Enrollment numbers and retention rates have been decreasing in technology programs in United States of America , particularly for underrepresented minority groups . At St. Mary 's University , we started an Engineering Summer Program ( ESP ) that consists of programming and robotics sessions for high school students . The programming courses offer an opportunity for many students to write a computer program for the first time in their lives . With the increasing application of Event Stream Processing ( ESP ) for event pattern detection , it has become important to enhance the extant ESP capabilities to deal with applications having dynamic behavior . This dissertation research explores the limitations of current ESP systems due to fixed pattern detection mechanism and discusses the motivational ideas that demand enhancements in ESP . We propose a solution called adaptive ESP that explores , learns , and updates evolving patterns in dynamic applications . Event Stream Processing ( ESP ) Systems are currently enabling a renaissance in the data processing area as they provide results at low latency compared to the traditional MapReduce approach . Although the majority of ESP systems offer some form of fault tolerance to their users , the provided fault tolerance scheme is often not tailored to the application at hand . For example , active replication is well suited for critical applications where unresponsiveness due to a background recovery process is not acceptable . This talk explores the relationship between CEP and ESP in the context of event processing , and in particular to an event processing reference architecture derived from earlier distributed blackboard computing models . After introducing the model , we explore where ESP and CEP " fit " by applying ESP and CEP concepts to practical use cases for event processing , drawing from signal processing , decision theory , control theory , and stochastic processing . The basis of the talk is derived from established thinking in the domain of multi-sensor data fusion , applying traditional concepts to today 's commercial view of event processing . In order to meet the need of the social development , ESP Course will become more and more important in English teaching . The paper , from the angle of the important role of ESP Course , tries ESP classroom design method centered as skills The most important is that its design must be centered as skill , so as to establish the training goals and standards of a talent . The new electrohydrodynamically ( EHD ) assisted ESP ( EHD ESP ) was developed to minimize reentrainment . The EHD ESP utilizes the ionic wind , combined with electrostatic force to transport the charged particles into the pocket zone attached to the collection plate effectively . The pocket zones are designed as zero electrostatic field , i.e. , no electrostatic repulsion force acting on particles , so that no reentrainment takes place . The main aim of the research is therefore to understand current assessment practices as well as to develop a standardized benchmarking for ESP teachers . The paper proposes a model of ESP standardized assessment based on the studied reference , ESP practices used in different countries , as well as standards of assessment in general . The present invention relates to a brake control device of the vehicle ABS unit and the composite control hydraulic parking brake is automatically controlled apparatus , normally-closed solenoid check which controls the flow of hydraulic Hyde rolrik unit of the ABS module to prevent locking of the wheel valve to be configured and relates to an apparatus and a control method for automatically controlling a hydraulic parking brake is controlled by the ECU commands to ABS and ESP devices and complex . The present invention To this end , the configuration for normal sucrose solenoid check valve in Hyde rolrik unit inside of the ABS and ESP module and speed sensor , a gear position sensor , an accelerator pedal sensor , a brake pedal command to the solenoid of the ECU for determining received a signal from a sensor , such as Check valves are automatic control only the operation of the common main brake the main brake when the parking brake the vehicle running condition when the vehicle by being respectively control the flow of oil pressure to the free flow and control flow in the stop state without a separate operation behavior and four-wheel this powerful hydraulic parking brake force is added to both . For example , a substantial amount of information is necessarily lost whenever the characteristics of a high-dimensional image are summarized by a single number . In this work , we introduce the Fourier radial Error Spectrum Plot ( ESP ) , which provides a novel and more nuanced assessment of error by decomposing the error into its different spatial frequency components . The usefulness of ESP is illustrated in the context of MRI reconstruction from under-sampled data . The principles of design of instructional materials for English for Specific Purposes ( ESP ) to be taught in a non-English-speaking country are outlined , and the development of materials for an Indonesian program is described . ESP materials design is tied closely to the specific subject areas of language use and the types of learners addressed . Content must address both vocabulary and linguistic structures typical of the register to be used in the subject area . Using a comprehensive set of