallel computation for a modest number of steps . Each surrogate class is formed by applying a variety of transformations to a randomly sampled ’ seed ’ image patch . We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition . The feature representation learned by our algorithm achieves classification results matching or outperforming the current state - of - the - art for unsupervised learning on several popular datasets ( STL - 10 , CIFAR - 10 , Caltech - 101 ) . section : Related Work Object class ( category ) recognition , albeit restricted to photographic depictions , has been studied extensively by researchers . However , little previous work exists for truly general multi - depiction object recognition . In the following we give a detailed description of all the experiments we performed . We evaluate our architecture on various commonly used object recognition benchmarks and improve the state - of - the - art on all of them . The description of the DNN architecture used for the various experiments is given in the following way : We present an exhaustive investigation of recent Deep Learning architectures , algorithms , and strategies for the task of document image classification to finally reduce the error by more than half . Existing approaches , such as the DeepDocClassifier , apply standard Convolutional Network architectures with transfer learning from the object recognition domain . The contribution of the paper is threefold : First , it investigates recently introduced very deep neural network architectures ( GoogLeNet , VGG , ResNet ) using transfer learning ( from real images ) . Furthermore , it may help to solve the real world problems which need the integrated reasoning of vision and language . Deep residual learning not only advances the studies in object recognition problems , but also gives a general framework for deep neural networks . The existing non - linear layers of neural networks serve to fit another mapping of , which is the residual of identity mapping . section : Introduction Deep neural networks have shown great success in various applications such as objection recognition ( see , e.g. , ) and speech recognition ( see , e.g. , ) . Furthermore , many recent works showed that neural networks can be successfully used in a number of tasks in natural language processing ( NLP ) . We build a convolutional neural network to solve this 3D object recognition problem . While training neural networks for 3D object recognition becomes popular these days , collecting enough training data is still a time-consuming task . We demonstrate that it is much more convenient and effective to generate the required training data from 3D CAD models . We present a system to detect passenger cars in aerial images where cars appear as small objects . We pose this as a 3D object recognition problem to account for the variation in viewpoint and the shadow . We started from psychological tests to find important features for human detection of cars . Then , using recursive generation of ; Zernike radial polynomials , pseudo-Zernike radial polynomials , associated Legendre functions , and introducing a novel method to define 3D points-of-symmetry of spherical harmonics multiplied by the 3D object , we present an algorithm for the fast computation of three-dimensional ( 3D ) Zernike moments ( 3DZMs ) and 3DPZMs . The methods that we propose may play an important role in 3D object analysis and recognition . Asymptotic computational complexity and simulation tests have shown that the proposed symmetry-based algorithm is much faster than the direct ( non-symmetry ) . A Case Study . This paper uses digital curvatures for 3D object analysis and recognition . For direct adjacency in 3D , digital surface points have only six types . Such representations greatly simplify the problems of model acquisition and representing complex shapes . I present theoretical and empirical evidence that this " view-based approximation " is an efficient , robust , and reliable approach to 3D visual object recognition . ( 3 ) I present Bayesian and MDL approaches to the similarity problem that may help us build more robust recognition systems . Abstract In this work , we carry out a study of the effect of adverse conditions , which characterize real-world scenes , on the accuracy of a Convolutional Neural Network applied to 3D object class recognition . Firstly , we discuss possible ways of representing 3D data to feed the network . The paper introduces an adaptive technique for 3D object identification and recognition in 3D scanned scenes . This technique uses neural learning of the 3D free-form surface representation of the object in study . In particular , nowadays an RGB-D sensor can be applied for vision-based perception . In this paper , two data-driven RGB-D image analysis steps , required for a reliable 3D object recognition process , are studied and appropriate algorithmic solutions are proposed . Clusters of 3D point features are detected in order to represent 3D object hypotheses . A Hardware Implementation of Virtual Interface Architecture Based on Gigabit Ethernet.- Modeling TCP Loss Recovery Latency with the Number of Retransmissions.- A Two-Step EM Algorithm for MAP Fitting.- Performance Evaluation of the 802.16 Medium Access Control Layer.- Fixed Size and Variable Size Packet Models in an Optical Ring Network : Complexity and Simulations.- A Tabu Search Algorithm for Sparse Placement of Wavelength Converters in Optical Networks.- Mixed Routing for ROMEO Optical Burst.- A Fast Hardware-Oriented Algorithm for Cellular Mobiles Positioning.- Relay Attacks on Bluetooth Authentication and Solutions.- Reliable Delivery of Popular Data Services in WIDE.- Wireless Interworking Independent CAN Segments.- Real-Time Admission Control Supporting Prioritized Soft Handoff Calls in Cellular DS-CDMA Systems.- Voice Traffic Integration in Wireless Data Networks with Channel State Information.- Blind Collision Resolution Using Retransmission Diversity under Quasi-Static Fading Channels.- CRAM : An Energy Efficient Routing Algorithm for Wireless Sensor Networks.- An Approach for Spam E-mail Detection with Support Vector Machine and n-Gram Indexing.- Finding Breach Paths Using the Watershed Segmentation Algorithm in Surveillance Wireless Sensor Networks.- Biometric Authentication Using Online Signatures.