The article " Information retrieval and video " deals with issues of more advanced methods of video retrieval . First chapter describes video and its components in general , chapters two , three and four deal with task of text , sound and picture in video and in retrieval respectively . Two CAC schemes are presented ( fixed-dynamic ) and different criteria for resource allocation are proposed and evaluated . The aggregation of circuit/packet switched multimedia services ( voice , video , WWW data ) is studied through detailed traffic models and the performance results are obtained using simulation . In wireless networks such as those based on IEEE 802.11 , packet losses due to fading and interference are often misinterpreted as indications of congestion by the congestion control protocol at higher layers , causing an unnecessary decrease in the data sending rate . For delay-constrained applications such as video telephony , packet losses may result in excessive artifacts or freeze in the decoded video . We propose a simple and yet effective mechanism to detect and reduce channel-caused packet losses by adjusting the retry limit parameter of the IEEE 802.11 protocol while taking into account the delay requirement of the traffic . While mobile networks have been enhanced to support a variety of mobile multimedia services such as video telephony and rich data content delivery , a new challenge is being created by the remarkable development of micro-device technologies such as micro processor-chips , sensors , and RF tags . These developments suggest the rapid emergence of the ubiquitous computing environment ; computers supporting human life without imposing any stress on the users . Our experimental results on the UAV dataset show that we can accomplish a 10,000-fold data reduction without losing significant activities of interest . We also present a summary of the emerging landscape of mobile video analytics and how it may evolve in conjunction with other distributed mobile and static sensor networks . Experimental results reveal that compared with the existing cloud computing model , mVideo reduces video data volume transmitted to the cloud nodes and power consumption by up to 99.5 % and 96.2 % , respectively . mVideo also improves the execution time by 90.0 % to optimize mobile video analytics performance . The present invention discloses a method and apparatus for video processing , belongs to the technical field of data processing . The method comprising : acquiring at least a three-dimensional image acquisition and processing video ; video analyzing be treated , to obtain at least two video images ; each of the three-dimensional image fusion separately for each video image , the video image obtained fusion ; the fused video image composite video to give a processed video . The present invention is an image acquired by each of the three-dimensional image to each video are parsed with the acquired video to be processed fusion , the fused video image composite video , the video obtained after processing , is added in the video three-dimensional image , and the processed video display three-dimensional images , and expand the application range of video processing , enriched the video display after treatment . 1 . A method of presenting clues reason for video call failure , comprising the steps of : network-side entity obtained value causes the video call failure determined by the initial switch to which the calling terminal is communicated ; ! determine the network entity side tip causes the corresponding value of reason for video call failure ; and ! are the subject of a network-side tip causes the calling terminal . ! 2 . Abstract Metro passenger detection is always a significant task and a bottleneck in metro video surveillance system . Much recent research has demonstrated that Convolutional Neural Network ( CNN ) is more powerful than other machine learning algorithms in numerous computer vision tasks . The proposed approach has been designed to deal with arbitrary television genres , making it suitablefor various applications . To achieve this goal , the main steps of a content-based video retrieval system are addressed in thiswork , namely : video segmentation , key-frame extraction , content-based video indexing and the video retrieval operation itself . Video segmentation is addressed as a typical TV broadcast structuring problem , which consists in automatically determiningthe boundaries of each broadcasted program ( like movies , news , among others ) and inter-program ( for instance , commercials).Specifically , to segment the videos , Electronic Program Guide ( EPG ) metadata is combined with the detection of two specialcues , namely , audio cuts ( silence ) and dark monochrome frames . Experiments with video sequences demonstrate that the proposed algorithm leads to good quality reconstructions in the presence of noise in the gradients . This makes the proposed algorithm valuable for gradient based video processing applications and a video editing example is included to illustrate this . The digital video broadcast - satellite , second generation ( DVB-S2 ) protocol has been developed and standardized as an evolution of the digital video broadcast - satellite ( DVB-S ) waveform for European satellite broadcasting by the European Telecommunications Standards Institute ( ETSI ) , and is a candidate for use in US military systems that employ DVB-S. This paper describes the evolutionary efficiencies of DVB-S2 over DVB-S , increases in performance , capacity , and effectiveness , and draw some conclusions about how military broadcast systems could benefit from this waveform in the future . The global broadcast service , a DVB-based , one-way , high-capacity , Department of Defense ( DoD ) satellite system that is used for transfer of classified and unclassified video , imagery , and other information , is presented in a case study to illustrate the potential impacts of migrating a DVB-S based system to the DVB-S2 standard . The paper focuses on the technology and waveform efficiency of the DVB-S2 standard in military satellite communication ( MILSATCOM ) applications and provides a clear description of the status and authority of this evolving standard Indexing video by content represents an important research area that one can find in the field of intelligent search of videos . Visual characteristics such as color are one of the most relevant components used to achieve this task . section : Introduction Video face recognition has caught more and more attention from the community in recent years [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Compared to image - based face recognition , more information of the subjects can be exploited from the input videos , which naturally incorporate faces of the same subject in varying poses and