simulations , we show that the proposed OPSA approach can improve the system utility of the ESP game significantly . In addition , we implement a quasi ESP game , called ESP Lite , which embeds three puzzle selection algorithms transparently and records the complete game trace for evaluation and further research . During a one-month experiment , we have investigated the inner properties of the three strategies in real-world GWAP systems , and verified that the OPSA scheme achieves the best system utility for the ESP game . This course is based on a novel approach to multiple-view geometry that only requires linear algebra , as opposed to more involved projective and algebraic geometry that most current methods employ . This new approach aims to make image-based modeling techniques accessible to a larger audience compared to existing ones . The presentations will be based on a recent Springer-Verlag textbook on 3D vision ( coauthored by the course organizers ) . As images/objects truly have hierarchical structures and have both visual and semantics properties , we argue that image retrieval using either semantics and image matching alone is insufficient . In this paper , we introduce a hierarchical structure for image modeling to support image retrieval using combinations of semantic expressions and visual examples at both the whole image and object levels . We give formal definitions of a multimedia query language and a system implementation based on this image modeling . Local NBNN gives a 100 times speed-up over the original NBNN on the Caltech 256 dataset . We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a common set of input features . We show that local NBNN outperforms all previous NBNN based methods and the original spatial pyramid model . Because of the unconstrained environment of scene text , traditional Optical Character Recognition ( OCR ) engines fail to achieve satisfactory results . In this paper , we propose a new technique which employs first order Histogram of Oriented Gradient ( HOG ) through a spatial pyramid . The spatial pyramid can encode the relative spatial layout of the character parts while HOG can only include the local image shape without spatial relation . In this paper we propose a simple but efficient image representation for solving the scene classification problem . Our new representation combines the benefits of spatial pyramid representation using nonlinear feature coding and latent Support Vector Machine ( LSVM ) to train a set of Latent Pyramidal Regions ( LPR ) . Each of our LPRs captures a discriminative characteristic of the scenes and is trained by searching over all possible sub-windows of the images in a latent SVM training procedure . This paper presents a method of the vehicle type classification based on spatial pyramid representation and BP neural network . We extract feature vectors of each vehicle image by using the spatial pyramid representation method . To solve the problem , we replace the binary patterns by three-value patterns , which is more efficient than LBP for such a specific classification task . Furthermore , we propose a spatial pyramid structure based on patch-level rotation invariant LTPs to capture spatial layout information . Then , we integrate PLTP-SRI feature and BoW representation into a discriminative and descriptive image representation . Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content . Here , we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy . In the experiments different sampling methods , dictionary sizes , kernel methods , and pyramid levels are examined . In this paper , we have proposed a novel dictionary learning method with the structure sparsity constraint to train a discriminative dictionary . In feature coding stage , we compute sparse codes of each patch with dictionaries learned from data and pool them to form local histogram in spatial pyramid manner . At lat , the feature vector is pipelined into a linear SVM classifier to train the model . In this paper , we study the problem of fine-grained image categorization , which is much more useful in real applications than basic image classification . Based on the most challenge dataset , CUB-200 , we combine Efficient match kernel ( EMK ) with the weighted spatial pyramid to achieve state-of-art performance . Comparison with BoW , which can also be viewed as kernel matching approach , EMK digs the relations among vocabulary bases and finds a new mapping in kernel framework . These three contributors are built through the same appearance model with different update rate . The appearance model , Pixel-wise Spatial Pyramid , employs pixel feature vectors instead of SIFT vectors , to combine several pixel characteristics . In particular , the reserved pixel feature vectors are used to create a new codebook together with the earlier codebook . We proposed the spatial pyramid configuration of convolutional neural network ( CNN ) classifier for social event detection in a static image . By applying the spatial pyramid configuration to the CNN classifier , the detail that occurs in the image can observe more accurately by the classifier . USED dataset provided by Ahmad et al. is used to evaluate our proposed method , which consists of two different image sets , EiMM , and SED dataset . Secondly , we introduce K-nearest neighbouring codewords for local features because locality is more important than sparseness . Finally , non-negativity