We present SlowFast networks for video recognition . Our model involves ( i ) a Slow pathway , operating at low frame rate , to capture spatial semantics , and ( ii ) a Fast pathway , operating at high frame rate , to capture motion at fine temporal resolution . Recently , Convolutional Neural Networks ( ConvNets ) have shown promising performances in many computer vision tasks , especially image-based recognition . How to effectively use ConvNets for video-based recognition is still an open problem . In this paper , we propose a compact , effective yet simple method to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images , referred to as Joint Trajectory Maps ( JTM ) , and ConvNets are adopted to exploit the discriminative features for real-time human action recognition . Near-infrared frame differencing improves the robustness to the outdoor illumination conditions . And we introduce the video-based recognition with pose clustering for pose invariant face recognition . We have collected large video dataset of drivers under various extreme outdoor illumination conditions . The curve road overspeed early-warning device comprises a vehicle speed sensor and a digital camera , wherein the vehicle speed sensor is mounted on a motor vehicle , and the digital camera is mounted right ahead of a cab ; the vehicle speed sensor and the digital camera are respectively connected with a singlechip ; and the singlechip is further connected with a buzzer . According to the curve road overspeed early-warning device based on the video image recognition , when the vehicle is in an overspeeding driving state , the singlechip controls the buzzer to alarm , so that a driver is reminded to slow down speed to pass through a curve road , and the occurrence of accidents arising from overspeed when the motor vehicle passes through the curve road in the overspeeding state can be effectively reduced . Video image recognition has been extensively studied with rapid progress recently . However , most methods focus on short-term rather than long-term ( contextual ) video recognition . It was found that information could be presented as an aggregate of quantitative ( physical ) and qualitative ( structural ) components to be considered together . It turned out that such full information theory could be efficiently used as the guiding theory at modeling of video-information recognition , perception and understanding . These hierarchical processes are solving the intellectual tasks step-by-step for formation of the corresponding video-information evaluation and also represent a strong interactions-measurements video-information ’s ensuring adequacy of these assessments . Group behavior analysis based on computer vision , has been a quite active research field , in which video population statistics is regarded as an important issue . This paper aims to discuss the recent development of video number recognition in detail and analyze the recent progress situation of number recognition from three perspectives — feature-based , area-based and image-matching trackings . Through comparison among studies of diffirent types of recognition algorithm ideas , problems to be solved at present in this research field are analyzed at length and an improved method of future number recognition is provide as well . Background subtraction algorithms are critical to many video recognition/analysis systems and have been studied for decades . Most of the algorithms assume that the camera is fixed . We investigate how to combine different modalities efficiently in the CNN framework . We adapt various fusion methods , which are previously employed on video recognition problem , to the brain tumor segmentation problem , and we investigate their efficiency in terms of memory and performance . Our experiments , which are performed on BRATS dataset , lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems . This paper introduces a video intelligent surveillance system , and presents the structure of the system , adaptability for bandwidth and the method for intelligent surveillance . It describes the key technologies , such as WLAN , efficient video coding arithmetic based on SPIHT(set partition in hierarchical trees),intelligent alarm aroused by video-recognizing , and so on , gives out the testing results and important parameter of performance by realizing the system . The potential advantage of SDEs over HMMS is the use of continuous state dynamics . We present encouraging results for a video sequence recognition task in which SDE models provided excellent performance when compared to hidden Markov models . In order to filter out these videos before been received , we have to recognize them in the transmission step on the satellite . In this paper , we focus on the video signal recognition on satellite and proposed a effective system for video recognition and surveillance . Neural Aggregation Network for Video Face Recognition section : Abstract This paper presents a Neural Aggregation Network ( NAN ) for video face recognition . The network takes a face video or face image set of a person with a variable number of face images as its input , and produces a compact , fixed - dimension feature representation for recognition . Recently , researchers have shown increasing interests in exploring structured layers to enhance representation capability of networks . One particular kind of structured layer is concerned with global covariance pooling after the last convolution layer , which has shown impressive improvement over the classical first - order pooling , successfully used in FGVC , visual question answering and video action recognition . Very recent works have demonstrated that matrix square root normalization of global covariance pooling plays a key role in achieving state - of - the - art performance in both large - scale visual recognition and challenging FGVC . It turns out that the minimum condition numbers occur at a=/spl radic/2/3 and a=2-/spl radic/2/3 and not at a=1 as we might have expected . The theory is verified using the example of video deinterlacing . The question " to interlace or not to interlace " divides the television and the personal computer communities . A proper answer requires a common understanding of what is possible nowadays in deinterlacing video signals . This paper outlines the most relevant proposals , ranging front simple linear methods to advanced motion-compensated algorithms , and provides a relative performance comparison for 12 of these methods . An Improved Algorithm For Deinterlacing Video Streams by Christopher Weiss Dr. Evangelos Y fantis . Examination Committee Chair Professor o f Deinterlacing is a basic requirement for video scaning format conversions . This paper has analysed the interlaced video signal and deinterlacing based on signal processing theory , and has discussed the condition for perfectly restoring the original signal and the method to design a de-interlacing filter . At the end of the paper , an applied filter has been presented . Appropriate weighting terms are introduced to take into account the interlaced scanning format . Two operational modes are derived : a single iteration mode for real time video de-interlacing , and a multiple iteration mode for enhanced still image generation . The problem is formulated as a semi-definite program which can be solved efficiently by existing techniques . The usefulness and correctness of the consistent resampling theory is demonstrated by its application to the video de-interlacing problem , image processing , the demodulation of ultra-wideband communication signals and mobile channel detection . The results show that the proposed resampling system has many advantages over existing approaches , including lower computational and time complexities , more accurate prediction of system performances , as well as robustness against noise . The 12 papers in this special issue on recent advances in video processing for consumer displays fall into four areas : image enhancement and spatial resolution ; video rate conversion ; video deinterlacing and compression ; and human vision and depth maps . To improve conventional deinterlacing issues , we adopt type-1 fuzzy set concepts to design a weight-measuring approach . We demonstrate that the fuzzy ensemble approach model is well suited to image processing and provide case studies in the video-deinterlacing field . In our proposed method , five fuzzy membership functions ( MFs ) of linguistic variable-based fuzzy logic filters are derived from the type-1 ( a.k.a . Recent Advances.- Fuzzy Measures : Collectors of Entropies.- Some Problems with Entropy Measures for the Atanassov Intuitionistic Fuzzy Sets.- Twofold Extensions of Fuzzy Datalog.- Combs Method Used in an Intuitionistic Fuzzy Logic Application.- Intuitionistic Fuzzy Spatial Relationships in Mobile GIS Environment.- A Two-Dimensional Entropic Approach to Intuitionistic Fuzzy Contrast Enhancement.- Intuitionistic Fuzzy Histogram Hyperbolization for Color Images.- Special Session on Soft Computing in Image Processing.- Computer Vision and Pattern Recognition in Homeland Security Applications.- A Genetic Algorithm Based on Eigen Fuzzy Sets for Image Reconstruction.- Fuzzy Metrics Application in Video Spatial Deinterlacing.- Fuzzy Directional-Distance Vector Filter.- Color Texture Segmentation with Local Fuzzy Patterns and Spatially Constrained Fuzzy C-Means.- A Flexible System for the Retrieval of Shapes in Binary Images.- Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity.- Dilation and Erosion of Spatial Bipolar Fuzzy Sets.- About the Embedding of Color Uncertainty in CBIR Systems.- Evolutionary Cellular Automata Based-Approach for Edge Detection.- Special Session Third International Workshop on Cross-Language Information Processing ( CLIP 2007).- The Multidisciplinary Facets of Research on Humour.- Multi-attribute Text Classification Using the Fuzzy Borda Method and Semantic Grades.- Approximate String Matching Techniques for Effective CLIR Among Indian Languages.- Using Translation Heuristics to Improve a Multimodal and Multilingual Information Retrieval System.- Explicit Human Feedback for Interactive Video Object Segmentation . This paper investigates how to exploit human feedback for interactive object segmentation in videos . In particular , we present an interactive video object segmentation approach where humans can contribute by either explicitly clicking on objects of interest in videos or implicitly while looking at video sequences . When combined with the proposed structured sampling procedure , we prove that the method provides stable and accurate reconstruction of the original signal . Finally , we present numerical experiments that illustrate our theoretical results and , as an example , show how to combine these methods for interactive object segmentation in an image using superpixels . Both steps are repeated alternately , within interactive response times , until the segmentation refinement of the final frame is accepted by the user . Extensive experiments demonstrate FOMTrace 's ability for tracing objects in comparison with state-of-the-art