illumination conditions . section : Aggregation module Consider the video face recognition task on n pairs of video face data ( , where X i is a face video sequence or a image set with varying image number Image and video SR are long - standing challenges of signal processing . SR aims at recovering a HR image or video from its LR version , and finds direct applications ranging from medical imaging to satellite imaging , as well as facilitating tasks such as face recognition . The reconstruction of HR data from a LR input is however a highly ill - posed problem that requires additional constraints to be solved . subsection : Motivation and contributions [ b ] 0.32 [ b ] 0.32 [ b ] 0.32 Existing solutions for HD video SR have not been able to effectively exploit temporal correlations while performing in real - time . On the one hand , ESPCN leverages sub - pixel convolution for a very efficient operation , but its naive extension to videos treating frames independently fails to exploit inter - frame redundancies and does not enforce a temporally consistent result . To that end we use the graph partitioning formulation that has been used for people tracking and pose estimation in the past , but has not been shown to enable articulated people tracking . To facilitate efficient inference in video we resort to fast inference methods based on local combinatorial optimization and aim for a sparse model that keeps the number of connections between variables to a minimum . As we demonstrate , in combination with feed - forward reasoning for joint - to - person association this allows us to achieve substantial speed - ups compared to state - of - the - art while maintaining the same level of accuracy . Generative adversarial networks ( GANs ) aim to model the natural image distribution by forcing the generated samples to be indistinguishable from natural images . GANs enable a wide variety of applications such as image generation , representation learning , image manipulation , object detection , and video applications . Various coarse - to - fine schemes have been proposed to synthesize larger images ( e.g. ) in an unconditional setting . However , a major drawback of CornerNet is its inference speed . It achieves an average precision ( AP ) of 42.2 % on COCO at an inference cost of 1.147s per image , which is too slow for video applications that require real - time or interactive rates . Although one can easily speed up inference by reducing the number of pixels processed ( e.g. by reducing the number of scales of processing or the image resolution ) , this can cause a large accuracy drop . In this paper , we solve the VQA problem using a multimodal memory network architecture that applies a spatial attention mechanism over an input image guided by an input text question . The neural attention mechanism has been widely used in different areas of computer vision and natural language processing , see for example the attention models in image captioning , video description generation , machine translation and machine reading systems . Most methods use the soft attention mechanism first proposed in , which adds a layer to the network that predicts soft weights and uses them to compute a weighted combination of the items in memory . To report validation score , we assign random uniform probability of success ( ) for correct recognition to the samples on which face detection did not succeed . subsection : Baseline Model for Video - based Recognition Problem For comparing the benefits of using SPDNet over existing methods , we use kernel based PLS that used covariance matrices as features in baseline method . We study that second - order networks are better able to capture facial landmark distortions . Similarly , covariance matrix computed from image feature vectors were used as input to SPDNet for video - based facial expression recognition problem . We were able to obtain state - of - the - art results on image - based facial expression recognition problems on the SFEW 2.0 and RAF datasets . Video denoising is an important feature of many modem video encoding architectures since it can considerably enhance coding efficiency while at the same time improving objective and subjective quality . In this paper , a new scheme for video denoising based on spatio-temporal filtering is presented , which can be used as a preprocessing stage within a video encoder . This paper aims at breaking new ground in modeling and estimation of recording noise of surveillance video for further development of new techniques to restore video images . In order to tackle the video denoising problem with non-stationary image contents and various noise sources , a critical task is estimation of varieties of noise in video signals . The estimation is based on a new general integrated surveillance video noise model ( GISVNM ) , which integrates all typical realistic noise models , including signal independent noise model and signal dependent model to model behaviors of Poisson , additive and impulse noises . With the increasing interest in the deployment of wireless multimedia sensor networks ( WMSN ) , new challenges have arisen with the complexity and high noise level of the monitoring environment . Given that the noise severely impairs the quality and visibility of video images perceived by sensors , video image denoising naturally becomes the key to ensure the validity and reliability of the WMSN video monitoring . In this paper , the sparse denoising algorithm via clustering-based sparse representation is proposed . We show how it can be efficiently implemented to take full advantage of a multicore architecture . The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising , where a significant speedup is achieved . Mostexisting filters forvideo denoising arespatio-temporal filters whichoperate ondataover3dimensions . Thispaper presents apurely linear temporal filter withmultihypothesis motion compensation (MC).Compared tothespatio-temporal filters , theproposed oneneeds muchfewer inputs andmuch simpler operation . This paper introduces an anisotropic decomposition structure of a recently introduced 3-D dual-tree discrete wavelet transform ( DDWT ) , and explores the applications for video denoising and coding . The 3-D DDWT is an attractive video representation because it isolates motion along different directions in separate subbands , and thus leads to sparse video decompositions . Frequency domain methods , which operate on the Fourier transform of an image . Video Denoising Video denoising is the process of removing noise from a video signal . It refers to the recovery of a digital video that has been contaminated by noise . The concrete scheme comprises the following steps : taking a current processing pixel point as a center , and carrying out intra-frame filtering on all pixel