and locality constrained operators are introduced to obtain a novel sparse coding for local features , and then in the pooling step , we use spatial pyramid division ( SPD ) and max pooling ( MP ) to represent the final images . As for image classification , multi-class linear SVM is adopted . The Pyramid Algorithm performs spatial spectral decomposition so that high frequency information can be separated from its lower frequency components . This spatial pyramid transform is integrated into a system which utilizes both spatial and temporal correlation of real-time video sequences ; the result is a robust system configuration that meets the required compression . The best acceptable image , given a specific data rate , is achieved by adaptively selecting either a spatial or a temporal algorithm on a pixel-by-pixel basis . document : Optical Flow Estimation using a Spatial Pyramid Network We learn to compute optical flow by combining a classical spatial - pyramid formulation with deep learning . This estimates large motions in a coarse - to - fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow . Approach . To deal with large motions we adopt a traditional coarse - to - fine approach using a spatial pyramid . At that top level of the pyramid , the hope is that the motions between frames are smaller than a few pixels and that , consequently , the convolutional filters can learn meaningful temporal structure . [ reference ] ) . The spatial pyramid approach enables a significant reduction in model parameters without sacrificing accuracy . There are two reasons Compared with , the improvement is similar as of using CRF to post - process the output of FCN . In addition , we also tried to follow the spatial pyramid idea to pool features from increasingly finer sub - regions and attach them to local features in the sub - regions , however , we did not observe significant improvements . We conjecture that it is because the ( empirical ) receptive field of high - level feature maps is larger than or similar as those sub - regions . These enable increasing batch sizes earlier during training , which leads to better training time . We extensively evaluate our method on Cifar-10/100 , SVHN , TinyImageNet , and ImageNet datasets , using multiple neural networks , including ResNets and smaller networks such as SqueezeNext . Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations ( up to 1\% and $ 5\times$ , respectively ) . This new network is able to match AlexNet 's accuracy on the ImageNet benchmark with 112 × fewer parameters , and one of its deeper variants is able to achieve VGG-19 accuracy with only 4.4 Million parameters , ( 31 × smaller than VGG-19 ) . SqueezeNext also achieves better top-5 classification accuracy with 1.3 × fewer parameters as compared to MobileNet , but avoids using depthwise-separable convolutions that are inefficient on some mobile processor platforms . This wide range of accuracy gives the user the ability to make speed-accuracy tradeoffs , depending on the available resources on the target hardware . Further , explores the bottleneck module and squeezenext basic block structure . The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture . The proposed architecture is further trained on the CIFAR-10 dataset from scratch . Further , explores the bottleneck module and squeezenext basic block structure . The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture . The proposed architecture is further trained on the CIFAR-10 dataset from scratch . The application of knowledge-based synthesis and compression methods ( i.e. derivation of synthetic views and lossless/lossy approximation of contents ) can be extremely beneficial in this scenario . In this paper , we present SqueezeX , a system for the synthesis/compression of XML data based on a multidimensional/classification-based interpretation of their schema and on the application of semantic and type-oriented compression techniques .
Enrollment numbers and retention rates have been decreasing in technology programs in United States of America , particularly for underrepresented minority groups . At St. Mary 's University , we started an Engineering Summer Program ( ESP ) that consists of programming and robotics sessions for high school students . The programming courses offer an opportunity for many students to write a computer program for the first time in their lives .
With the increasing application of Event Stream Processing ( ESP ) for event pattern detection , it has become important to enhance the extant ESP capabilities to deal with applications having dynamic behavior . This dissertation research explores the limitations of current ESP systems due to fixed pattern detection mechanism and discusses the motivational ideas that demand enhancements in ESP . We propose a solution called adaptive ESP that explores , learns , and updates evolving patterns in dynamic applications .
Event Stream Processing ( ESP ) Systems are currently enabling a renaissance in the data processing area as they provide results at low latency compared to the traditional MapReduce approach . Although the majority of ESP systems offer some form of fault tolerance to their users , the provided fault tolerance scheme is often not tailored to the application at hand . For example , active replication is well suited for critical applications where unresponsiveness due to a background recovery process is not acceptable .