approaches for interactive video segmentation , supervised , and unsupervised object tracking . We describe a solution using efficient heuristics to make multi-label video segmentation interactive . As our solution generalizes typical binary segmentation tasks , while also improving efficiency in multi-label tasks , our work shows the promise of multicuts for interactive video segmentation . To get a realistic model , segmentation is one of the most important procedures in the whole process . The interactive segmentation of objects on the basis of ellipsoidal regions in RGB-space greatly enhances the realism of 3D reconstructions of the Visible Human datasets . Furthermore , this article describes a double-ellipsoids method in RGB-space for the specific segmentation of muscles originally from our mass of work . This paper presents the improvement of the watershed from propagated markers , a generic method to interactive segmentation of objects in image sequences , by the inclusion of a temporal gradient to the segmentation framework . Segmentation is done by applying the watershed from markers to a gradient image extracted from the temporal gradient sequence and using markers provided by the binding of markers heuristics . We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest . Whereas conventional interactive pipelines take the user 's initialization as a starting point , we show the value in the system taking the lead even in initialization . We present a deep learning method for the interactive video object segmentation . Our method is built upon two core operations , interaction and propagation , and each operation is conducted by Convolutional Neural Networks . The second simplifies the energy minimization problem by “ reducing ” the number of variables in the energy function . We demonstrate a substantial improvement in the running time of various labelling problems such as , interactive image and video segmentation , object recognition , stereo matching . In the second part of the thesis we explore the use of natural image statistics for the single view reconstruction problem , where the task is to recover a theatre-stage representation ( containing planar surfaces and their geometrical relationships to each other ) from a single 2D image . The graph cut based approach has become very popular for interactive segmentation of the object of interest from the background . One of the most important and yet largely unsolved issues in the graph cut segmentation framework is parameter selection . This includes size and type of the dataset used as well as the evaluation metrics . Furthermore , in order to let the reader make use of the survey , we list eight possible application scenarios , including an own section for interactive video scene segmentation , and identify those algorithms that can be applied to them . At the end , current challenges for scene segmentation algorithms are discussed . Video grounding aims to temporally localize an action in an untrimmed video referred to by a query in natural language , which plays an important role in fine-grained video understanding . Given temporal proposals of limited granularity , the task is challenging that it requires fusing multi-modal features from questions and videos effectively , and localizing the referred action accurately . It essentially aims at reconstructing a video , as if it would be without foreground objects , e.g. cars or people . What differentiates video grounding from known background estimation methods is that the camera follows unconstrained motion so that background undergoes ongoing changes . We build on video matching aspects since more videos contribute to the reconstruction . For multipoint video conferencing over a wide-area network , a video bridge is often used to combine the coded video signals from multiple participants into a single video for display . The video combining can be done in the coded-domain by concatenating coded video bitstreams , or in the pixel-domain by transcoding . In this paper we compare the video quality using the two approaches for a multipoint video conference over a symmetrical wide-area network such as Integrated Service Digital Network ( ISDN ) . Automatic endoscope video analysis is an essential function for medical robot and computer-aided diagnosis system . However , the performance of these video analysis algorithms are often degraded by low quality endoscope images under the uncontrolled environment , where some of them are difficult even for human ourselves for analysis , such as over-saturated by reflection , too dark or obscure . We address the problem of indexing video sequences according to the events they depict . While a number of different approaches have been proposed in order to describe events , none is sufficiently generic and computationally efficient to be applied to event-based retrieval of video sequences within large databases . In this paper , we propose a novel index of video sequences which aims at describing their dynamic content . The video streaming business has grown substantially during the last decades . To optimize the user experience in video streaming , it is important to know how the user satisfaction relates to the te ... Our method combines fast single image based pose estimation methods with the temporal correlation of poses between frames . We collect a new high frame rate upper body pose dataset that better reflects practical scenarios calling for fast online video pose estimation . When evaluated on this dataset and the VideoPose2 benchmark dataset , CDBN-MODEC achieves improvements in both performance and running efficiency over several state-of-art online video pose estimation methods . It generates blocking effects in video due to compressing video using block-based DCT and includes de-blocking filter to reduce blocking effect . Therefore , the filter makes the video over-smoothing and the quality of it is reduced . In this paper , we propose a improved de-blocking filter to solve the demerit . Holography aims to record and regenerate volume filling light fields to reproduce ghost-like 3-D images that are optically indistinguishable from their physical 3-D originals . Digital holographic video displays are pixelated devices on which digital holograms can be written at video rates . Spatial light modulators ( SLMs ) are used for such purposes in practice ; even though it is desirable to have SLMs that can modulate both the phase and amplitude of the incident light at each pixel , usually amplitude-only or phase-only SLMs are available . Frame duplication is a typical method of tampering surveillance video which replacing a video clips with the repetitive copies of one single frame to pretend a static scene in order to conceal some important events . The forensics algorithms based on the image content can hardly identify the forged clip from the video sequence of the static scene because of the high similarity of the frames in a surveillance video . PURPOSE : A digital recorder/player and its method are provided to be compatible with various multimedia products , to edit as a screen unit and to perform a fast searching function by coding and decoding through compression coding and decoding schemes of MPEG-2(Moving Picture Experts Group ) . CONSTITUTION : The first decoder(301 ) supplies coded video data by compressively coding inputted video data as a screen unit . The second decoder(311 ) supplies coded audio data by coding inputted audio data . This eliminates the need to perform most of the SR operation in the far larger HR resolution . For this purpose , we propose an efficient sub - pixel convolution layer to learn the upscaling operation for image and video super - resolution . The advantages of these contributions are two fold : In our network , upscaling is handled by the last layer of the network . section : Future work A reasonable assumption when processing video information is that most of a scene ’s content is shared by neighbouring video frames . In addition to the fact that the architecture fits naturally to the latent hierarchical structures in many temporal data , the multiscale approach provides the following advantages that resolve some inherent problems of standard RNNs : ( a ) computational efficiency obtained by updating the high - level layers less frequently , ( b ) efficiently delivering long - term dependencies with fewer updates at the high - level layers , which mitigates the vanishing gradient problem , ( c ) flexible resource allocation ( e.g. , more hidden units to the higher layers that focus on modelling long - term dependencies and less hidden units to the lower layers which are in charge of learning short - term dependencies ) . In addition , the learned latent hierarchical structures can provide useful information to other downstream tasks such Published as a conference paper at ICLR 2017 as module structures in computer program learning , sub - task structures in hierarchical reinforcement learning , and story segments in video understanding . There have been various approaches to implementing the multiscale RNNs . In these works , a color image is available as a starting point for synthesis . Video synthesis can be accomplished by advecting the content of this initial image . In our setting , photographic scene appearance must be synthesized without such initialization . section : Introduction Tracking is a fundamental task in any video application requiring some degree of reasoning about objects of interest , as it allows to establish object correspondances between frames [ reference ] . It finds use in a wide range of scenarios such as automatic surveillance , vehicle navigation , video labelling , human - computer interaction and activity recognition . A solution was proposed by Shi et al . with an efficient sub - pixel convolution network ( ESPCN ) , where an upscaling operation directly mapping from LR to HR space is learnt by the network . This technique reduces runtime by an order of magnitude and enables real - time video SR by independently processing frames with a single frame model . Similar solutions to improve efficiency have also been proposed based on transposed convolutions . devises a deep learning network to consider both image feature and basic feature of display adverting . develops a two - stage deep learning framework for YouTube video recommendation . section : Conclusions Our NAN is trained with a standard classification or verification loss without any extra supervision signal , and we found that it automatically learns to advocate high - quality face images while repelling low - quality ones such as blurred , occluded and improperly exposed faces . The experiments on IJB - A , YouTube Face , Celebrity - 1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state - of - the - art accuracy . section : Introduction Such algorithms are computationally expensive , often taking several seconds or minutes per frame . This has limited the application of optical flow in robotics , embedded systems , and video analysis . Using a GPU can speed up traditional methods but with reduced accuracy .