points within an N*N range in a current processing frame to obtain a second pixel block ; taking a first processing pixel point as a center , and carrying out intra-frame filtering on all pixel points within an N*N range in a reference frame to obtain a fourth pixel block ; acquiring a weighted value of time-domain filtering of the current processing pixel point according to the second pixel block and the fourth pixel block ; acquiring a time-domain filtering component of the current processing pixel point according to the weighted value ; carrying out intra-frame filtering on the current processing pixel point to obtain a spatial filtering component ; and updating a gray value of the current processing pixel point according to the time-domain filtering component , the spatial filtering component and the weighted value . The method and the device are applied in a video image denoising process . As a remedy , out - of - the - box optimization can be applied to the network predictions as a postprocessing operation , for example , optical flow estimates can be refined with a variational approach . In some cases , this refinement can be approximated by neural networks : Chen & Pock formulate reaction diffusion model as a CNN and apply it to image denoising , deblocking and superresolution . Recently , it has been shown that similar refinement can be obtained by stacking several convolutional networks on top of each other . In this paper , a novel technique is introduced to address the video alignment task which is one of the hot topics in computer vision . Specifically , we aim at finding the best possible correspondences between two overlapping videos without the restrictions imposed by previous techniques . The novelty of this work is that the video alignment problem is solved by drawing an analogy between it and the machine comprehension ( MC ) task in natural language processing ( NLP ) . Simply , MC seeks to give the best answer to a question about a given paragraph . The proposed model has been trained using 10k of collected video pairs from ” YouTube ” . The initial experimental results show that it is a promising solution for the video alignment task when compared to the state of the art techniques . Video frame alignment with the original source signal is required in a variety of applications , for instance non-blind watermark detection . Robustness to temporal cropping , scene removal , frame-rate changes and other non-linear temporal distortions are required . Camera shakes cause video motion blur . Video deblurring has been studied for years , and however , there are still unresolved problems , such as video frame alignment , frame selection , and frame ambiguity evaluation . We propose a video deblurring algorithm based on the motion vector and an encoder – decoder network . To deal with this challenge , we propose a novel global SfM pipeline where camera registration is formulated as a well-posed similarity averaging problem solved robustly with L1 optimization . What ’s more , the novel pipeline makes the filtering of noisy relative poses simple and effective , which can further improve the robustness of global SfM. We show the effectiveness of our global SfM system by applying it into the video alignment problem which aims to find per-pixel correspondences between two video sequences in both spatial and temporal dimensions . Guided by the 3D information from global SfM , the proposed video registration method can align videos taken at different times with substantially different appearances , in the presence of moving objects and moving cameras with slightly different trajectories . To this end , we present a multi-homography-based video stitching method which stitches videos from a wide-angle camera and a fisheye camera . The method consists of video image alignment , seam cutting , and image blending . We directly align the wide-angle video image and the fisheye video image based on the multi-homography alignment without calibration , distortion correction , and unwarping procedures . To achieve this , we build upon recent advances in deep and recurrent neural networks which provide efficient vectorial and contextual representations of the modalities to be aligned . Our label-based method for automatic alignment of video with narrative sentences proposes a highly efficient alignment technique that does not require heavy pre-processing steps , while enabling a fine level of granularity in the alignment result . Then , we develop an end-to-end differentiable neural architecture that addresses the limitations of the two-stage solutions by optimizing the similarity metric specifically for the alignment task while supporting one-to-one , oneto-many , skipping unmatched elements , and non-monotonic alignment . A 140 Mb/s video codec , ( coder/decoder ) is described . Special emphasis is given to data compression , frequency accommodation , and video alignment aspects . A scheme based in a ping-pong structure of elastic buffers and PLL , which explores the particular characteristics of the video signal , is presented.<<ETX > > Frame images are acquired from discrete camera stations while videos are taken from continuous trajectories . The proposed method includes five major parts : ( 1 ) camera calibration , ( 2 ) video conversion and alignment , ( 3 ) orientation modelling , ( 4 ) dense matching , and ( 5 ) evaluation . As the action cameras usually have large FOV in wide viewing mode , camera calibration plays an important role to calibrate the effect of lens distortion before image matching . Slightly different viewing angles of both lines of a staggered array can result in a variable sampling pattern on the ground because of the attitude fluctuations , satellite movement , terrain topography , PSM steering and small variations in the angular placement of the CCD lines ( from the pre-launch values ) in the focal plane . Non-accounting of this variable sampling value during the video data alignment will introduce deterioration of image quality and geometric discontinuity of features . The stagger parameters can be computed by the reconstruction of the viewing geometry with a calibrated camera geometry model and a public domain DEM . Multiview relations and constraints developed in Computer Vision recently can provide more elegant solutions to this problem . In this paper , we provide two solutions for the video frame alignment problem using two view and three view constraints . We present solutions to this problem for the case when the videos are taken using affine cameras and for general projective cameras . Fiducials imprinted on laser beams are used to perform video image based alignment of the beams in the National Ignition Facility ( NIF ) of Lawrence Livermore National Laboratory . In any laser beam alignment operation , a beam needs to be