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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

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Hierarchical Deep Network for Group Discovery and Multi-level Activity Recognition 11

Hierarchical Deep Network for Group Discovery and Multi-leve...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Goyal, Ashish Bhargava, Neha Chaudhuri, Subhasis Velmurugan, Rajbabu Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

We present a deep network based hierarchical framework to recognize activities performed by people collectively from videos at various levels of granularity - individual person, group and overall (or scene level). Individual person analysis, which includes person detection, tracking, pose estimation and individual activity recognition, has been studied extensively. Most of the existing work on collective activity recognition has focused on overall scene activity estimation. However, in scenarios where multiple groups perform different group activities, overall scene activity recognition in isolation paints an incomplete picture of various activities. Identifying groups and recognizing their activities is therefore important to understand a scene in it's completeness. To this end, we add an extra layer in existing methods that finds the groups (or clusters) of people present in a scene and their activities. We then utilize these group activities along with the scene context to recognize the scene activity. To discover these groups, we propose a min-max criteria within the framework to train a sub-network which learns pairwise similarity between any two individuals, used by a clustering algorithm for identification of groups. the group activity is captured by an LSTM module whereas the individual and scene activities are captured by CNN-LSTM based modules. these modules along with the grouping layer form the proposed network. We evaluate the network on a publicly available dataset to indicate the usefulness of our approach.

关键词： Clustering algorithms

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Visual odometry based omni-directional hyperlapse 6th

Visual odometry based omni-directional hyperlapse

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Rani, Prachi Jangid, Arpit Namboodiri, Vinay P. Venkatesh, K.S. Department of Elecrical Engineering Indian Institute of Technology Kanpur Kanpur India Computer Science and Engineering Department Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9789811300196

the prohibitive amounts of time required to review the large amounts of data captured by surveillance and other cameras has brought into question the very utility of large scale video logging. Yet, one recognizes that such logging and analysis are indispensable to security applications. the only way out of this paradox is to devise expedited browsing, by the creation of hyperlapse. We address the hyperlapse problem for the very challenging category of intensive egomotion which makes the hyperlapse highly jerky. We propose an economical approach for trajectory estimation based on Visual Odometry and implement cost functions to penalize pose and path deviations. Also, this is implemented on data taken by omni-directional camera, so that the viewer can opt to observe any direction while browsing. this requires many innovations, including handling the massive radial distortions and implementing scene stabilization that need to be operated upon the least distorted region of the omni view. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Cost functions

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Temporal conditional random fields: A conditional state space predictor for visual tracking

Temporal conditional random fields: A conditional state spac...

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Iranian conference on Machine vision and image processing

作者： Shafiee, M.J. Azimifar, Z. Fieguth, P. Computer Vision and Pattern Recognition Laboratory School of Electrical and Computer Engineering Shiraz University Shiraz Iran Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (纸本)9781424497065

We present a modified Temporal Conditional Random Fields framework for modeling and predicting object motion. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames). We evaluate our proposed Temporal Conditional Random Field method with real and synthetic data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results show that our proposed method estimates future target state with zero error until target dynamic changes. Our proposed modified CRF method with simple and easy to implement feature functions, can learn any target dynamic, thus, it can predict next state of target with zero error. © 2010 IEEE.

关键词： Template matching

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Balanced Two-Stage Residual Networks for image Super-Resolution 30

Balanced Two-Stage Residual Networks for Image Super-Resolut...

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30th IEEE/CVF conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Fan, Yuchen Shi, Honghui Yu, Jiahui Liu, Ding Han, Wei Yu, Haichao Wang, Zhangyang Wang, Xinchao Huang, thomas S. Univ Illinois Beckman Inst Urbana IL 61801 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

ISBN: (纸本)9781538607336

In this paper, balanced two-stage residual networks (BT SRN) are proposed for single image super-resolution. the deep residual design with constrained depth achieves the optimal balance between the accuracy and the speed for super-resolving images. the experiments show that the balanced two-stage structure, together with our lightweight two-layer PConv residual block design, achieves very promising results when considering both accuracy and speed. We evaluated our models on the New Trends in image Restoration and Enhancement workshop and challenge on image super-resolution (NTIRE SR 2017). Our final model with only 10 residual blocks ranked among the best ones in terms of not only accuracy (6th among 20 final teams) but also speed (2nd among top 6 teams in terms of accuracy).

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Performance Comparison of Object Detection algorithms with different Feature Extractors 6

Performance Comparison of Object Detection algorithms with d...

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6th International conference on Signal processing and Integrated Networks (SPIN)

作者： Gupta, Archit Puri, Raghav Verma, Mrinal Gunjyal, Siddharth Kumar, Ashish BVCOE ECE Dept New Delhi India

ISBN: (纸本)9781728113807

In this work, speed vs accuracy of different Neural Network architectures using alternate feature extractors in the field of Object Detection is being computed, thereby finding the fastest and most accurate architecture out of the lot in order to carry out Object Detection. We made use of three architectures and three extractors to build different combinations of models in order to compute mAP, which is the metric used or commenting upon accuracy. COCO data-set has been used to extract sample images and the work is implemented on TensorFlow library.