- Computer Vision and Image Processing.- Moving Region Detection in Compressed Video.- Shape Recognition with Generalized Beam Angle Statistics.- 3D Cognitive Map Construction by Active Stereo Vision in a Virtual World.- 3D Real Object Recognition on the Basis of Moment Invariants and Neural Networks.- A Novel Watermarking for Image Security.- Boosting Face Recognition Speed with a Novel Divide-and-Conquer Approach.- A New Document Watermarking Algorithm Based on Hybrid Multi-scale Ant Colony We discuss various optimisation criteria and provide results on a TIMIT phoneme recognition task , using single frame MMI embedded in Viterbi training , and a global MMI criterion . As expected , global MMJ is found superior to the frame-based criterion for continuous recognition . We furt.her observe that optimal sentence decoding is essential to achieve maximum recognition rate for models trained by global MMI . Conventionally , the object detection algorithm proposed by Viola Jones ( using Haar-like features , Integral image and AdaBoost algorithm ) is implemented in majority of the face detection applications . In this article , an improvement to the application of Viola Jones algorithm in real time environment is presented by exploiting the analogy between video motion estimation and continuous object detection . Using the temporal correlation between successive frames of a real time input , various modifications improving the robustness and computational complexity of face detection are proposed . To reduce network bandwidth usage , Glimpse computes trigger frames to send to the server for recognizing and labeling . Experiments with Android smartphones and Google Glass over Verizon , AT&T , and a campus Wi-Fi network show that with hardware face detection support ( available on many mobile devices ) , Glimpse achieves precision between 96.4 % to 99.8 % for continuous face recognition , which improves over a scheme performing hardware face detection and server-side recognition without Glimpse 's techniques by between 1.8 - 2.5 × . The improvement in precision for face recognition without hardware detection is between 1.6 - 5.5 × . The proposed method involves extracting important features of the face , subjecting them to PCA to reduce the dimensions and calculate the Eigen image . Much of the focus for the implemented system was also on its real-time behavior while performing continuous face recognition . Evaluation of our system is performed with respect to false acceptance and false rejection errors to computer the recognition rates . In the context of real-world object recognition applications ( e.g. , robotic vision ) , where continuous learning is crucial , very few datasets and benchmarks are available to evaluate and compare emerging techniques . In this work we propose a new dataset and benchmark CORe50 , specifically designed for continuous object recognition , and introduce baseline approaches for different continuous learning scenarios . For the consolidation of knowledge in the absence of external sensory input , the episodic memory periodically replays trajectories of neural reactivations . We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition , showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenarios . We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn . This setting has a set of unique characteristics : it assumes an egocentric point-of-view bound to the needs of a singleperson , which implies a relatively low diversity of data and a coldstart with no data ; it requires to operate in an open world , where newobjects can be encounteredat any time ; supervision is scarce and hasto be solicited to the user , and completelyunsupervised recognitionof new objects should be possible . Whereas the classification maps each image into one of the 7 basic emotions ( neutral , happy , sad , disgust , surprise , fear , anger ) and the regression maps each image into an one-dimensional emotion space . Both methods , the continual recognition and the classification based on Active Appearance Models ( AAMs ) and Support Vector Machines ( SVMs ) . To reduce the influence of individual features an Individual Mean Face ( MF ) is estimated over time . We present an efficient algorithm for continuous image recognition and feature descriptor tracking in video which operates by reducing the search space of possible interest points inside of the scale space image pyramid . Instead of performing tracking in 2D images , we search and match candidate features in local neighborhoods inside the 3D image pyramid without computing their feature descriptors . Excerpted from " Glimpse : Continuous , Real-Time Object Recognition on Mobile Devices " from Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems with permission . http://dx.doi.org/10.1145/2809695.2809711 © ACM 2015 . Furthermore , we show that the neural networks outperform previous methods significantly in terms of classification accuracy . Finally , we demonstrate that the networks retrained with Random Activation Preservation ( RAP ) can classify very early - within a fraction of a second - but robustly at the same time in a continuous recognition scenario with varying classes . The automatic segmentation boundaries derived from modeling PLAS units agreed well with corresponding manual segmentation points , i.e. they were within a + or-20-ms interval 95 % of the time . A 4 % phoneme recognition error rate was obtained in a continuous recognition test . Natural-sounding speech was synthesized at an average bit rate of 55 b/s allocated to segmental information.<<ETX > > There is no need for any prior learning . Our descriptor achieves results on par with existing state of the art , when applied to object matching on a standard dataset consisting of photographs alone and outperforms the state of the art when applied to depiction-invariant object matching . We compare SwiDeN with alternative architectures and prior work on a 50-category Photo-Art dataset containing objects depicted in multiple styles . Experimental results show that SwiDeN outperforms other approaches for the depiction-invariant object recognition problem . A fundamental issue in visual cognition is whether high-level visual areas code objects in a part-based or a view-based ( holistic ) format . Previous behavioral and neuroimaging studies that examined the viewpoint invariance of object recognition have yielded ambiguous results , providing evidence for either type of representational format . A critical factor distinguishing the two formats could be the availability of attentional resources , as a number of priming studies have found greater viewpoint invariance for attended compared to unattended objects . We compare SwiDeN with alternative architectures and prior work on a - category Photo - Art dataset containing objects depicted in multiple styles . Experimental results show that SwiDeN outperforms other approaches for the depiction - invariant object recognition problem . 