aligned to a reference location . This paper presents a new metric access method -- Slim2-tree -- which combines information from multiple modalities within a single index structure for video retrieval . Experimental studies on a large real dataset show the video similarity search performance of the proposed technique . Additionally , we present experiments comparing our method against state-of-the-art of multimodal solutions . Searching relevant visual information based on content features in large databases is an interesting and changeling topic that has drawn lots of attention from both the research community and industry . This paper gives an overview of our investigations on effective and efficient video similarity search . We briefly introduce some novel techniques developed for two specific tasks studied in this PhD project : video retrieval in a large collection of segmented video clips , and video subsequence identification from a long unsegmented stream . Summary form only given . The proliferation of video content makes video similarity detection an indispensable tool in video management , searching , and navigation . We propose a new algorithm that targets detecting given video clips from TV programs . The scene tree is shown to capture some semantic information as well as to provide a construct for hierarchical browsing of compressed videos . Finally , we build a new model for video similarity based on global as well as local motion associated with each node in the scene tree . To this end , we propose new approaches to camera motion and object motion estimation . The scene tree is shown to capture some semantic information as well as to provide a construct for hierarchical browsing of compressed videos . Finally , we build a new model for video similarity based on global as well as local motion associated with each node in the scene tree . To this end , we propose new approaches to camera motion and object motion estimation . Video Spatiotemporal Feature was defined to represent the content of video . The proposed Grads matching algorithm and improved Dynamic Time Warping algorithm(DTW ) were introduced to video similarity matching . Experimental database includeed 80 advertising videos and 200 re-editing videos . There is still no satisfying search method for scalable fast similarity search in large video database . In order to solve two challenging problems : video similarity measure and fast search method in large database , a novel efficient video similarity search strategy for video-on-demand systems is proposed in this paper . A compact video signature was computed according to image histogram and spatial-temporal features of video . By generating a video link graph , the system can conceptually groups the videos that contains same objects together for searching and browsing . Both the chosen measures of video object similarity and the video data mining technologies are discussed here and included in the related software demonstrator . In addition , the software offers a query-by-image-example video search capability to jump into the video graph at a certain point to begin browsing the archive . The study reported in this paper shows that the mosaic-based similarity analysis can be expected to yield a more reliable result , whereas the key frame-based similarity analysis could be potentially applied to a wider range of applications . The weighted non-linear feature combination is shown to yield better results than a single feature for video similarity analysis . The coefficient of determination is shown to be a better criterion than the mean absolute error in similarity matching analysis . The framework works incrementally and is suitable for videos of data streams in sequential order . Extensive experiments demonstrate that the framework can obtain promising results on video similarity comparison , and also with a comparably computational speedup . Building a search engine to identify such similar content in the World-Wide Web requires : 1 ) robust video similarity measurements ; 2 ) fast similarity search techniques on large databases ; and 3 ) intuitive organization of search results . In a previous paper , we proposed a randomized technique called the video signature ( ViSig ) method for video similarity measurement . In this paper , we focus on the remaining two issues by proposing a feature extraction scheme for fast similarity search , and a clustering algorithm for identification of similar clusters . We propose a method of similarity searching for video objects using 2-phase filtering . The method proposed in this paper provides efficient and effective video similarity searching from a set of sub-sequences of video objects given a clip video . It can also find video objects that match to the clip exactly or approximately .
The article " Information retrieval and video " deals with issues of more advanced methods of video retrieval . First chapter describes video and its components in general , chapters two , three and four deal with task of text , sound and picture in video and in retrieval respectively .
Two CAC schemes are presented ( fixed-dynamic ) and different criteria for resource allocation are proposed and evaluated . The aggregation of circuit/packet switched multimedia services ( voice , video , WWW data ) is studied through detailed traffic models and the performance results are obtained using simulation .
In wireless networks such as those based on IEEE 802.11 , packet losses due to fading and interference are often misinterpreted as indications of congestion by the congestion control protocol at higher layers , causing an unnecessary decrease in the data sending rate . For delay-constrained applications such as video telephony , packet losses may result in excessive artifacts or freeze in the decoded video . We propose a simple and yet effective mechanism to detect and reduce channel-caused packet losses by adjusting the retry limit parameter of the IEEE 802.11 protocol while taking into account the delay requirement of the traffic .
While mobile networks have been enhanced to support a variety of mobile multimedia services such as video telephony and rich data content delivery , a new challenge is being created by the remarkable development of micro-device technologies such as micro processor-chips , sensors , and RF tags . These developments suggest the rapid emergence of the ubiquitous computing environment ; computers supporting human life without imposing any stress on the users .
Our experimental results on the UAV dataset show that we can accomplish a 10,000-fold data reduction without losing significant activities of interest . We also present a summary of the emerging landscape of mobile video analytics and how it may evolve in conjunction with other distributed mobile and static sensor networks .