This talk explores the relationship between CEP and ESP in the context of event processing , and in particular to an event processing reference architecture derived from earlier distributed blackboard computing models . After introducing the model , we explore where ESP and CEP " fit " by applying ESP and CEP concepts to practical use cases for event processing , drawing from signal processing , decision theory , control theory , and stochastic processing . The basis of the talk is derived from established thinking in the domain of multi-sensor data fusion , applying traditional concepts to today 's commercial view of event processing .
In order to meet the need of the social development , ESP Course will become more and more important in English teaching . The paper , from the angle of the important role of ESP Course , tries ESP classroom design method centered as skills The most important is that its design must be centered as skill , so as to establish the training goals and standards of a talent .
The new electrohydrodynamically ( EHD ) assisted ESP ( EHD ESP ) was developed to minimize reentrainment . The EHD ESP utilizes the ionic wind , combined with electrostatic force to transport the charged particles into the pocket zone attached to the collection plate effectively . The pocket zones are designed as zero electrostatic field , i.e. , no electrostatic repulsion force acting on particles , so that no reentrainment takes place .
The main aim of the research is therefore to understand current assessment practices as well as to develop a standardized benchmarking for ESP teachers . The paper proposes a model of ESP standardized assessment based on the studied reference , ESP practices used in different countries , as well as standards of assessment in general .
The present invention relates to a brake control device of the vehicle ABS unit and the composite control hydraulic parking brake is automatically controlled apparatus , normally-closed solenoid check which controls the flow of hydraulic Hyde rolrik unit of the ABS module to prevent locking of the wheel valve to be configured and relates to an apparatus and a control method for automatically controlling a hydraulic parking brake is controlled by the ECU commands to ABS and ESP devices and complex . The present invention To this end , the configuration for normal sucrose solenoid check valve in Hyde rolrik unit inside of the ABS and ESP module and speed sensor , a gear position sensor , an accelerator pedal sensor , a brake pedal command to the solenoid of the ECU for determining received a signal from a sensor , such as Check valves are automatic control only the operation of the common main brake the main brake when the parking brake the vehicle running condition when the vehicle by being respectively control the flow of oil pressure to the free flow and control flow in the stop state without a separate operation behavior and four-wheel this powerful hydraulic parking brake force is added to both .
For example , a substantial amount of information is necessarily lost whenever the characteristics of a high-dimensional image are summarized by a single number . In this work , we introduce the Fourier radial Error Spectrum Plot ( ESP ) , which provides a novel and more nuanced assessment of error by decomposing the error into its different spatial frequency components . The usefulness of ESP is illustrated in the context of MRI reconstruction from under-sampled data .
The principles of design of instructional materials for English for Specific Purposes ( ESP ) to be taught in a non-English-speaking country are outlined , and the development of materials for an Indonesian program is described . ESP materials design is tied closely to the specific subject areas of language use and the types of learners addressed . Content must address both vocabulary and linguistic structures typical of the register to be used in the subject area .
Using a comprehensive set of simulations , we show that the proposed OPSA approach can improve the system utility of the ESP game significantly . In addition , we implement a quasi ESP game , called ESP Lite , which embeds three puzzle selection algorithms transparently and records the complete game trace for evaluation and further research . During a one-month experiment , we have investigated the inner properties of the three strategies in real-world GWAP systems , and verified that the OPSA scheme achieves the best system utility for the ESP game .
This course is based on a novel approach to multiple-view geometry that only requires linear algebra , as opposed to more involved projective and algebraic geometry that most current methods employ . This new approach aims to make image-based modeling techniques accessible to a larger audience compared to existing ones . The presentations will be based on a recent Springer-Verlag textbook on 3D vision ( coauthored by the course organizers ) .
As images/objects truly have hierarchical structures and have both visual and semantics properties , we argue that image retrieval using either semantics and image matching alone is insufficient . In this paper , we introduce a hierarchical structure for image modeling to support image retrieval using combinations of semantic expressions and visual examples at both the whole image and object levels . We give formal definitions of a multimedia query language and a system implementation based on this image modeling .