We present SlowFast networks for video recognition . Our model involves ( i ) a Slow pathway , operating at low frame rate , to capture spatial semantics , and ( ii ) a Fast pathway , operating at high frame rate , to capture motion at fine temporal resolution .
Recently , Convolutional Neural Networks ( ConvNets ) have shown promising performances in many computer vision tasks , especially image-based recognition . How to effectively use ConvNets for video-based recognition is still an open problem . In this paper , we propose a compact , effective yet simple method to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images , referred to as Joint Trajectory Maps ( JTM ) , and ConvNets are adopted to exploit the discriminative features for real-time human action recognition .
Near-infrared frame differencing improves the robustness to the outdoor illumination conditions . And we introduce the video-based recognition with pose clustering for pose invariant face recognition . We have collected large video dataset of drivers under various extreme outdoor illumination conditions .
The curve road overspeed early-warning device comprises a vehicle speed sensor and a digital camera , wherein the vehicle speed sensor is mounted on a motor vehicle , and the digital camera is mounted right ahead of a cab ; the vehicle speed sensor and the digital camera are respectively connected with a singlechip ; and the singlechip is further connected with a buzzer . According to the curve road overspeed early-warning device based on the video image recognition , when the vehicle is in an overspeeding driving state , the singlechip controls the buzzer to alarm , so that a driver is reminded to slow down speed to pass through a curve road , and the occurrence of accidents arising from overspeed when the motor vehicle passes through the curve road in the overspeeding state can be effectively reduced .
Video image recognition has been extensively studied with rapid progress recently . However , most methods focus on short-term rather than long-term ( contextual ) video recognition .
It was found that information could be presented as an aggregate of quantitative ( physical ) and qualitative ( structural ) components to be considered together . It turned out that such full information theory could be efficiently used as the guiding theory at modeling of video-information recognition , perception and understanding . These hierarchical processes are solving the intellectual tasks step-by-step for formation of the corresponding video-information evaluation and also represent a strong interactions-measurements video-information ’s ensuring adequacy of these assessments .
Group behavior analysis based on computer vision , has been a quite active research field , in which video population statistics is regarded as an important issue . This paper aims to discuss the recent development of video number recognition in detail and analyze the recent progress situation of number recognition from three perspectives — feature-based , area-based and image-matching trackings . Through comparison among studies of diffirent types of recognition algorithm ideas , problems to be solved at present in this research field are analyzed at length and an improved method of future number recognition is provide as well .
Background subtraction algorithms are critical to many video recognition/analysis systems and have been studied for decades . Most of the algorithms assume that the camera is fixed .
We investigate how to combine different modalities efficiently in the CNN framework . We adapt various fusion methods , which are previously employed on video recognition problem , to the brain tumor segmentation problem , and we investigate their efficiency in terms of memory and performance . Our experiments , which are performed on BRATS dataset , lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems .
This paper introduces a video intelligent surveillance system , and presents the structure of the system , adaptability for bandwidth and the method for intelligent surveillance . It describes the key technologies , such as WLAN , efficient video coding arithmetic based on SPIHT(set partition in hierarchical trees),intelligent alarm aroused by video-recognizing , and so on , gives out the testing results and important parameter of performance by realizing the system .
The potential advantage of SDEs over HMMS is the use of continuous state dynamics . We present encouraging results for a video sequence recognition task in which SDE models provided excellent performance when compared to hidden Markov models .
In order to filter out these videos before been received , we have to recognize them in the transmission step on the satellite . In this paper , we focus on the video signal recognition on satellite and proposed a effective system for video recognition and surveillance .
Neural Aggregation Network for Video Face Recognition section : Abstract This paper presents a Neural Aggregation Network ( NAN ) for video face recognition . The network takes a face video or face image set of a person with a variable number of face images as its input , and produces a compact , fixed - dimension feature representation for recognition .
Recently , researchers have shown increasing interests in exploring structured layers to enhance representation capability of networks . One particular kind of structured layer is concerned with global covariance pooling after the last convolution layer , which has shown impressive improvement over the classical first - order pooling , successfully used in FGVC , visual question answering and video action recognition . Very recent works have demonstrated that matrix square root normalization of global covariance pooling plays a key role in achieving state - of - the - art performance in both large - scale visual recognition and challenging FGVC .
