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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是471-480 订阅

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Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos 30

Spatio-Temporal Self-Organizing Map Deep Network for Dynamic...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Du, Yang Yuan, Chunfeng Li, Bing Hu, Weiming Maybank, Stephen Univ Chinese Acad Sci Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Tech Natl Lab Pattern RecognitInst Automat Beijing Peoples R China Birkbeck Coll London England

ISBN: (纸本)9781538604571

In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. this paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. the proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.

关键词： Self-organizing feature maps Object detection Videos Dynamics Bayes methods Kernel computer vision

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Identifying First-person Camera Wearers in third-person Videos 30

Identifying First-person Camera Wearers in Third-person Vide...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fan, Chenyou Lee, Jangwon Xu, Mingze Singh, Krishna Kumar Lee, Yong Jae Crandall, David J. Ryoo, Michael S. Indiana Univ Bloomington Bloomington IN 47405 USA Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781538604571

We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene. To do this, we need to establish person-level correspondences across first-and third-person videos, which is challenging because the camera wearer is not visible from his/her own egocentric video, preventing the use of direct feature matching. In this paper, we propose a new semi-Siamese Convolutional Neural Network architecture to address this novel challenge. We formulate the problem as learning a joint embedding space for first-and third-person videos that considers both spatial-and motion-domain cues. A new triplet loss function is designed to minimize the distance between correct first-and third-person matches while maximizing the distance between incorrect ones. this end-to-end approach performs significantly better than several baselines, in part by learning the first-and third-person features optimized for matching jointly with the distance measure itself.

关键词： Videotapes Cameras BASELINE NETWORK STRUCTURE Activity recognition Environments neural net architecture

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Noise Robust Depth from Focus using a Ring Difference Filter 30

Noise Robust Depth from Focus using a Ring Difference Filter

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Surh, Jaeheung Jeon, Hae-Gon Park, Yunwon Im, Sunghoon Ha, Hyowon Kweon, In So Korea Adv Inst Sci & Technol Robot & Comp Vis Lab Daejeon South Korea

ISBN: (纸本)9781538604571

Depth from focus (DfF) is a method of estimating depth of a scene by using the information acquired through the change of the focus of a camera. Within the framework of DfF, the focus measure (FM) forms the foundation on which the accuracy of the output is determined. With the result from the FM, the role of a DfF pipeline is to determine and recalculate unreliable measurements while enhancing those that are reliable. In this paper, we propose a new FM that more accurately and robustly measures focus, which we call the "ring difference filter" (RDF). FMs can usually be categorized as confident local methods or noise robust non-local methods. RDF's unique ring-and-disk structure allows it to have the advantageous sides of both local and non-local FMs. We then describe an efficient pipeline that utilizes the properties that the RDF brings. Our method is able to reproduce results that are on par with or even better than those of the state-of-the-art, while spending less time in computation.

关键词： focus Noise robustness Frequency modulation FACTORY MUTUAL DEPth OF FIELD Resource Description Framework refuse derived fuel computer time Cameras pipeline

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A Low Power, Fully Event-Based Gesture recognition System 30

A Low Power, Fully Event-Based Gesture Recognition System

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Amir, Arnon Taba, Brian Berg, David Melano, Timothy McKinstry, Jeffrey Di Nolfo, Carmelo Nayak, Tapan Andreopoulos, Alexander Garreau, Guillaume Mendoza, Marcela Kusnitz, Jeff Debole, Michael Esser, Steve Delbruck, Tobi Flickner, Myron Modha, Dharmendra IBM Res Yorktown Hts NY 10598 USA Univ Calif San Diego San Diego CA USA UZH ETH Zurich Zurich Switzerland IniLabs GmbH Zurich Switzerland

ISBN: (纸本)9781538604571

We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic vision Sensor (DVS). the biologically inspired DVS transmits data only when a pixel detects a change, unlike traditional frame-based cameras which sample every pixel at a fixed frame rate. this sparse, asynchronous data representation lets event-based cameras operate at much lower power than frame-based cameras. However, much of the energy efficiency is lost if, as in previous work, the event stream is interpreted by conventional synchronous processors. Here, for the first time, we process a live DVS event stream using TrueNorth, a natively event-based processor with 1 million spiking neurons. Configured here as a convolutional neural network (CNN), the TrueNorth chip identifies the onset of a gesture with a latency of 105 ms while consuming less than 200mW. the CNN achieves 96.5% out-of-sample accuracy on a newly collected DVS dataset (DvsGesture) comprising 11 hand gesture categories from 29 subjects under 3 illumination conditions.