1 Moreover , the associated performance scores can also aid in quantitatively determining the semantic gap between human and machine capabilities . Surprisingly , not much work exists for depiction - invariant object recognition . To address this gap , we propose a Convolutional Neural Network ( CNN ) architecture for depiction - invariant object category recognition which we call SwiDeN ( Section [ reference ] ) . A novel aspect of our architecture is a ‘ deep ’ dynamic switching mechanism between two parallel CNN sub - architectures ( Section [ reference ] ) . All the approaches mentioned above utilize multiple hand - crafted modules in the recognition pipeline . To the best of our knowledge , ours is the first end - to - end deep learning approach for depiction - invariant recognition of object categories . section : Our framework ( c ) ) . Since our final objective is to achieve depiction - invariant recognition , we require our network to learn a depiction - invariant feature representation . This is achieved by having a final set of layers .
A new polar-exponential grid technique for object recognition is proposed in this paper . An object in the rectangular coordinate is mapped into the polar-log coordinate , and the closed profile of the object is transformed a one dimension curve .
Images that have multiple labels are cues to what objects co-occur and what do not ; but they are also cues to what image features are stable and what are not . Linguistic phenomena can give strong information about what it means to do object recognition . For example , sentences written about pictures can reveal spatial relations between objects .
However real-world scenarios remain a challenge , because face acquisition process can undergo to a wide range of variations . Hence the AFR can be thought as a very complex object recognition problem , where the object to be recognized is the face . This problem becomes even more difficult because the search is done among objects belonging to the same class and very few images of each class are available to train the system .
The authors have extended the evidence-based object recognition system of Jain and Hoffman ( 1988 ) to include some new view-independent features , a new optimized rule generation procedure based upon minimum entropy clustering and a neural network which estimates optimal evidence weights and provides an associated matching procedure . This approach provides an objective definition of the difficulty of an object recognition problem . The authors also evaluate the procedures and performance of the system with two sets of CAD ( range ) models.<<ETX > >
In the development of technology in the current era is one of the most rapid progress , it is marked by almost all managers of data and information has been done with the computer because of the increasingly diverse information issues handled , Detection of objects is one of the most important early stages before Done object recognition process . Eye blink detection can be used to open a new file or an application .
The DDMCMC combines reversible jump and diffusion dynamics to enable the generative models to explain the input images in a competitive and cooperative manner . Our experiments illustrate the advantages and importance of combining bottom-up and top-down models and of performing segmentation and object detection/recognition simultaneously .
Abstract Since the features in object-images such as color and shape can not clearly express the characteristic of objects , those features lead to vagueness of object-image recognition . Recently there have been studied on object-image recognition based on knowledge base in order to reduce the vagueness .
Applications of Algebraic Geometry to Object/Image Recognition . ( August 2007 )
We felt that the time was ripe for a special issue in this area , covering the full range of probabilistic image models . Topics solicited included : low level models ( image patches , random fields ) ; object recognition/detection ; structural models/image parsing ; structured models of human motion ; probabilistic frameworks for image representation ; efficient algorithms for learning such models ; and frameworks and datasets for evaluating such models . We were particularly interested in approaches that exploit rich structure ( deep , graphical , hierarchical , compositional , etc . ) or that minimize the amount of labelled data required to learn new classes , e.g. by exploiting latent structure or reusing components or priors .
Detection of object tracking is an important part of object recognition analysis . In object tracking applications , object detection is the first step of video surveillance , where accurate object detection becomes important and difficult because there are still problems that arise like the shadow of the detected object ( false detection ) .
In creating this web-based software , Darkflow and Tensorflow libraries are used which are based on deep learning based Python programming language and allow the design of interactive web based applications . While performing object recognition analysis in the developed software , CNN ( Convolutional Neural Networks ) multiple convolution layers are uncovered hidden and useful features obtained by various calculation methods . With CNN , objects are classified , objects are detected , and objects are determned by image segmentation .
Object recognition is obtained by matching local histograms of color invariant features against a learned database . We effectively decompose object appearance recognition into a view based ( learned ) part and an appearance ( invariant ) part . Invariance deals with lighting conditions , color constancy , and robustness against shading effects and cast shadows .