It turns out that the minimum condition numbers occur at a=/spl radic/2/3 and a=2-/spl radic/2/3 and not at a=1 as we might have expected . The theory is verified using the example of video deinterlacing .
The question " to interlace or not to interlace " divides the television and the personal computer communities . A proper answer requires a common understanding of what is possible nowadays in deinterlacing video signals . This paper outlines the most relevant proposals , ranging front simple linear methods to advanced motion-compensated algorithms , and provides a relative performance comparison for 12 of these methods .
An Improved Algorithm For Deinterlacing Video Streams by Christopher Weiss Dr. Evangelos Y fantis . Examination Committee Chair Professor o f
Deinterlacing is a basic requirement for video scaning format conversions . This paper has analysed the interlaced video signal and deinterlacing based on signal processing theory , and has discussed the condition for perfectly restoring the original signal and the method to design a de-interlacing filter . At the end of the paper , an applied filter has been presented .
Appropriate weighting terms are introduced to take into account the interlaced scanning format . Two operational modes are derived : a single iteration mode for real time video de-interlacing , and a multiple iteration mode for enhanced still image generation .
The problem is formulated as a semi-definite program which can be solved efficiently by existing techniques . The usefulness and correctness of the consistent resampling theory is demonstrated by its application to the video de-interlacing problem , image processing , the demodulation of ultra-wideband communication signals and mobile channel detection . The results show that the proposed resampling system has many advantages over existing approaches , including lower computational and time complexities , more accurate prediction of system performances , as well as robustness against noise .
The 12 papers in this special issue on recent advances in video processing for consumer displays fall into four areas : image enhancement and spatial resolution ; video rate conversion ; video deinterlacing and compression ; and human vision and depth maps .
To improve conventional deinterlacing issues , we adopt type-1 fuzzy set concepts to design a weight-measuring approach . We demonstrate that the fuzzy ensemble approach model is well suited to image processing and provide case studies in the video-deinterlacing field . In our proposed method , five fuzzy membership functions ( MFs ) of linguistic variable-based fuzzy logic filters are derived from the type-1 ( a.k.a .
Recent Advances.- Fuzzy Measures : Collectors of Entropies.- Some Problems with Entropy Measures for the Atanassov Intuitionistic Fuzzy Sets.- Twofold Extensions of Fuzzy Datalog.- Combs Method Used in an Intuitionistic Fuzzy Logic Application.- Intuitionistic Fuzzy Spatial Relationships in Mobile GIS Environment.- A Two-Dimensional Entropic Approach to Intuitionistic Fuzzy Contrast Enhancement.- Intuitionistic Fuzzy Histogram Hyperbolization for Color Images.- Special Session on Soft Computing in Image Processing.- Computer Vision and Pattern Recognition in Homeland Security Applications.- A Genetic Algorithm Based on Eigen Fuzzy Sets for Image Reconstruction.- Fuzzy Metrics Application in Video Spatial Deinterlacing.- Fuzzy Directional-Distance Vector Filter.- Color Texture Segmentation with Local Fuzzy Patterns and Spatially Constrained Fuzzy C-Means.- A Flexible System for the Retrieval of Shapes in Binary Images.- Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity.- Dilation and Erosion of Spatial Bipolar Fuzzy Sets.- About the Embedding of Color Uncertainty in CBIR Systems.- Evolutionary Cellular Automata Based-Approach for Edge Detection.- Special Session Third International Workshop on Cross-Language Information Processing ( CLIP 2007).- The Multidisciplinary Facets of Research on Humour.- Multi-attribute Text Classification Using the Fuzzy Borda Method and Semantic Grades.- Approximate String Matching Techniques for Effective CLIR Among Indian Languages.- Using Translation Heuristics to Improve a Multimodal and Multilingual Information Retrieval System.-
Explicit Human Feedback for Interactive Video Object Segmentation . This paper investigates how to exploit human feedback for interactive object segmentation in videos . In particular , we present an interactive video object segmentation approach where humans can contribute by either explicitly clicking on objects of interest in videos or implicitly while looking at video sequences .
When combined with the proposed structured sampling procedure , we prove that the method provides stable and accurate reconstruction of the original signal . Finally , we present numerical experiments that illustrate our theoretical results and , as an example , show how to combine these methods for interactive object segmentation in an image using superpixels .