关键词： Event-based Gestures LOW POWER Daphne virus S Gesture recognition Energy efficiency pixel end-to-end Cameras PROCESSOR recognition system Latency

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A Novel Tensor-based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors 30

A Novel Tensor-based Video Rain Streaks Removal Approach via...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Jiang, Tai-Xiang Huang, Ting-Zhu Zhao, Xi-Le Deng, Liang-Jian Wang, Yao Univ Elect Sci & Technol China Sch Math Sci Chengdu Sichuan Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China

ISBN: (纸本)9781538604571

Rain streaks removal is an important issue of the outdoor vision system and has been recently investigated extensively. In this paper, we propose a novel tensor based video rain streaks removal approach by fully considering the discriminatively intrinsic characteristics of rain streaks and clean videos, which needs neither rain detection nor time-consuming dictionary learning stage. In specific, on the one hand, rain streaks are sparse and smooth along the raindrops' direction, and on the other hand, the clean videos possess smoothness along the rain-perpendicular direction and global and local correlation along time direction. We use the l(1) norm to enhance the sparsity of the underlying rain streaks, two unidirectional Total Variation (TV) regularizers to guarantee the different discriminative smoothness, and a tensor nuclear norm and a time directional difference operator to characterize the exclusive correlation of the clean video along time. Alternation direction method of multipliers (ADMM) is employed to solve the proposed concise tensor based convex model. Experiments implemented on synthetic and real data substantiate the effectiveness and efficiency of the proposed method. Under comprehensive quantitative performance measures, our approach outperforms other state-of-the-art methods.

关键词： rain Streak Videotapes computer vision difference operator Smoothness

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Temporal Action Co-segmentation in 3D Motion Capture Data and Videos 30

Temporal Action Co-segmentation in 3D Motion Capture Data an...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Papoutsakis, Konstantinos Panagiotakis, Costas Argyros, Antonis A. FORTH Inst Comp Sci Computat Vis & Robot Lab Iraklion Greece Univ Crete Dept Comp Sci Rethimnon Greece TEI Crete Business Adm Dept Agios Nikolaos Iraklion Greece

ISBN: (纸本)9781538604571

Given two action sequences, we are interested in spotting/co-segmenting all pairs of sub-sequences that represent the same action. We propose a totally unsupervised solution to this problem. No a-priori model of the actions is assumed to be available. the number of common sub-sequences may be unknown. the sub-sequences can be located anywhere in the original sequences, may differ in duration and the corresponding actions may be performed by a different person, in different style. We treat this type of temporal action co-segmentation as a stochastic optimization problem that is solved by employing Particle Swarm Optimization (PSO). the objective function that is minimized by PSO capitalizes on Dynamic Time Warping (DTW) to compare two action sub-sequences. Due to the generic problem formulation and solution, the proposed method can be applied to motion capture (i.e., 3D skeletal) data or to conventional RGB videos acquired in the wild. We present extensive quantitative experiments on standard data sets as well as on data sets we introduced in this paper. the obtained results demonstrate that the proposed method achieves a remarkable increase in co-segmentation quality compared to all tested state of the art methods.

关键词： Videos Optimization Image segmentation Linear programming computer vision three-dimensional displays computer science Videotapes three-dimensional displays computer science images computer vision image segmentation linear programming motion capture state of the art Dataset Action Particle swarm optimization

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Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks 30

Physically-Based Rendering for Indoor Scene Understanding Us...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Yinda Song, Shuran Yumer, Ersin Savva, Manolis Lee, Joon-Young Jin, Hailin Funkhouser, thomas Princeton Univ Princeton NJ 08544 USA Adobe Res Princeton NJ USA

ISBN: (纸本)9781538604571

Indoor scene understanding is central to applications such as robot navigation and human companion assistance. Over the last years, data-driven deep neural networks have outperformed many traditional approaches thanks to their representation learning capabilities. One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object boundary detection. To address this problem, a number of works proposed using synthetic data. However, a systematic study of how such synthetic data is generated is missing. In this work, we introduce a large-scale synthetic dataset with 500K physicallybased rendered images from 45K realistic 3D indoor scenes. We study the effects of rendering methods and scene lighting on training for three computer vision tasks: surface normal prediction, semantic segmentation, and object boundary detection. this study provides insights into the best practices for training with synthetic data (more realistic rendering is worth it) and shows that pretraining with our new synthetic dataset can improve results beyond the current state of the art on all three tasks.