In this framework , the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner . Object detection is performed to check existence of objects in video and to precisely locate that object Object recognition in computer vision is the task of finding a given object in an image or video sequence Object detection and tracking is the critical task in many computer vision applications such as video surveillance , driver assistance system , person identification , behavior analysis . Object detection and tracking especially for human and vehicle is currently most active research topic .
Matching is achieved using template matching and normalized correlation techniques between the secondary representations . We demonstrate the power of this new technique with several examples of object recognition of models derived from actively sensed range data .
subsection : Multi - scale Context - aware Network Visual context is an important component to assist visual - related tasks , such as object recognition and object detection . Typical convolutional neural networks model context information through hierarchical convolution and pooling .
document : Multiple Instance Detection Network with Online Instance Classifier Refinement Of late , weakly supervised object detection is with great importance in object recognition . Based on deep learning , weakly supervised detectors have achieved many promising results .
In this task , shape category label is not known during the test phase and for each point the network needs to select one of the part label from all possible part labels in all categories . Cross - category setting introduces larger geometric and topological variance among shapes , thus could help examining the spectral CNN ’s ability of recognizing objects . At the same time the impact of spectral domain misalignment becomes stronger , providing a better testbed for validating the effectiveness of SpecTN .
section : Introduction Recognizing objects at vastly different scales is a fundamental challenge in computer vision . Feature pyramids built upon image pyramids ( for short we call these featurized image pyramids ) form the basis of a standard solution [ reference ] ( Fig . 1 ( a ) ) .
section : INTRODUCTION Recognizing objects and estimating their poses in 3D has a wide range of applications in robotic tasks . For instance , recognizing the 3D location and orientation of objects is important for robot manipulation .
section : Introduction Deep Neural Networks ( DNNs ) are extremely powerful machine learning models that achieve excellent performance on difficult problems such as speech recognition and visual object recognition . DNNs are powerful because they can perform arbitrary parallel computation for a modest number of steps .
Each surrogate class is formed by applying a variety of transformations to a randomly sampled ’ seed ’ image patch . We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition . The feature representation learned by our algorithm achieves classification results matching or outperforming the current state - of - the - art for unsupervised learning on several popular datasets ( STL - 10 , CIFAR - 10 , Caltech - 101 ) .
section : Related Work Object class ( category ) recognition , albeit restricted to photographic depictions , has been studied extensively by researchers . However , little previous work exists for truly general multi - depiction object recognition .
In the following we give a detailed description of all the experiments we performed . We evaluate our architecture on various commonly used object recognition benchmarks and improve the state - of - the - art on all of them . The description of the DNN architecture used for the various experiments is given in the following way :
We present an exhaustive investigation of recent Deep Learning architectures , algorithms , and strategies for the task of document image classification to finally reduce the error by more than half . Existing approaches , such as the DeepDocClassifier , apply standard Convolutional Network architectures with transfer learning from the object recognition domain . The contribution of the paper is threefold : First , it investigates recently introduced very deep neural network architectures ( GoogLeNet , VGG , ResNet ) using transfer learning ( from real images ) .
Furthermore , it may help to solve the real world problems which need the integrated reasoning of vision and language . Deep residual learning not only advances the studies in object recognition problems , but also gives a general framework for deep neural networks . The existing non - linear layers of neural networks serve to fit another mapping of , which is the residual of identity mapping .
section : Introduction Deep neural networks have shown great success in various applications such as objection recognition ( see , e.g. , ) and speech recognition ( see , e.g. , ) . Furthermore , many recent works showed that neural networks can be successfully used in a number of tasks in natural language processing ( NLP ) .
We build a convolutional neural network to solve this 3D object recognition problem . While training neural networks for 3D object recognition becomes popular these days , collecting enough training data is still a time-consuming task . We demonstrate that it is much more convenient and effective to generate the required training data from 3D CAD models .
We present a system to detect passenger cars in aerial images where cars appear as small objects . We pose this as a 3D object recognition problem to account for the variation in viewpoint and the shadow . We started from psychological tests to find important features for human detection of cars .
Then , using recursive generation of ; Zernike radial polynomials , pseudo-Zernike radial polynomials , associated Legendre functions , and introducing a novel method to define 3D points-of-symmetry of spherical harmonics multiplied by the 3D object , we present an algorithm for the fast computation of three-dimensional ( 3D ) Zernike moments ( 3DZMs ) and 3DPZMs . The methods that we propose may play an important role in 3D object analysis and recognition . Asymptotic computational complexity and simulation tests have shown that the proposed symmetry-based algorithm is much faster than the direct ( non-symmetry ) .
A Case Study . This paper uses digital curvatures for 3D object analysis and recognition . For direct adjacency in 3D , digital surface points have only six types .
Such representations greatly simplify the problems of model acquisition and representing complex shapes . I present theoretical and empirical evidence that this " view-based approximation " is an efficient , robust , and reliable approach to 3D visual object recognition . ( 3 ) I present Bayesian and MDL approaches to the similarity problem that may help us build more robust recognition systems .