Experimental results reveal that compared with the existing cloud computing model , mVideo reduces video data volume transmitted to the cloud nodes and power consumption by up to 99.5 % and 96.2 % , respectively . mVideo also improves the execution time by 90.0 % to optimize mobile video analytics performance .
The present invention discloses a method and apparatus for video processing , belongs to the technical field of data processing . The method comprising : acquiring at least a three-dimensional image acquisition and processing video ; video analyzing be treated , to obtain at least two video images ; each of the three-dimensional image fusion separately for each video image , the video image obtained fusion ; the fused video image composite video to give a processed video . The present invention is an image acquired by each of the three-dimensional image to each video are parsed with the acquired video to be processed fusion , the fused video image composite video , the video obtained after processing , is added in the video three-dimensional image , and the processed video display three-dimensional images , and expand the application range of video processing , enriched the video display after treatment .
1 . A method of presenting clues reason for video call failure , comprising the steps of : network-side entity obtained value causes the video call failure determined by the initial switch to which the calling terminal is communicated ; ! determine the network entity side tip causes the corresponding value of reason for video call failure ; and ! are the subject of a network-side tip causes the calling terminal . ! 2 .
Abstract Metro passenger detection is always a significant task and a bottleneck in metro video surveillance system . Much recent research has demonstrated that Convolutional Neural Network ( CNN ) is more powerful than other machine learning algorithms in numerous computer vision tasks .
The proposed approach has been designed to deal with arbitrary television genres , making it suitablefor various applications . To achieve this goal , the main steps of a content-based video retrieval system are addressed in thiswork , namely : video segmentation , key-frame extraction , content-based video indexing and the video retrieval operation itself . Video segmentation is addressed as a typical TV broadcast structuring problem , which consists in automatically determiningthe boundaries of each broadcasted program ( like movies , news , among others ) and inter-program ( for instance , commercials).Specifically , to segment the videos , Electronic Program Guide ( EPG ) metadata is combined with the detection of two specialcues , namely , audio cuts ( silence ) and dark monochrome frames .
Experiments with video sequences demonstrate that the proposed algorithm leads to good quality reconstructions in the presence of noise in the gradients . This makes the proposed algorithm valuable for gradient based video processing applications and a video editing example is included to illustrate this .
The digital video broadcast - satellite , second generation ( DVB-S2 ) protocol has been developed and standardized as an evolution of the digital video broadcast - satellite ( DVB-S ) waveform for European satellite broadcasting by the European Telecommunications Standards Institute ( ETSI ) , and is a candidate for use in US military systems that employ DVB-S. This paper describes the evolutionary efficiencies of DVB-S2 over DVB-S , increases in performance , capacity , and effectiveness , and draw some conclusions about how military broadcast systems could benefit from this waveform in the future . The global broadcast service , a DVB-based , one-way , high-capacity , Department of Defense ( DoD ) satellite system that is used for transfer of classified and unclassified video , imagery , and other information , is presented in a case study to illustrate the potential impacts of migrating a DVB-S based system to the DVB-S2 standard . The paper focuses on the technology and waveform efficiency of the DVB-S2 standard in military satellite communication ( MILSATCOM ) applications and provides a clear description of the status and authority of this evolving standard
Indexing video by content represents an important research area that one can find in the field of intelligent search of videos . Visual characteristics such as color are one of the most relevant components used to achieve this task .
section : Introduction Video face recognition has caught more and more attention from the community in recent years [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Compared to image - based face recognition , more information of the subjects can be exploited from the input videos , which naturally incorporate faces of the same subject in varying poses and illumination conditions .
section : Aggregation module Consider the video face recognition task on n pairs of video face data ( , where X i is a face video sequence or a image set with varying image number
Image and video SR are long - standing challenges of signal processing . SR aims at recovering a HR image or video from its LR version , and finds direct applications ranging from medical imaging to satellite imaging , as well as facilitating tasks such as face recognition . The reconstruction of HR data from a LR input is however a highly ill - posed problem that requires additional constraints to be solved .
subsection : Motivation and contributions [ b ] 0.32 [ b ] 0.32 [ b ] 0.32 Existing solutions for HD video SR have not been able to effectively exploit temporal correlations while performing in real - time . On the one hand , ESPCN leverages sub - pixel convolution for a very efficient operation , but its naive extension to videos treating frames independently fails to exploit inter - frame redundancies and does not enforce a temporally consistent result .
To that end we use the graph partitioning formulation that has been used for people tracking and pose estimation in the past , but has not been shown to enable articulated people tracking . To facilitate efficient inference in video we resort to fast inference methods based on local combinatorial optimization and aim for a sparse model that keeps the number of connections between variables to a minimum . As we demonstrate , in combination with feed - forward reasoning for joint - to - person association this allows us to achieve substantial speed - ups compared to state - of - the - art while maintaining the same level of accuracy .
Generative adversarial networks ( GANs ) aim to model the natural image distribution by forcing the generated samples to be indistinguishable from natural images . GANs enable a wide variety of applications such as image generation , representation learning , image manipulation , object detection , and video applications . Various coarse - to - fine schemes have been proposed to synthesize larger images ( e.g. ) in an unconditional setting .