关键词： computer vision Object Detection image processing TensorFlow Faster R-CNN App Development

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A new scheme of face image encoding through wireless fading channels using WBCT and block thresholding

A new scheme of face image encoding through wireless fading ...

引用

Iranian conference on Machine vision and image processing

作者： Owjimehr, M. Yazdi, M. Asli, A. Zolghadr Department of Communications and Electronics Faculty of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9781424497065

Transmitting the face image data through wireless fading channels have been widely used for face recognition and automatic surveillance applications and many techniques can be used to do that. However, due to the noise and wireless fading channels, the perfect recovery cannot be achieved. So there are needs to use efficient techniques for image recovery and denoising. the wavelet and contourlet transforms along with some denoising schemes such as Hard thresholding to estimate the true coefficients from noisy ones have been already used. In this paper, we propose to use Wavelet-Based Contourlet Transform (WBCT) comprised with Block thresholding to more efficiently denoise and recovery transmitted face images. the simulation results show that for general face images the WBCT is quite competitive to the contourlet and wavelet transforms in the SNR sense and in visual aspects. © 2010 IEEE.

关键词： Face recognition

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Proceedings of the 2009 6th International conference on computer graphics, Imaging and Visualization: New Advances and Trends, CGIV2009

Proceedings of the 2009 6th International Conference on Comp...

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2009 6th International conference on computer graphics, Imaging and Visualization: New Advances and Trends, CGIV2009

ISBN: (纸本)9780769537894

the proceedings contain 92 papers. the topics discussed include: GPU supported patch-based tessellation for dual subdivision;ball robot and the graphics generation and its image calculation based on cad geometric model;particle importance based fluid simulation;facial expression representation using a quadratic deformation model;implementation of ant colony algorithm based on GPU;a fast method for real-time computation of approximated global illumination;expansion of communication in media art through the intelligent interaction;a motion retargeting method for topologically different characters;real-time simulation of large area nearshore wave for marine simulator;a preliminary study of human motion based on actor physiques using motion capture;providing novel and useful data for game development using usability expert evaluation and testing;digital video watermarking in the discrete wavelet transform domain;and style-sheets extraction from existing digital contents by image processing for web-based BML contents management system.

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Flow-free video object segmentation 6th

Flow-free video object segmentation

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Vora, Aditya Raman, Shanmuganathan Electrical Engineering Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India

ISBN: (纸本)9789811300196

Segmenting foreground object from a video is a challenging task because of large deformations of objects, occlusions, and background clutter. In this paper, we propose a frame-by-frame but computationally efficient approach for video object segmentation by clustering visually similar generic object segments throughout the video. Our algorithm segments object instances appearing in the video and then performs clustering in order to group visually similar segments into one cluster. Since the object that needs to be segmented appears in most part of the video, we can retrieve the foreground segments from the cluster having maximum number of segments. We then apply a track and fill approach in order to localize the object in the frames where the object segmentation framework fails to segment any object. Our algorithm performs comparably to the recent automatic methods for video object segmentation when benchmarked on DAVIS dataset while being computationally much faster. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Motion compensation

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Leveraging information from imperfect examples : Common action sequence mining from a mix of incorrect performances 11

Leveraging information from imperfect examples : Common acti...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Jain, Hiteshi Harit, Gaurav Indian Inst Technol Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781450366151

As much as good representation and theory are needed to explain human actions, so are the action videos used for learning good segmentation techniques. To accurately model complex actions such as diving, figure skating, and yoga practices, videos depicting action by human experts are required. Lack of experts in any domain leads to reduced number of videos and hence an improper learning. In this work we attempt to utilize imperfect amateur performances to get more confident representations of human action sequences. We introduce a novel Community Detection based unsupervised framework that provides mechanisms to interpret video data and address its limitations to produce better action representation. Human actions are composed of distinguishable key poses which form dense communities in graph structures. Anomalous poses performed for a longer duration can also form such dense communities but can be identified based on their rare occurrence across action videos and be rejected. Further, we propose a technique to learn the temporal order of these key poses from these imperfect videos, where the inter community links help reduce the search space of many possible pose sequences. Our framework is seen to improve the segmentation performance of complex human actions with the help of some imperfect performances. the efficacy of our approach has been illustrated over two complex action datasets - Sun Salutation and Warm-up exercise, that have been developed using random executions from amateur performers.

关键词： Unsupervised Anomaly Detection network modeling community detection human action segmentation

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OUHANDS database for hand detection and pose recognition 6

OUHANDS database for hand detection and pose recognition

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6th International conference on image processing theory, Tools and Applications (IPTA)

作者： Matilainen, Matti Sangi, Pekka Holappa, Jukka Silven, Olli Ctr Machine Vis & Signal Anal CMVS Oulu Oulu Finland

In this paper we propose a publicly available static hand pose database called OUHANDS and protocols for training and evaluating hand pose classification and hand detection methods. A comparison between the OUHANDS da... 详细信息

ISBN: (纸本)9781467389105

关键词： Database computer vision Hand pose estimation Hand detection Pattern recognition

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