Abstract In this work , we carry out a study of the effect of adverse conditions , which characterize real-world scenes , on the accuracy of a Convolutional Neural Network applied to 3D object class recognition . Firstly , we discuss possible ways of representing 3D data to feed the network .
The paper introduces an adaptive technique for 3D object identification and recognition in 3D scanned scenes . This technique uses neural learning of the 3D free-form surface representation of the object in study .
In particular , nowadays an RGB-D sensor can be applied for vision-based perception . In this paper , two data-driven RGB-D image analysis steps , required for a reliable 3D object recognition process , are studied and appropriate algorithmic solutions are proposed . Clusters of 3D point features are detected in order to represent 3D object hypotheses .
A Hardware Implementation of Virtual Interface Architecture Based on Gigabit Ethernet.- Modeling TCP Loss Recovery Latency with the Number of Retransmissions.- A Two-Step EM Algorithm for MAP Fitting.- Performance Evaluation of the 802.16 Medium Access Control Layer.- Fixed Size and Variable Size Packet Models in an Optical Ring Network : Complexity and Simulations.- A Tabu Search Algorithm for Sparse Placement of Wavelength Converters in Optical Networks.- Mixed Routing for ROMEO Optical Burst.- A Fast Hardware-Oriented Algorithm for Cellular Mobiles Positioning.- Relay Attacks on Bluetooth Authentication and Solutions.- Reliable Delivery of Popular Data Services in WIDE.- Wireless Interworking Independent CAN Segments.- Real-Time Admission Control Supporting Prioritized Soft Handoff Calls in Cellular DS-CDMA Systems.- Voice Traffic Integration in Wireless Data Networks with Channel State Information.- Blind Collision Resolution Using Retransmission Diversity under Quasi-Static Fading Channels.- CRAM : An Energy Efficient Routing Algorithm for Wireless Sensor Networks.- An Approach for Spam E-mail Detection with Support Vector Machine and n-Gram Indexing.- Finding Breach Paths Using the Watershed Segmentation Algorithm in Surveillance Wireless Sensor Networks.- Biometric Authentication Using Online Signatures.- Computer Vision and Image Processing.- Moving Region Detection in Compressed Video.- Shape Recognition with Generalized Beam Angle Statistics.- 3D Cognitive Map Construction by Active Stereo Vision in a Virtual World.- 3D Real Object Recognition on the Basis of Moment Invariants and Neural Networks.- A Novel Watermarking for Image Security.- Boosting Face Recognition Speed with a Novel Divide-and-Conquer Approach.- A New Document Watermarking Algorithm Based on Hybrid Multi-scale Ant Colony
We discuss various optimisation criteria and provide results on a TIMIT phoneme recognition task , using single frame MMI embedded in Viterbi training , and a global MMI criterion . As expected , global MMJ is found superior to the frame-based criterion for continuous recognition . We furt.her observe that optimal sentence decoding is essential to achieve maximum recognition rate for models trained by global MMI .
Conventionally , the object detection algorithm proposed by Viola Jones ( using Haar-like features , Integral image and AdaBoost algorithm ) is implemented in majority of the face detection applications . In this article , an improvement to the application of Viola Jones algorithm in real time environment is presented by exploiting the analogy between video motion estimation and continuous object detection . Using the temporal correlation between successive frames of a real time input , various modifications improving the robustness and computational complexity of face detection are proposed .
To reduce network bandwidth usage , Glimpse computes trigger frames to send to the server for recognizing and labeling . Experiments with Android smartphones and Google Glass over Verizon , AT&T , and a campus Wi-Fi network show that with hardware face detection support ( available on many mobile devices ) , Glimpse achieves precision between 96.4 % to 99.8 % for continuous face recognition , which improves over a scheme performing hardware face detection and server-side recognition without Glimpse 's techniques by between 1.8 - 2.5 × . The improvement in precision for face recognition without hardware detection is between 1.6 - 5.5 × .
The proposed method involves extracting important features of the face , subjecting them to PCA to reduce the dimensions and calculate the Eigen image . Much of the focus for the implemented system was also on its real-time behavior while performing continuous face recognition . Evaluation of our system is performed with respect to false acceptance and false rejection errors to computer the recognition rates .
In the context of real-world object recognition applications ( e.g. , robotic vision ) , where continuous learning is crucial , very few datasets and benchmarks are available to evaluate and compare emerging techniques . In this work we propose a new dataset and benchmark CORe50 , specifically designed for continuous object recognition , and introduce baseline approaches for different continuous learning scenarios .
For the consolidation of knowledge in the absence of external sensory input , the episodic memory periodically replays trajectories of neural reactivations . We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition , showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenarios .
We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn . This setting has a set of unique characteristics : it assumes an egocentric point-of-view bound to the needs of a singleperson , which implies a relatively low diversity of data and a coldstart with no data ; it requires to operate in an open world , where newobjects can be encounteredat any time ; supervision is scarce and hasto be solicited to the user , and completelyunsupervised recognitionof new objects should be possible .
