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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1001-1010 订阅

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Edge Enhanced Depth Motion Map for Dynamic Hand Gesture recognition

Edge Enhanced Depth Motion Map for Dynamic Hand Gesture Reco...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Chenyang Tian, Yingli CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9780769549903

In this paper, we propose a novel approach to recognize dynamic hand gestures from depth video by integrating Edge Enhanced Depth Motion Map together with Histogram of Gradient descriptor. the novelty of this paper has two aspects: first, we propose a novel feature representation, Edge Enhanced Depth Motion Map (E2DMM), balancing the information weighing between shape and motion, which is more suitable for hand gesture recognition;second, we further employ a dynamic temporal pyramid to segment the depth video sequence to address temporal structure information of dynamic hand gestures. Histogram of Gradient is applied on E2DMM to generate vectored representation. Comparison study has been conducted with the state-of-the-art approaches and demonstrates that our approach can achieve better and more stable performance while keeping a relative simple model with lower complexity as well as higher generality.

关键词： edge detection feature extraction gesture recognition image motion analysis image representation image segmentation image sequences statistical distributions video signal processing

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Action recognition with Temporal Relationships

Action Recognition with Temporal Relationships

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Cheng, Guangchun Wan, Yiwen Santiteerakul, Wasana Tang, Shijun Buckles, Bill P. Univ N Texas Denton TX 76203 USA

ISBN: (纸本)9780769549903

Action recognition is an important component in human-machine interactive systems and video analysis. Besides low-level actions, temporal relationships are also important for many actions, which are not fully studied for recognizing actions. We model the temporal structure of low-level actions based on dense trajectory groups. Trajectory groups are a higher level and more meaningful representation of actions than raw individual trajectories. Based on the temporal ordering of trajectory groups, we describe the temporal structure using Allen's temporal relations in a discriminative manner, and combine it with a generative model using bag-of-words. the simple idea behind the model is to extract mid-level features from domain-independent dense trajectories and classify the actions by exploring the temporal structure among them based on a set of Allen's relations. We compare the proposed approach with bag-of-words representation using public datasets, and the results show that our approach improves recognition accuracy.

关键词： feature extraction image recognition image representation

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LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse Representation Classification

LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ptucha, Raymond Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780769549903

Sparse representations have successfully been exploited for the development of highly accurate classifiers. Unfortunately, these classifiers are computationally intensive and subject to the adverse effects of coefficient contamination, where for example variations in pose may affect identity and expression recognition. We propose a technique, called LGE-KSVD, that addresses both problems and attains state-of-the-art results for face and gesture classification problems. Specifically, LGE-KSVD utilizes variants of Linear extension of Graph Embedding to optimize K-SVD, an iterative technique for small yet overcomplete dictionary learning. the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based linear classifier are jointly learned through LGE-KSVD. the atom optimization process is redefined to have variable support using graph embedding techniques to produce a more flexible and elegant dictionary learning algorithm. Results are obtained for a wide variety of facial and activity recognition problems to demonstrate the robustness of the proposed method.

关键词： dictionaries face recognition graph theory image classification image representation learning (artificial intelligence) object recognition optimisation singular value decomposition

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Constrained Clustering and Its Application to Face Clustering in Videos

Constrained Clustering and Its Application to Face Clusterin...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wu, Baoyuan Zhang, Yifan Hu, Bao-Gang Ji, Qiang CASIA NLPR Beijing 100190 Peoples R China Rensselaer Polytech Inst Troy NY 12180 USA

ISBN: (纸本)9780769549897

In this paper, we focus on face clustering in videos. Given the detected faces from real-world videos, we partition all faces into K disjoint clusters. Different from clustering on a collection of facial images, the faces from videos are organized as face tracks and the frame index of each face is also provided. As a result, many pairwise constraints between faces can be easily obtained from the temporal and spatial knowledge of the face tracks. these constraints can be effectively incorporated into a generative clustering model based on the Hidden Markov Random Fields (HMRFs). Within the HMRF model, the pairwise constraints are augmented by label-level and constraint-level local smoothness to guide the clustering process. the parameters for both the unary and the pairwise potential functions are learned by the simulated field algorithm, and the weights of constraints can be easily adjusted. We further introduce an efficient clustering framework specially for face clustering in videos, considering that faces in adjacent frames of the same face track are very similar. the framework is applicable to other clustering algorithms to significantly reduce the computational cost. Experiments on two face data sets from real-world videos demonstrate the significantly improved performance of our algorithm over state-of-the-art algorithms.

关键词： face recognition hidden Markov models object tracking pattern clustering video signal processing

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Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers

Adapting a Pedestrian Detector by Boosting LDA Exemplar Clas...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Xu, Jiaolong Vazquez, David Ramos, Sebastian Lopez, Antonio M. Ponsa, Daniel Autonomous Univ Barcelona Comp Vis Ctr E-08193 Barcelona Spain

ISBN: (纸本)9780769549903

Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. the results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.