However , a major drawback of CornerNet is its inference speed . It achieves an average precision ( AP ) of 42.2 % on COCO at an inference cost of 1.147s per image , which is too slow for video applications that require real - time or interactive rates . Although one can easily speed up inference by reducing the number of pixels processed ( e.g. by reducing the number of scales of processing or the image resolution ) , this can cause a large accuracy drop .
In this paper , we solve the VQA problem using a multimodal memory network architecture that applies a spatial attention mechanism over an input image guided by an input text question . The neural attention mechanism has been widely used in different areas of computer vision and natural language processing , see for example the attention models in image captioning , video description generation , machine translation and machine reading systems . Most methods use the soft attention mechanism first proposed in , which adds a layer to the network that predicts soft weights and uses them to compute a weighted combination of the items in memory .
To report validation score , we assign random uniform probability of success ( ) for correct recognition to the samples on which face detection did not succeed . subsection : Baseline Model for Video - based Recognition Problem For comparing the benefits of using SPDNet over existing methods , we use kernel based PLS that used covariance matrices as features in baseline method .
We study that second - order networks are better able to capture facial landmark distortions . Similarly , covariance matrix computed from image feature vectors were used as input to SPDNet for video - based facial expression recognition problem . We were able to obtain state - of - the - art results on image - based facial expression recognition problems on the SFEW 2.0 and RAF datasets .
Video denoising is an important feature of many modem video encoding architectures since it can considerably enhance coding efficiency while at the same time improving objective and subjective quality . In this paper , a new scheme for video denoising based on spatio-temporal filtering is presented , which can be used as a preprocessing stage within a video encoder .
This paper aims at breaking new ground in modeling and estimation of recording noise of surveillance video for further development of new techniques to restore video images . In order to tackle the video denoising problem with non-stationary image contents and various noise sources , a critical task is estimation of varieties of noise in video signals . The estimation is based on a new general integrated surveillance video noise model ( GISVNM ) , which integrates all typical realistic noise models , including signal independent noise model and signal dependent model to model behaviors of Poisson , additive and impulse noises .
With the increasing interest in the deployment of wireless multimedia sensor networks ( WMSN ) , new challenges have arisen with the complexity and high noise level of the monitoring environment . Given that the noise severely impairs the quality and visibility of video images perceived by sensors , video image denoising naturally becomes the key to ensure the validity and reliability of the WMSN video monitoring . In this paper , the sparse denoising algorithm via clustering-based sparse representation is proposed .
We show how it can be efficiently implemented to take full advantage of a multicore architecture . The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising , where a significant speedup is achieved .
Mostexisting filters forvideo denoising arespatio-temporal filters whichoperate ondataover3dimensions . Thispaper presents apurely linear temporal filter withmultihypothesis motion compensation (MC).Compared tothespatio-temporal filters , theproposed oneneeds muchfewer inputs andmuch simpler operation .
This paper introduces an anisotropic decomposition structure of a recently introduced 3-D dual-tree discrete wavelet transform ( DDWT ) , and explores the applications for video denoising and coding . The 3-D DDWT is an attractive video representation because it isolates motion along different directions in separate subbands , and thus leads to sparse video decompositions .
Frequency domain methods , which operate on the Fourier transform of an image . Video Denoising Video denoising is the process of removing noise from a video signal . It refers to the recovery of a digital video that has been contaminated by noise .
The concrete scheme comprises the following steps : taking a current processing pixel point as a center , and carrying out intra-frame filtering on all pixel points within an N*N range in a current processing frame to obtain a second pixel block ; taking a first processing pixel point as a center , and carrying out intra-frame filtering on all pixel points within an N*N range in a reference frame to obtain a fourth pixel block ; acquiring a weighted value of time-domain filtering of the current processing pixel point according to the second pixel block and the fourth pixel block ; acquiring a time-domain filtering component of the current processing pixel point according to the weighted value ; carrying out intra-frame filtering on the current processing pixel point to obtain a spatial filtering component ; and updating a gray value of the current processing pixel point according to the time-domain filtering component , the spatial filtering component and the weighted value . The method and the device are applied in a video image denoising process .
As a remedy , out - of - the - box optimization can be applied to the network predictions as a postprocessing operation , for example , optical flow estimates can be refined with a variational approach . In some cases , this refinement can be approximated by neural networks : Chen & Pock formulate reaction diffusion model as a CNN and apply it to image denoising , deblocking and superresolution . Recently , it has been shown that similar refinement can be obtained by stacking several convolutional networks on top of each other .
In this paper , a novel technique is introduced to address the video alignment task which is one of the hot topics in computer vision . Specifically , we aim at finding the best possible correspondences between two overlapping videos without the restrictions imposed by previous techniques . The novelty of this work is that the video alignment problem is solved by drawing an analogy between it and the machine comprehension ( MC ) task in natural language processing ( NLP ) . Simply , MC seeks to give the best answer to a question about a given paragraph .