Local NBNN gives a 100 times speed-up over the original NBNN on the Caltech 256 dataset . We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a common set of input features . We show that local NBNN outperforms all previous NBNN based methods and the original spatial pyramid model .
Because of the unconstrained environment of scene text , traditional Optical Character Recognition ( OCR ) engines fail to achieve satisfactory results . In this paper , we propose a new technique which employs first order Histogram of Oriented Gradient ( HOG ) through a spatial pyramid . The spatial pyramid can encode the relative spatial layout of the character parts while HOG can only include the local image shape without spatial relation .
In this paper we propose a simple but efficient image representation for solving the scene classification problem . Our new representation combines the benefits of spatial pyramid representation using nonlinear feature coding and latent Support Vector Machine ( LSVM ) to train a set of Latent Pyramidal Regions ( LPR ) . Each of our LPRs captures a discriminative characteristic of the scenes and is trained by searching over all possible sub-windows of the images in a latent SVM training procedure .
This paper presents a method of the vehicle type classification based on spatial pyramid representation and BP neural network . We extract feature vectors of each vehicle image by using the spatial pyramid representation method .
To solve the problem , we replace the binary patterns by three-value patterns , which is more efficient than LBP for such a specific classification task . Furthermore , we propose a spatial pyramid structure based on patch-level rotation invariant LTPs to capture spatial layout information . Then , we integrate PLTP-SRI feature and BoW representation into a discriminative and descriptive image representation .
Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content . Here , we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy . In the experiments different sampling methods , dictionary sizes , kernel methods , and pyramid levels are examined .
In this paper , we have proposed a novel dictionary learning method with the structure sparsity constraint to train a discriminative dictionary . In feature coding stage , we compute sparse codes of each patch with dictionaries learned from data and pool them to form local histogram in spatial pyramid manner . At lat , the feature vector is pipelined into a linear SVM classifier to train the model .
In this paper , we study the problem of fine-grained image categorization , which is much more useful in real applications than basic image classification . Based on the most challenge dataset , CUB-200 , we combine Efficient match kernel ( EMK ) with the weighted spatial pyramid to achieve state-of-art performance . Comparison with BoW , which can also be viewed as kernel matching approach , EMK digs the relations among vocabulary bases and finds a new mapping in kernel framework .
These three contributors are built through the same appearance model with different update rate . The appearance model , Pixel-wise Spatial Pyramid , employs pixel feature vectors instead of SIFT vectors , to combine several pixel characteristics . In particular , the reserved pixel feature vectors are used to create a new codebook together with the earlier codebook .
We proposed the spatial pyramid configuration of convolutional neural network ( CNN ) classifier for social event detection in a static image . By applying the spatial pyramid configuration to the CNN classifier , the detail that occurs in the image can observe more accurately by the classifier . USED dataset provided by Ahmad et al. is used to evaluate our proposed method , which consists of two different image sets , EiMM , and SED dataset .
Secondly , we introduce K-nearest neighbouring codewords for local features because locality is more important than sparseness . Finally , non-negativity and locality constrained operators are introduced to obtain a novel sparse coding for local features , and then in the pooling step , we use spatial pyramid division ( SPD ) and max pooling ( MP ) to represent the final images . As for image classification , multi-class linear SVM is adopted .
The Pyramid Algorithm performs spatial spectral decomposition so that high frequency information can be separated from its lower frequency components . This spatial pyramid transform is integrated into a system which utilizes both spatial and temporal correlation of real-time video sequences ; the result is a robust system configuration that meets the required compression . The best acceptable image , given a specific data rate , is achieved by adaptively selecting either a spatial or a temporal algorithm on a pixel-by-pixel basis .
document : Optical Flow Estimation using a Spatial Pyramid Network We learn to compute optical flow by combining a classical spatial - pyramid formulation with deep learning . This estimates large motions in a coarse - to - fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow .
Approach . To deal with large motions we adopt a traditional coarse - to - fine approach using a spatial pyramid . At that top level of the pyramid , the hope is that the motions between frames are smaller than a few pixels and that , consequently , the convolutional filters can learn meaningful temporal structure .
