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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741-750 订阅

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Gesture recognition in Ego-Centric Videos using Dense Trajectories and Hand Segmentation

Gesture Recognition in Ego-Centric Videos using Dense Trajec...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Baraldi, Lorenzo Paci, Francesco Serra, Giuseppe Benini, Luca Cucchiara, Rita Univ Modena & Reggio Emilia Dipartimento Ingn Enzo Ferrari Modena MO Italy Univ Bologna DEI Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9781479943098

We present a novel method for monocular hand gesture recognition in ego-vision scenarios that deals with static and dynamic gestures and can achieve high accuracy results using a few positive samples. Specifically, we use and extend the dense trajectories approach that has been successfully introduced for action recognition. Dense features are extracted around regions selected by a new hand segmentation technique that integrates superpixel classification, temporal and spatial coherence. We extensively test our gesture recognition and segmentation algorithms on public datasets and propose a new dataset shot with a wearable camera. In addition, we demonstrate that our solution can work in near real-time on a wearable device.

关键词： Gesture recognition

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Randomized Max-Margin Compositions for Visual recognition 27

Randomized Max-Margin Compositions for Visual Recognition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Eigenstetter, Angela Takami, Masato Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc & IWR D-69115 Heidelberg Germany Robert Bosch GmbH Corp Res Hildesheim Germany

ISBN: (纸本)9781479951178

A main theme in object detection are currently discriminative part-based models. the powerful model that combines all parts is then typically only feasible for few constituents, which are in turn iteratively trained to make them as strong as possible. We follow the opposite strategy by randomly sampling a large number of instance specific part classifiers. Due to their number, we cannot directly train a powerful classifier to combine all parts. therefore, we randomly group them into fewer, overlapping compositions that are trained using a maximum-margin approach. In contrast to the common rationale of compositional approaches, we do not aim for semantically meaningful ensembles. Rather we seek randomized compositions that are discriminative and generalize over all instances of a category. Our approach not only localizes objects in cluttered scenes, but also explains them by parsing with compositions and their constituent parts. We conducted experiments on PASCAL VOC07, on the VOC10 evaluation server, and on the MITIndoor scene dataset. To the best of our knowledge, our randomized max-margin compositions ((RMC)-C-2) are the currently best performing single class object detector using only HOG features. Moreover, the individual contributions of compositions and their parts are evaluated in separate experiments that demonstrate their potential.

关键词： compositionality mid-level representation object detection part-based models randomized models scene classification visual parsing visual recognition

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Secure Fingerprint Matching With Generic Local Structures

Secure Fingerprint Matching With Generic Local Structures

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Morse, Matthew Hartloff, Jesse Effland, thomas Schuler, Jim Cordaro, Jennifer Tulyakov, Sergey Rudra, Atri Govindaraju, Venu SUNY Buffalo Buffalo NY 14260 USA

ISBN: (纸本)9781479943098

In this work we evaluate the performance of generic local structures as template points for secure fingerprint matching. We present a generic template structure called an n-gon that derives from a set of n neighboring minutiae points. We secure templates consisting of sets of n-gons using the fuzzy vault construct to obfuscate the data. We report the matching performance of our system in terms of the ZeroFAR for comparison with other systems. We also briefly describe a keyed version of our system for comparison with secure systems that utilize a secret user key.

关键词： fingerprint fuzzy vault security Fingerprint recognition Fingerprints n gon Secure jigs(templates) Security

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vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Vision on Wheels: Looking at Driver, Vehicle, and Surround f...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ohn-Bar, Eshed Tawari, Ashish Martin, Sujitha Trivedi, Mohan M. Univ Calif San Diego Comp Vis & Robot Res Lab San Diego CA 92103 USA

ISBN: (纸本)9781479943098

Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. the implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.

关键词： active safety driver assistance systems mobile vision applications real-time behavior analysis temporal action recognition

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A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions 27

A Probabilistic Framework for Multitarget Tracking with Mutu...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yang, Menglong Liu, Yiguang Wen, Longyin You, Zhisheng Li, Stan Z. Sichuan Univ Key Lab Fundamental Synthet Vis Graph & Image Nat Sch Aeronaut & Astronaut & Comp Sci Chengdu Peoples R China Chinese Acad Sci Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. this paper presents a novel probability framework for multitarget tracking with mutual occlusions. the primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. the occlusion variable describes occlusion states of the targets. this forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and occluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a new move type called "occlusion state update" is presented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.

关键词： Markov processes

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Describing Textures in the Wild 27

Describing Textures in the Wild

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Cimpoi, Mircea Maji, Subhransu Kokkinos, Iasonas Mohamed, Sammy Vedaldi, Andrea Univ Oxford Oxford OX1 2JD England Toyota Technol Inst Nagoya Aichi Japan Ecole Cent Paris INRIA Saclay Paris France

ISBN: (纸本)9781479951178

patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". the resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks;in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and Kth-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.