关键词： computer vision object detection pedestrians statistical analysis

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General Regression and Representation Model for Face recognition

General Regression and Representation Model for Face Recogni...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Qian, Jianjun Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9780769549903

Recently, the regularized coding-based classification method (e. g. SRC and CRC) shows a great potential for face recognition. However, most existing coding methods ignore the statistical information from the training data, which actually plays an important role in classification. To address this problem, we develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also introduces the prior information and the specific information to enhance the classification performance. In GRR, we combine the leave-one-out strategy with K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using the iterative algorithm to update the feature weights of the test sample. Finally, we classify the test sample based on the reconstruction error of each class. the proposed model is evaluated on public face image databases. And the experimental results demonstrate the advantages of GRR over state-of-the-art methods.

关键词： face recognition image classification image coding image reconstruction image representation iterative methods regression analysis visual databases

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Cardiff Conversation Database (CCDb): A Database of Natural Dyadic Conversations

Cardiff Conversation Database (CCDb): A Database of Natural ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Aubrey, Andrew J. Marshall, David Rosin, Paul L. Vandeventer, Jason Cunningham, Douglas W. Wallraven, Christian Cardiff Univ Sch Comp Sci Cardiff CF10 3AX S Glam Wales Brandenburg Tech Univ Cottbus Cottbus Germany Korea Univ Seoul South Korea

ISBN: (纸本)9780769549903

We present the Cardiff Conversation Database (CCDb), a unique 2D audiovisual database containing natural conversations between pairs of people. the database currently contains 30 conversations. To date, eight conversations are fully annotated for speaker activity, facial expressions, head motion, and non-verbal utterances. In this paper we describe the data collection and annotation process. We also provide results of baseline experiments in which an SVM classifier was used to identify which parts of the recordings are from the frontchannel speaker and which are backchannel signals. We believe this database will make a useful contribution to computer vision, affective computing, and cognitive science communities by providing raw data, features, annotations and baseline comparisons.

关键词： audio databases pattern classification support vector machines visual databases

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PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors

PISA: Pixelwise Image Saliency by Aggregating Complementary ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Shi, Keyang Wang, Keze Lu, Jiangbo Lin, Liang Sun Yat Sen Univ Guangzhou 510275 Guangdong Peoples R China Advanced Digital Sci Cte Singapore Singapore

ISBN: (纸本)9780769549897

Driven by recent vision and graphics applications such as image segmentation and object recognition, assigning pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly critical. More often, such fine-grained saliency detection is also desired to have a fast runtime. Motivated by these, we propose a generic and fast computational framework called PISA - Pixelwise Image Saliency Aggregating complementary saliency cues based on color and structure contrasts with spatial priors holistically. Overcoming the limitations of previous methods often using homogeneous superpixel-based and color contrast-only treatment, our PISA approach directly performs saliency modeling for each individual pixel and makes use of densely overlapping, feature-adaptive observations for saliency measure computation. We further impose a spatial prior term on each of the two contrast measures, which constrains pixels rendered salient to be compact and also centered in image domain. By fusing complementary contrast measures in such a pixelwise adaptive manner, the detection effectiveness is significantly boosted. Without requiring reliable region segmentation or post-relaxation, PISA exploits an efficient edge-aware image representation and filtering technique and produces spatially coherent yet detail-preserving saliency maps. Extensive experiments on three public datasets demonstrate PISA's superior detection accuracy and competitive runtime speed over the state-of-the-arts approaches.

关键词： computer vision edge detection feature extraction filtering theory image colour analysis image representation image resolution image segmentation object detection object recognition rendering (computer graphics)

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Automated X-ray object recognition using an efficient search algorithm in multiple views

Automated X-ray object recognition using an efficient search...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mery, Domingo Riffo, Vladimir Zuccar, Irene Pieringer, Christian Pontificia Univ Catolica Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9780769549903

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of an automated method based on multiple Xray views to recognize certain regular objects with highly defined shapes and sizes. the method consists of two steps: 'monocular analysis', to obtain possible detections in each view of a sequence, and 'multiple view analysis', to recognize the objects of interest using matchings in all views. the search for matching candidates is efficiently performed using a lookup table that is computed off-line. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing regular objects -clips, springs and razor blades-in pen cases are shown achieving around 93% accuracy for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.

关键词： X-ray imaging object recognition security

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3D R Transform on Spatio-Temporal Interest Points for Action recognition

3D <i>R</i> Transform on Spatio-Temporal Interest Points for...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yuan, Chunfeng Li, Xi Hu, Weiming Ling, Haibin Maybank, Stephen Natl Lab Pattern Recognit Inst Automat Beijing Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Birkbeck Coll Sch Comp Sci & Informat Syst London WC1E 7HX England

ISBN: (纸本)9780769549897

Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the R transform which is defined as an extended 3D discrete Radon transform, followed by applying a two-directional two-dimensional principal component analysis. Such R feature captures the geometrical information of the interest points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the R feature with the BOVW representation for further improving recognition accuracy. We utilize a context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition.

关键词： Radon transforms computational geometry feature extraction image motion analysis image representation

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