Both steps are repeated alternately , within interactive response times , until the segmentation refinement of the final frame is accepted by the user . Extensive experiments demonstrate FOMTrace 's ability for tracing objects in comparison with state-of-the-art approaches for interactive video segmentation , supervised , and unsupervised object tracking .
We describe a solution using efficient heuristics to make multi-label video segmentation interactive . As our solution generalizes typical binary segmentation tasks , while also improving efficiency in multi-label tasks , our work shows the promise of multicuts for interactive video segmentation .
To get a realistic model , segmentation is one of the most important procedures in the whole process . The interactive segmentation of objects on the basis of ellipsoidal regions in RGB-space greatly enhances the realism of 3D reconstructions of the Visible Human datasets . Furthermore , this article describes a double-ellipsoids method in RGB-space for the specific segmentation of muscles originally from our mass of work .
This paper presents the improvement of the watershed from propagated markers , a generic method to interactive segmentation of objects in image sequences , by the inclusion of a temporal gradient to the segmentation framework . Segmentation is done by applying the watershed from markers to a gradient image extracted from the temporal gradient sequence and using markers provided by the binding of markers heuristics .
We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest . Whereas conventional interactive pipelines take the user 's initialization as a starting point , we show the value in the system taking the lead even in initialization .
We present a deep learning method for the interactive video object segmentation . Our method is built upon two core operations , interaction and propagation , and each operation is conducted by Convolutional Neural Networks .
The second simplifies the energy minimization problem by “ reducing ” the number of variables in the energy function . We demonstrate a substantial improvement in the running time of various labelling problems such as , interactive image and video segmentation , object recognition , stereo matching . In the second part of the thesis we explore the use of natural image statistics for the single view reconstruction problem , where the task is to recover a theatre-stage representation ( containing planar surfaces and their geometrical relationships to each other ) from a single 2D image .
The graph cut based approach has become very popular for interactive segmentation of the object of interest from the background . One of the most important and yet largely unsolved issues in the graph cut segmentation framework is parameter selection .
This includes size and type of the dataset used as well as the evaluation metrics . Furthermore , in order to let the reader make use of the survey , we list eight possible application scenarios , including an own section for interactive video scene segmentation , and identify those algorithms that can be applied to them . At the end , current challenges for scene segmentation algorithms are discussed .
Video grounding aims to temporally localize an action in an untrimmed video referred to by a query in natural language , which plays an important role in fine-grained video understanding . Given temporal proposals of limited granularity , the task is challenging that it requires fusing multi-modal features from questions and videos effectively , and localizing the referred action accurately .
It essentially aims at reconstructing a video , as if it would be without foreground objects , e.g. cars or people . What differentiates video grounding from known background estimation methods is that the camera follows unconstrained motion so that background undergoes ongoing changes . We build on video matching aspects since more videos contribute to the reconstruction .
For multipoint video conferencing over a wide-area network , a video bridge is often used to combine the coded video signals from multiple participants into a single video for display . The video combining can be done in the coded-domain by concatenating coded video bitstreams , or in the pixel-domain by transcoding . In this paper we compare the video quality using the two approaches for a multipoint video conference over a symmetrical wide-area network such as Integrated Service Digital Network ( ISDN ) .
Automatic endoscope video analysis is an essential function for medical robot and computer-aided diagnosis system . However , the performance of these video analysis algorithms are often degraded by low quality endoscope images under the uncontrolled environment , where some of them are difficult even for human ourselves for analysis , such as over-saturated by reflection , too dark or obscure .
We address the problem of indexing video sequences according to the events they depict . While a number of different approaches have been proposed in order to describe events , none is sufficiently generic and computationally efficient to be applied to event-based retrieval of video sequences within large databases . In this paper , we propose a novel index of video sequences which aims at describing their dynamic content .
The video streaming business has grown substantially during the last decades . To optimize the user experience in video streaming , it is important to know how the user satisfaction relates to the te ...
Our method combines fast single image based pose estimation methods with the temporal correlation of poses between frames . We collect a new high frame rate upper body pose dataset that better reflects practical scenarios calling for fast online video pose estimation . When evaluated on this dataset and the VideoPose2 benchmark dataset , CDBN-MODEC achieves improvements in both performance and running efficiency over several state-of-art online video pose estimation methods .