[ reference ] ) . The spatial pyramid approach enables a significant reduction in model parameters without sacrificing accuracy . There are two reasons
Compared with , the improvement is similar as of using CRF to post - process the output of FCN . In addition , we also tried to follow the spatial pyramid idea to pool features from increasingly finer sub - regions and attach them to local features in the sub - regions , however , we did not observe significant improvements . We conjecture that it is because the ( empirical ) receptive field of high - level feature maps is larger than or similar as those sub - regions .
These enable increasing batch sizes earlier during training , which leads to better training time . We extensively evaluate our method on Cifar-10/100 , SVHN , TinyImageNet , and ImageNet datasets , using multiple neural networks , including ResNets and smaller networks such as SqueezeNext . Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations ( up to 1\% and $ 5\times$ , respectively ) .
This new network is able to match AlexNet 's accuracy on the ImageNet benchmark with 112 × fewer parameters , and one of its deeper variants is able to achieve VGG-19 accuracy with only 4.4 Million parameters , ( 31 × smaller than VGG-19 ) . SqueezeNext also achieves better top-5 classification accuracy with 1.3 × fewer parameters as compared to MobileNet , but avoids using depthwise-separable convolutions that are inefficient on some mobile processor platforms . This wide range of accuracy gives the user the ability to make speed-accuracy tradeoffs , depending on the available resources on the target hardware .
Further , explores the bottleneck module and squeezenext basic block structure . The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture . The proposed architecture is further trained on the CIFAR-10 dataset from scratch .
Further , explores the bottleneck module and squeezenext basic block structure . The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture . The proposed architecture is further trained on the CIFAR-10 dataset from scratch .
The application of knowledge-based synthesis and compression methods ( i.e. derivation of synthetic views and lossless/lossy approximation of contents ) can be extremely beneficial in this scenario . In this paper , we present SqueezeX , a system for the synthesis/compression of XML data based on a multidimensional/classification-based interpretation of their schema and on the application of semantic and type-oriented compression techniques .
A Teaching Methodology for Introductory Programming Courses using Alice An adaptive event stream processing environment User-Constraint and Self-Adaptive Fault Tolerance for Event Stream Processing Systems Mythbusters: event stream processing versus complex event processing On Thought about ESP Design Centered as Skills Electrohydrodynamically-Assisted Electrostatic Precipitator for Collection of Low Resistive Dust IS ESP STANDARDIZED ASSESSMENT FEASIBLE The oil pressure formula parking brake automatic controller which ABS unit and compound control become and a control way. The Fourier radial error spectrum plot: A more nuanced quantitative evaluation of image reconstruction quality E.S.P. Materials Preparation in a Foreign Language Situation. The Design of Puzzle Selection Strategies for ESP-Like GWAP Systems Multiple-view geometry for image-based modeling Hierarchical Image Modeling for Object-Based Media Retrieval Local Naive Bayes Nearest Neighbor for image classification Using pyramid of histogram of oriented gradients on natural scene text recognition Latent Pyramidal Regions for Recognizing Scenes Research on Vehicle Type Classification Based on Spatial Pyramid Representation and BP Neural Network Encoding Image Features Video scene classification using spatial pyramid based features Spatial Pyramid Pooling in Structured Sparse Representation for Flame Detection Efficient match kernel in fine-grained image categorization Robust Tracking Based on Pixel-Wise Spatial Pyramid and Biased Fusion Spatial Pyramid Convolutional Neural Network for Social Event Detection in Static Image Non-negativity and locality constrained Laplacian sparse coding for image classification Real-Time Multi-Directional Data Compression Of Full Motion Video Optical Flow Estimation Using a Spatial Pyramid Network ParseNet: Looking Wider to See Better Large batch size training of neural networks with adversarial training and second-order information SqueezeNext: Hardware-Aware Neural Network Design High Performance SqueezeNext for CIFAR-10 Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment SqueezeX: synthesis and compression of XML data
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SIGMOD/PODS PhD Symposium DSN DEBS IAS ISBI SIGGRAPH Course Notes ICIP ECCV (5) ICIG (3) SIU ICIMCS ACCV (4) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv CCWC ITCC
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