关键词： Fisher Vector attribute convolutional neural network material recognition texture

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recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts 27

Recognition of Complex Events: Exploiting Temporal Dynamics ...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Bhattacharya, Subhabrata Kalaych, Mahdi M. Sukthankar, Rahul Shah, Mubarak Columbia Univ New York NY 10027 USA Univ Cent Florida Orlando FL 32816 USA Google Res Mountain View CA USA

ISBN: (纸本)9781479951178

While approaches based on bags of features excel at low-level action classification, they are ill-suited for recognizing complex events in video, where concept-based temporal representations currently dominate. this paper proposes a novel representation that captures the temporal dynamics of windowed mid-level concept detectors in order to improve complex event recognition. We first express each video as an ordered vector time series, where each time step consists of the vector formed from the concatenated confidences of the pre-trained concept detectors. We hypothesize that the dynamics of time series for different instances from the same event class, as captured by simple linear dynamical system (LDS) models, are likely to be similar even if the instances differ in terms of low-level visual features. We propose a two-part representation composed of fusing: (1) a singular value decomposition of block Hankel matrices (SSID-S) and (2) a harmonic signature (H-S) computed from the corresponding eigen-dynamics matrix. the proposed method offers several benefits over alternate approaches: our approach is straightforward to implement, directly employs existing concept detectors and can be plugged into linear classification frameworks. Results on standard datasets such as NIST's TRECVID Multimedia Event Detection task demonstrate the improved accuracy of the proposed method.

关键词： Complex Event recognition Hankel Matrices Harmonic signatures Linear Dynamical Systems Subspace state identification TRECVID MED Temporal Representation Hankel matrices DYNAMICAL SYSTEMS recognition (Psychology) Videotapes detector Time series dynamics

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From Ego to Nos-vision: Detecting Social Relationships in First-Person Views

From Ego to Nos-vision: Detecting Social Relationships in Fi...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Alletto, Stefano Serra, Giuseppe Calderara, Simone Solera, Francesco Cucchiara, Rita Univ Modena & Reggio Emilia I-41125 Modena Italy

ISBN: (纸本)9781479943098

In this paper we present a novel approach to detect groups in ego-vision scenarios. People in the scene are tracked through the video sequence and their head pose and 3D location are estimated. Based on the concept of f-formation, we define with the orientation and distance an inherently social pairwise feature that describes the affinity of a pair of people in the scene. We apply a correlation clustering algorithm that merges pairs of people into socially related groups. Due to the very shifting nature of social interactions and the different meanings that orientations and distances can assume in different contexts, we learn the weight vector of the correlation clustering using Structural SVMs. We extensively test our approach on two publicly available datasets showing encouraging results when detecting groups from first-person camera views.

关键词： Ego-vision f-formation social interaction

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Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models 27

Seeing 3D chairs: exemplar part-based 2D-3D alignment using ...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Aubry, Mathieu Maturana, Daniel Efros, Alexei A. Russell, Bryan C. Sivic, Josef INRIA Paris France Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Berkeley Berkeley CA USA Intel Labs Cambridge England

ISBN: (纸本)9781479951178

this paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online. Using the "chair" class as a running example, we propose an exemplar-based 3D category representation, which can explicitly model chairs of different styles as well as the large variation in viewpoint. We develop an approach to establish part-based correspondences between 3D CAD models and real photographs. this is achieved by (i) representing each 3D model using a set of view-dependent mid-level visual elements learned from synthesized views in a discriminative fashion, (ii) carefully calibrating the individual element detectors on a common dataset of negative images, and (iii) matching visual elements to the test image allowing for small mutual deformations but preserving the viewpoint and style constraints. We demonstrate the ability of our system to align 3D models with 2D objects in the challenging PASCAL VOC images, which depict a wide variety of chairs in complex scenes.

关键词： 3D CAD models alignment category representation mid-level visual elements recognition seat Alignment three Dimensions recognition (Psychology) three-dimensional displays computer-Aided Design DFFB gene images Comment test sample Dataset

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Superpixel Estimation for Hyperspectral Imagery

Superpixel Estimation for Hyperspectral Imagery

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Massoudifar, Pegah Rangarajan, Anand Gader, Paul Univ Florida Dept CISE Gainesville FL 32611 USA

ISBN: (纸本)9781479943098

In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and superpixel estimation has emerged in this area. Here, we present an approach to superpixel estimation in hyperspectral images by adapting the well known UCM approach to hyperspectral volumes. this approach benefits from the channel information at each pixel of the hyperspectral image while obtaining a compact representation of the hyperspectral volume using principal component analysis. Our experimental evaluation demonstrates that the additional information of spectral channels will substantially improve superpixel estimation from a single "monochromatic" channel. Furthermore, superpixel estimation performed on the compact hyperspectral representation outperforms the same when executed on the entire volume.

关键词： hyperspectral imagery information channels image segmentation Compact Compact pattern pixel extra message principal components analysis wavelengths

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