Whereas the classification maps each image into one of the 7 basic emotions ( neutral , happy , sad , disgust , surprise , fear , anger ) and the regression maps each image into an one-dimensional emotion space . Both methods , the continual recognition and the classification based on Active Appearance Models ( AAMs ) and Support Vector Machines ( SVMs ) . To reduce the influence of individual features an Individual Mean Face ( MF ) is estimated over time .
We present an efficient algorithm for continuous image recognition and feature descriptor tracking in video which operates by reducing the search space of possible interest points inside of the scale space image pyramid . Instead of performing tracking in 2D images , we search and match candidate features in local neighborhoods inside the 3D image pyramid without computing their feature descriptors .
Excerpted from " Glimpse : Continuous , Real-Time Object Recognition on Mobile Devices " from Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems with permission . http://dx.doi.org/10.1145/2809695.2809711 © ACM 2015 .
Furthermore , we show that the neural networks outperform previous methods significantly in terms of classification accuracy . Finally , we demonstrate that the networks retrained with Random Activation Preservation ( RAP ) can classify very early - within a fraction of a second - but robustly at the same time in a continuous recognition scenario with varying classes .
The automatic segmentation boundaries derived from modeling PLAS units agreed well with corresponding manual segmentation points , i.e. they were within a + or-20-ms interval 95 % of the time . A 4 % phoneme recognition error rate was obtained in a continuous recognition test . Natural-sounding speech was synthesized at an average bit rate of 55 b/s allocated to segmental information.<<ETX > >
There is no need for any prior learning . Our descriptor achieves results on par with existing state of the art , when applied to object matching on a standard dataset consisting of photographs alone and outperforms the state of the art when applied to depiction-invariant object matching .
We compare SwiDeN with alternative architectures and prior work on a 50-category Photo-Art dataset containing objects depicted in multiple styles . Experimental results show that SwiDeN outperforms other approaches for the depiction-invariant object recognition problem .
A fundamental issue in visual cognition is whether high-level visual areas code objects in a part-based or a view-based ( holistic ) format . Previous behavioral and neuroimaging studies that examined the viewpoint invariance of object recognition have yielded ambiguous results , providing evidence for either type of representational format . A critical factor distinguishing the two formats could be the availability of attentional resources , as a number of priming studies have found greater viewpoint invariance for attended compared to unattended objects .
We compare SwiDeN with alternative architectures and prior work on a - category Photo - Art dataset containing objects depicted in multiple styles . Experimental results show that SwiDeN outperforms other approaches for the depiction - invariant object recognition problem . 1
Moreover , the associated performance scores can also aid in quantitatively determining the semantic gap between human and machine capabilities . Surprisingly , not much work exists for depiction - invariant object recognition . To address this gap , we propose a Convolutional Neural Network ( CNN ) architecture for depiction - invariant object category recognition which we call SwiDeN ( Section [ reference ] ) . A novel aspect of our architecture is a ‘ deep ’ dynamic switching mechanism between two parallel CNN sub - architectures ( Section [ reference ] ) .
All the approaches mentioned above utilize multiple hand - crafted modules in the recognition pipeline . To the best of our knowledge , ours is the first end - to - end deep learning approach for depiction - invariant recognition of object categories . section : Our framework
( c ) ) . Since our final objective is to achieve depiction - invariant recognition , we require our network to learn a depiction - invariant feature representation . This is achieved by having a final set of layers .