The proposed model has been trained using 10k of collected video pairs from ” YouTube ” . The initial experimental results show that it is a promising solution for the video alignment task when compared to the state of the art techniques .
Video frame alignment with the original source signal is required in a variety of applications , for instance non-blind watermark detection . Robustness to temporal cropping , scene removal , frame-rate changes and other non-linear temporal distortions are required .
Camera shakes cause video motion blur . Video deblurring has been studied for years , and however , there are still unresolved problems , such as video frame alignment , frame selection , and frame ambiguity evaluation . We propose a video deblurring algorithm based on the motion vector and an encoder – decoder network .
To deal with this challenge , we propose a novel global SfM pipeline where camera registration is formulated as a well-posed similarity averaging problem solved robustly with L1 optimization . What ’s more , the novel pipeline makes the filtering of noisy relative poses simple and effective , which can further improve the robustness of global SfM. We show the effectiveness of our global SfM system by applying it into the video alignment problem which aims to find per-pixel correspondences between two video sequences in both spatial and temporal dimensions . Guided by the 3D information from global SfM , the proposed video registration method can align videos taken at different times with substantially different appearances , in the presence of moving objects and moving cameras with slightly different trajectories .
To this end , we present a multi-homography-based video stitching method which stitches videos from a wide-angle camera and a fisheye camera . The method consists of video image alignment , seam cutting , and image blending . We directly align the wide-angle video image and the fisheye video image based on the multi-homography alignment without calibration , distortion correction , and unwarping procedures .
To achieve this , we build upon recent advances in deep and recurrent neural networks which provide efficient vectorial and contextual representations of the modalities to be aligned . Our label-based method for automatic alignment of video with narrative sentences proposes a highly efficient alignment technique that does not require heavy pre-processing steps , while enabling a fine level of granularity in the alignment result . Then , we develop an end-to-end differentiable neural architecture that addresses the limitations of the two-stage solutions by optimizing the similarity metric specifically for the alignment task while supporting one-to-one , oneto-many , skipping unmatched elements , and non-monotonic alignment .
A 140 Mb/s video codec , ( coder/decoder ) is described . Special emphasis is given to data compression , frequency accommodation , and video alignment aspects . A scheme based in a ping-pong structure of elastic buffers and PLL , which explores the particular characteristics of the video signal , is presented.<<ETX > >
Frame images are acquired from discrete camera stations while videos are taken from continuous trajectories . The proposed method includes five major parts : ( 1 ) camera calibration , ( 2 ) video conversion and alignment , ( 3 ) orientation modelling , ( 4 ) dense matching , and ( 5 ) evaluation . As the action cameras usually have large FOV in wide viewing mode , camera calibration plays an important role to calibrate the effect of lens distortion before image matching .
Slightly different viewing angles of both lines of a staggered array can result in a variable sampling pattern on the ground because of the attitude fluctuations , satellite movement , terrain topography , PSM steering and small variations in the angular placement of the CCD lines ( from the pre-launch values ) in the focal plane . Non-accounting of this variable sampling value during the video data alignment will introduce deterioration of image quality and geometric discontinuity of features . The stagger parameters can be computed by the reconstruction of the viewing geometry with a calibrated camera geometry model and a public domain DEM .
Multiview relations and constraints developed in Computer Vision recently can provide more elegant solutions to this problem . In this paper , we provide two solutions for the video frame alignment problem using two view and three view constraints . We present solutions to this problem for the case when the videos are taken using affine cameras and for general projective cameras .
Fiducials imprinted on laser beams are used to perform video image based alignment of the beams in the National Ignition Facility ( NIF ) of Lawrence Livermore National Laboratory . In any laser beam alignment operation , a beam needs to be aligned to a reference location .
This paper presents a new metric access method -- Slim2-tree -- which combines information from multiple modalities within a single index structure for video retrieval . Experimental studies on a large real dataset show the video similarity search performance of the proposed technique . Additionally , we present experiments comparing our method against state-of-the-art of multimodal solutions .
Searching relevant visual information based on content features in large databases is an interesting and changeling topic that has drawn lots of attention from both the research community and industry . This paper gives an overview of our investigations on effective and efficient video similarity search . We briefly introduce some novel techniques developed for two specific tasks studied in this PhD project : video retrieval in a large collection of segmented video clips , and video subsequence identification from a long unsegmented stream .
Summary form only given . The proliferation of video content makes video similarity detection an indispensable tool in video management , searching , and navigation . We propose a new algorithm that targets detecting given video clips from TV programs .
The scene tree is shown to capture some semantic information as well as to provide a construct for hierarchical browsing of compressed videos . Finally , we build a new model for video similarity based on global as well as local motion associated with each node in the scene tree . To this end , we propose new approaches to camera motion and object motion estimation .
The scene tree is shown to capture some semantic information as well as to provide a construct for hierarchical browsing of compressed videos . Finally , we build a new model for video similarity based on global as well as local motion associated with each node in the scene tree . To this end , we propose new approaches to camera motion and object motion estimation .