关键词： Rendering (computer graphics) Lighting three-dimensional displays Cameras Semantics Training three-dimensional displays Semantics Lighting Cameras Functional training synthetic data Rendering Training Edge detection semiotics Mobile robot navigation

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Robust Joint and Individual Variance Explained 30

Robust Joint and Individual Variance Explained

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sagonas, Christos Panagakis, Yannis Leidinger, Alina Zafeiriou, Stefanos Imperial Coll London London England Onfido London England Middlesex Univ London London England

ISBN: (纸本)9781538604571

Discovering the common (joint) and individual sub-spaces is crucial for analysis of multiple data sets, including multi-view and multi-modal data. Several statistical machine learning methods have been developed for discovering the common features across multiple data sets. the most well studied family of the methods is that of Canonical Correlation Analysis (CCA) and its variants. Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications. that is, it discovers only common features and not individual ones, and it is sensitive to gross errors present in visual data. Recently, efforts have been made in order to develop methods that discover individual and common components. Nevertheless, these methods are mainly applicable in two sets of data. In this paper, we investigate the use of a recently proposed statistical method, the so-called Joint and Individual Variance Explained (JIVE) method, for the recovery of joint and individual components in an arbitrary number of data sets. Since, the JIVE is not robust to gross errors, we propose alternatives, which are both robust to non-Gaussian noise of large magnitude, as well as able to automatically find the rank of the individual components. We demonstrate the effectiveness of the proposed approach to two computer vision applications, namely facial expression synthesis and face age progression in-the-wild.

关键词： Dataset crassitude error Joints Joints images computer vision non-Gaussian noise Representation of data alphameric data Exploration Variance

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On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution 30

On the Global Geometry of Sphere-Constrained Sparse Blind De...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Yuqian Lau, Yenson Kuo, Han-wen Cheung, Sky Pasupathy, Abhay Wright, John Columbia Univ Dept Elect Engn New York NY 10027 USA Columbia Univ Dept Phys 538 W 120th St New York NY 10027 USA

ISBN: (纸本)9781538604571

Blind deconvolution is the problem of recovering a convolutional kernel a(0) and an activation signal x(0) from their convolution y = a(0) circle star x(0). this problem is ill-posed without further constraints or priors. this paper studies the situation where the nonzero entries in the activation signal are sparsely and randomly populated. We normalize the convolution kernel to have unit Frobenius norm and cast the sparse blind deconvolution problem as a nonconvex optimization problem over the sphere. With this spherical constraint, every spurious local minimum turns out to be close to some signed shift truncation of the ground truth, under certain hypotheses. this benign property motivates an effective two stage algorithm that recovers the ground truth from the partial information offered by a suboptimal local minimum. this geometry-inspired algorithm recovers the ground truth for certain microscopy problems, also exhibits promising performance in the more challenging image deblurring problem. Our insights into the global geometry and the two stage algorithm extend to the convolutional dictionary learning problem, where a superposition of multiple convolution signals is observed.

关键词： ground truth Global geometry blind deconvolution Convolution Local Minimum

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Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network 30

Regressing Robust and Discriminative 3D Morphable Models wit...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tran, Anh Tuan Hassner, Tal Masi, Iacopo Medioni, Erard USC Inst Robot & Intelligent Syst Los Angeles CA 90007 USA USC Inst Informat Sci Los Angeles CA 90007 USA Open Univ Israel Raanana Israel

ISBN: (纸本)9781538604571

the 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied "in the wild", their 3D estimates are either unstable and change for different photos of the same subject or they are over-regularized and generic. In response, we describe a robust method for regressing discriminative 3D morphable face models (3DMM). We use a convolutional neural network (CNN) to regress 3DMM shape and texture parameters directly from an input photo. We overcome the shortage of training data required for this purpose by offering a method for generating huge numbers of labeled examples. the 3D estimates produced by our CNN surpass state of the art accuracy on the MICC data set. Coupled with a 3D-3D face matching pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face shapes as representations, rather than the opaque deep feature vectors used by other modern systems.

关键词： three-dimensional displays Shape Face recognition Robustness Training Image reconstruction Training data

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