Object recognition using polar-exponential grid technique Words and Pictures Local Feature Based Face Recognition Some new techniques for evidence-based object recognition: EB-ORS1 DETEKSI KEDIPAN MATA PADA VIDEO MENGGUNAKAN OPEN CV Image parsing: unifying segmentation, detection, and recognition Ontology-based Object-Image Recognition by Using Information on Inner-Objects Applications of algebraic geometry to object/image recognition Editorial: Special Issue on Probabilistic Models for Image Understanding PENGEMBANGAN METODE PELACAKAN OBJEK BERBASIS SEGMENTASI MENGGUNAKAN ALGORITMA FCM A Web-Based Application for Identifying Objects In Images: Object Recognition Software Object Recognition by a Robot Dog Connected to aWide-Area Grid System Result Analysis of Object Detection and Tracking for low vision video Model-based 3-D object recognition using scalar transform descriptors Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Multiple Instance Detection Network with Online Instance Classifier Refinement SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Feature Pyramid Networks for Object Detection PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Sequence to Sequence Learning with Neural Networks Discriminative Unsupervised Feature Learning with Convolutional Neural Networks SwiDeN: Convolutional Neural Networks For Depiction Invariant Object Recognition Multi-column deep neural networks for image classification Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification Multimodal Residual Learning for Visual QA Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Chess recognition from a single depth image Car detection in low resolution aerial image 3D (pseudo) Zernike moments: Fast computation via symmetry properties of spherical harmonics and recursive radial polynomials Digital Curvatures Applied to 3D Object Analysis and Recognition: A Case Study Geometric Aspects of Visual Object Recognition A study of the effect of noise and occlusion on the accuracy of convolutional neural networks applied to 3D object recognition Adaptive object identification and recognition using neural networks and surface signatures Point-Based Object Recognition in RGB-D Images Computer and information sciences : ISCIS 2004, 19th international symposium, Kemer-Antalya, Turkey, October 27-29, 2004 : proceedings NON-LINEAR INPUT TRANSFORMATIONS FOR DISCRIMINATIVE HMMS Temporal correlation and probabilistic prediction based face detection framework in real time environment Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices Development of A Biometric Based Access Control System CORe50: a New Dataset and Benchmark for Continuous Object Recognition Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization Continual egocentric object recognition AAM based continuous facial expression recognition for face image sequences SURFTrac: Efficient tracking and continuous object recognition using local feature descriptors GLIMPSE: Continuous, Real-Time Object Recognition on Mobile Devices Recurrent Neural Networks for fast and robust vibration-based ground classification on mobile robots A phonetically labeled acoustic segment (PLAS) approach to speech analysis-synthesis Depiction Inviariant Object Matching SwiDeN: Convolutional Neural Networks For Depiction Invariant Object Recognition Non-holistic coding of objects in lateral occipital complex with and without attention SwiDeN: Convolutional Neural Networks For Depiction Invariant Object Recognition
74463250720c1984080aff158787711a01456246 03ab262103f9281d00b6af75fb9eb619a2b8d86f 68902dae8e0e6158342fcbf6fa0f7068a83c083a bcde3a961a3936718a00ba5af30684c639f25a3c d19c7b2c231e115a4b18f4365e6577e2913ed842 cca9200d9da958b7f90eab901b2f30c04f1e0e9c f6683c62cde6fa7acb9e7ca9922531da0fa74751 e6a79671c908aac590cca0dff4dff8df3d010bd7 449a9b6edafa0a96073f70590d2fb433583c4bd0 ef3ee3930d6fc7736ce5941f72631f234cfe38ed c17a3c824f0f49be74ff0eb2d05bda3872c9614c bcc7275f45073a1a5165a709f62c7a27f340ae64 fee3b479148e28ad13bb40bf3f63bf0584651f30 f97b70ed2cbb66cf246a02c0b90d0c47154b9a8c f68bdb702409e04c5c709b0fc5579f27d52b60d0 90885cfbb7b8cb2a7a7e85f615f647413f05bfc7 1e18544d23a4ec37f6b1277f6d1b578ca0740ade 6b2248d768051a33f3659bbe5c25595d6066024e f4a52ccc11a09f1e84ff909f61470bd642890296 b070fbca253e205b1521ff27a1c0039bd396ae6d e6fba6c696b061376b5526c2ed5eff112dec404f 5a914964fa872095062d90ba52a038f92688dd26 1a24daae81da97d6b5d0751407fef8b0528d2ef2 8aee4b5ce8e186d1ca1e8957563bc7269c3ba7f3 dae811b5acdf8ea17a7d904127c9e7af808ddcbf 4c6fe7ceb3f23c44881ba0ffa6efc5741c581a3c b4f3022f9028343e2b991f3783b6236dfa9b694e 0eddc1600c51f524887b3c5fe3d972e3c4af4dcc a4b158ec2c64731c1fc6a293476f611a15fa32eb 38e82f59b7ea81014687193946b70f133360925c aec9d11d65b0e1884fee505a023796d446c8b97c 442f41049ce0360eba8d0b85bbad1ca995070444 6822c448f215181d12a888e8eb70621d819e2c21 60f536a542ad1f45f9bf728334f44563f183d83c 190ace9b9e6731e39ea1b7b05de6c31aade3e9ed 0ec3c817c50d3bc19ac78e42962606c7ed671dfb 0f0a25d3be0d50a134f6f68e6a82bd8a2f668882 3c38ee47d2dd2a8637ae75c0460aabac102a9ec8