Video Spatiotemporal Feature was defined to represent the content of video . The proposed Grads matching algorithm and improved Dynamic Time Warping algorithm(DTW ) were introduced to video similarity matching . Experimental database includeed 80 advertising videos and 200 re-editing videos .
There is still no satisfying search method for scalable fast similarity search in large video database . In order to solve two challenging problems : video similarity measure and fast search method in large database , a novel efficient video similarity search strategy for video-on-demand systems is proposed in this paper . A compact video signature was computed according to image histogram and spatial-temporal features of video .
By generating a video link graph , the system can conceptually groups the videos that contains same objects together for searching and browsing . Both the chosen measures of video object similarity and the video data mining technologies are discussed here and included in the related software demonstrator . In addition , the software offers a query-by-image-example video search capability to jump into the video graph at a certain point to begin browsing the archive .
The study reported in this paper shows that the mosaic-based similarity analysis can be expected to yield a more reliable result , whereas the key frame-based similarity analysis could be potentially applied to a wider range of applications . The weighted non-linear feature combination is shown to yield better results than a single feature for video similarity analysis . The coefficient of determination is shown to be a better criterion than the mean absolute error in similarity matching analysis .
The framework works incrementally and is suitable for videos of data streams in sequential order . Extensive experiments demonstrate that the framework can obtain promising results on video similarity comparison , and also with a comparably computational speedup .
Building a search engine to identify such similar content in the World-Wide Web requires : 1 ) robust video similarity measurements ; 2 ) fast similarity search techniques on large databases ; and 3 ) intuitive organization of search results . In a previous paper , we proposed a randomized technique called the video signature ( ViSig ) method for video similarity measurement . In this paper , we focus on the remaining two issues by proposing a feature extraction scheme for fast similarity search , and a clustering algorithm for identification of similar clusters .
We propose a method of similarity searching for video objects using 2-phase filtering . The method proposed in this paper provides efficient and effective video similarity searching from a set of sub-sequences of video objects given a clip video . It can also find video objects that match to the clip exactly or approximately .
Vyhledávání a video Call admission policies for UMTS Congestion-Aware MAC Layer Adaptation to Improve Video Telephony over Wi-Fi An Extension of 4G Mobile Networks towards the Ubiquitous Real Space Moving camera analytics: Emerging scenarios, challenges, and applications mVideo: Edge Computing Based Mobile Video Processing Systems A method and apparatus for video processing A method and apparatus for presenting a video call failure reasons tips A fast occluded passenger detector based on MetroNet and Tiny MetroNet A unified approach to content-based indexing and retrieval of digital videos from television archives Video editing in the gradient domain using a wavelet based 3-D reconstruction algorithm and an iterative Poisson solver DVB-S2 benefits for military broadcast systems Indexing Video by the Content Neural Aggregation Network for Video Face Recognition Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation ArtTrack: Articulated Multi-Person Tracking in the Wild High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs CornerNet-Lite: Efficient Keypoint Based Object Detection Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Covariance Pooling for Facial Expression Recognition Adaptive spatio-temporal filtering for video denoising Modeling of surveillance video noise Clustering-Based Image Sparse Denoising in Wireless Multimedia Sensor Networks A Multicore Convex Optimization Algorithm with Applications to Video Restoration A MULTIHYPOTHESISMOTION-COMPENSATEDTEMPORAL FILTERFOR VIDEO DENOISING A new structure of 3D dual-tree discrete wavelet transforms and applications to video denoising and coding 3-D Sherlet Transform for Biomedical Applications Method and device for denoising video image FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks Bi-Directional Attention Flow for Video Alignment Temporal Video Registration for Watermark Detection A Video Deblurring Algorithm Based on Motion Vector and An Encorder-Decoder Network Global Structure-from-Motion and Its Application Stitching Videos from a Fisheye Lens Camera and a Wide-Angle Lens Camera for Telepresence Robots Automatic Alignment Methods for Visual and Textual Data with Narrative Content Video, sampling and network synchronization in a TV codec VIDEO-BASED POINT CLOUD GENERATION USING MULTIPLE ACTION CAMERAS Pre-processing considerations of IRS-P6 LISS-4 imagery Video frame alignment in multiple views Multi-object feature detection and error correction for NIF automatic optical alignment An efficient access method for multimodal video retrieval Challenges and techniques for effective and efficient similarity search in large video databases An effective video matching algorithm for adaptive video fingerprints A motion-based scene tree for browsing and retrieval of compressed videos A motion based scene tree for browsing and retrieval of compressed videos Video Similarity Matching Algorithm Based on Spatiotemporal Feature An efficient video similarity search strategy for video-on-demand systems Browsing Linked Video Archives of WWW Video Similarity Analysis of Video Sequences Using an Artificial Neural Network Incremetal Spatio-Temporal Feature Extraction and Retrieval for Large Video Database Fast similarity search and clustering of video sequences on the world-wide-web New efficient video similarity searching method with 2-phase filtering
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PACRIM 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) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ICIP ICIP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) VISIGRAPP (5: VISAPP) ICASSP (2) ICIP (3) WebMedia MMDB MMM (2) ISCAS
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