It generates blocking effects in video due to compressing video using block-based DCT and includes de-blocking filter to reduce blocking effect . Therefore , the filter makes the video over-smoothing and the quality of it is reduced . In this paper , we propose a improved de-blocking filter to solve the demerit .
Holography aims to record and regenerate volume filling light fields to reproduce ghost-like 3-D images that are optically indistinguishable from their physical 3-D originals . Digital holographic video displays are pixelated devices on which digital holograms can be written at video rates . Spatial light modulators ( SLMs ) are used for such purposes in practice ; even though it is desirable to have SLMs that can modulate both the phase and amplitude of the incident light at each pixel , usually amplitude-only or phase-only SLMs are available .
Frame duplication is a typical method of tampering surveillance video which replacing a video clips with the repetitive copies of one single frame to pretend a static scene in order to conceal some important events . The forensics algorithms based on the image content can hardly identify the forged clip from the video sequence of the static scene because of the high similarity of the frames in a surveillance video .
PURPOSE : A digital recorder/player and its method are provided to be compatible with various multimedia products , to edit as a screen unit and to perform a fast searching function by coding and decoding through compression coding and decoding schemes of MPEG-2(Moving Picture Experts Group ) . CONSTITUTION : The first decoder(301 ) supplies coded video data by compressively coding inputted video data as a screen unit . The second decoder(311 ) supplies coded audio data by coding inputted audio data .
This eliminates the need to perform most of the SR operation in the far larger HR resolution . For this purpose , we propose an efficient sub - pixel convolution layer to learn the upscaling operation for image and video super - resolution . The advantages of these contributions are two fold : In our network , upscaling is handled by the last layer of the network .
section : Future work A reasonable assumption when processing video information is that most of a scene ’s content is shared by neighbouring video frames .
In addition to the fact that the architecture fits naturally to the latent hierarchical structures in many temporal data , the multiscale approach provides the following advantages that resolve some inherent problems of standard RNNs : ( a ) computational efficiency obtained by updating the high - level layers less frequently , ( b ) efficiently delivering long - term dependencies with fewer updates at the high - level layers , which mitigates the vanishing gradient problem , ( c ) flexible resource allocation ( e.g. , more hidden units to the higher layers that focus on modelling long - term dependencies and less hidden units to the lower layers which are in charge of learning short - term dependencies ) . In addition , the learned latent hierarchical structures can provide useful information to other downstream tasks such Published as a conference paper at ICLR 2017 as module structures in computer program learning , sub - task structures in hierarchical reinforcement learning , and story segments in video understanding . There have been various approaches to implementing the multiscale RNNs .
In these works , a color image is available as a starting point for synthesis . Video synthesis can be accomplished by advecting the content of this initial image . In our setting , photographic scene appearance must be synthesized without such initialization .
section : Introduction Tracking is a fundamental task in any video application requiring some degree of reasoning about objects of interest , as it allows to establish object correspondances between frames [ reference ] . It finds use in a wide range of scenarios such as automatic surveillance , vehicle navigation , video labelling , human - computer interaction and activity recognition .
A solution was proposed by Shi et al . with an efficient sub - pixel convolution network ( ESPCN ) , where an upscaling operation directly mapping from LR to HR space is learnt by the network . This technique reduces runtime by an order of magnitude and enables real - time video SR by independently processing frames with a single frame model . Similar solutions to improve efficiency have also been proposed based on transposed convolutions .
devises a deep learning network to consider both image feature and basic feature of display adverting . develops a two - stage deep learning framework for YouTube video recommendation . section : Conclusions
Our NAN is trained with a standard classification or verification loss without any extra supervision signal , and we found that it automatically learns to advocate high - quality face images while repelling low - quality ones such as blurred , occluded and improperly exposed faces . The experiments on IJB - A , YouTube Face , Celebrity - 1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state - of - the - art accuracy . section : Introduction
Such algorithms are computationally expensive , often taking several seconds or minutes per frame . This has limited the application of optical flow in robotics , embedded systems , and video analysis . Using a GPU can speed up traditional methods but with reduced accuracy .
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