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine","Psychology"]
2003 2007 2011 1992 2017 2003 2009 2007 2010 2019 2019 2005 2016 1992 2017 2017 2017 2017 2018 2014 2014 2016 2012 2017 2016 2014 2017 2001 2012 2009 2011 2017 2003 2015 2004 1994 2012 2015 2012 2017 2018 2019 2011 2009 2016 2016 1989 2012 2016 2015 2016
ICPR (2) ICCV ICME 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Robotics: Science and Systems NIPS NIPS ACM Multimedia 2012 IEEE Conference on Computer Vision and Pattern Recognition 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) NIPS EMNLP ICME ICCV ICIP AVSS IEEE Conf. on Intelligent Systems (2) IHCI SenSys CVPR ICRA BMVC ACM Multimedia ACM Multimedia
https://api.semanticscholar.org/74463250720c1984080aff158787711a01456246 https://api.semanticscholar.org/03ab262103f9281d00b6af75fb9eb619a2b8d86f https://api.semanticscholar.org/68902dae8e0e6158342fcbf6fa0f7068a83c083a https://api.semanticscholar.org/bcde3a961a3936718a00ba5af30684c639f25a3c https://api.semanticscholar.org/d19c7b2c231e115a4b18f4365e6577e2913ed842 https://api.semanticscholar.org/cca9200d9da958b7f90eab901b2f30c04f1e0e9c https://api.semanticscholar.org/f6683c62cde6fa7acb9e7ca9922531da0fa74751 https://api.semanticscholar.org/e6a79671c908aac590cca0dff4dff8df3d010bd7 https://api.semanticscholar.org/449a9b6edafa0a96073f70590d2fb433583c4bd0 https://api.semanticscholar.org/ef3ee3930d6fc7736ce5941f72631f234cfe38ed https://api.semanticscholar.org/c17a3c824f0f49be74ff0eb2d05bda3872c9614c https://api.semanticscholar.org/bcc7275f45073a1a5165a709f62c7a27f340ae64 https://api.semanticscholar.org/fee3b479148e28ad13bb40bf3f63bf0584651f30 https://api.semanticscholar.org/f97b70ed2cbb66cf246a02c0b90d0c47154b9a8c https://www.semanticscholar.org/paper/605886b07ec27492e8d5bc48f79ddc39c02ec190 https://www.semanticscholar.org/paper/05357b8c05b5bc020e871fc330a88910c3177e4d https://www.semanticscholar.org/paper/ade1b1bfe2abefbcfe324499aa284db2b8fc50a6 https://www.semanticscholar.org/paper/b9b4e05faa194e5022edd9eb9dd07e3d675c2b36 https://www.semanticscholar.org/paper/1c7e078611c9df412e6eb3a356f31a0da0c1f99c https://www.semanticscholar.org/paper/cea967b59209c6be22829699f05b8b1ac4dc092d https://www.semanticscholar.org/paper/a6fd96a900d4130940b488863b71fd09ad41ccb9 https://www.semanticscholar.org/paper/0f0a25d3be0d50a134f6f68e6a82bd8a2f668882 https://www.semanticscholar.org/paper/398c296d0cc7f9d180f84969f8937e6d3a413796 https://www.semanticscholar.org/paper/3526555fa0178c101ee9896252c818f9e03532a5 https://www.semanticscholar.org/paper/1afb710a5b35a2352a6495e4bf6eef66808daf1b https://www.semanticscholar.org/paper/0b544dfe355a5070b60986319a3f51fb45d1348e https://api.semanticscholar.org/f68bdb702409e04c5c709b0fc5579f27d52b60d0 https://api.semanticscholar.org/90885cfbb7b8cb2a7a7e85f615f647413f05bfc7 https://api.semanticscholar.org/1e18544d23a4ec37f6b1277f6d1b578ca0740ade https://api.semanticscholar.org/6b2248d768051a33f3659bbe5c25595d6066024e https://api.semanticscholar.org/f4a52ccc11a09f1e84ff909f61470bd642890296 https://api.semanticscholar.org/b070fbca253e205b1521ff27a1c0039bd396ae6d https://api.semanticscholar.org/e6fba6c696b061376b5526c2ed5eff112dec404f https://api.semanticscholar.org/5a914964fa872095062d90ba52a038f92688dd26 https://api.semanticscholar.org/1a24daae81da97d6b5d0751407fef8b0528d2ef2 https://api.semanticscholar.org/8aee4b5ce8e186d1ca1e8957563bc7269c3ba7f3 https://api.semanticscholar.org/dae811b5acdf8ea17a7d904127c9e7af808ddcbf https://api.semanticscholar.org/4c6fe7ceb3f23c44881ba0ffa6efc5741c581a3c https://api.semanticscholar.org/b4f3022f9028343e2b991f3783b6236dfa9b694e https://api.semanticscholar.org/0eddc1600c51f524887b3c5fe3d972e3c4af4dcc https://api.semanticscholar.org/a4b158ec2c64731c1fc6a293476f611a15fa32eb https://api.semanticscholar.org/38e82f59b7ea81014687193946b70f133360925c https://api.semanticscholar.org/aec9d11d65b0e1884fee505a023796d446c8b97c https://api.semanticscholar.org/442f41049ce0360eba8d0b85bbad1ca995070444 https://api.semanticscholar.org/6822c448f215181d12a888e8eb70621d819e2c21 https://api.semanticscholar.org/60f536a542ad1f45f9bf728334f44563f183d83c https://api.semanticscholar.org/190ace9b9e6731e39ea1b7b05de6c31aade3e9ed https://api.semanticscholar.org/0ec3c817c50d3bc19ac78e42962606c7ed671dfb https://api.semanticscholar.org/0f0a25d3be0d50a134f6f68e6a82bd8a2f668882 https://api.semanticscholar.org/3c38ee47d2dd2a8637ae75c0460aabac102a9ec8 https://www.semanticscholar.org/paper/0f0a25d3be0d50a134f6f68e6a82bd8a2f668882