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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

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Egocentric Gesture recognition Using Recurrent 3D Convolutional Neural Networks with Spatiotemporal Transformer Modules 16

Egocentric Gesture Recognition Using Recurrent 3D Convolutio...

引用

16th IEEE International conference on computer vision (ICCV)

作者： Cao, Congqi Zhang, Yifan Wu, Yi Lu, Hanqing Cheng, Jian Chinese Acad Sci Inst Automat Nat Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanjing Audit Univ Sch Technol Nanjing Jiangsu Peoples R China Indiana Univ Dept Med Bloomington IN 47405 USA CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9781538610329

Gesture is a natural interface in interacting with wearable devices such as VR/AR helmet and glasses. the main challenge of gesture recognition in egocentric vision arises from the global camera motion caused by the spontaneous head movement of the device wearer. In this paper, we address the problem by a novel recurrent 3D convolutional neural network for end-to-end learning. We specially design a spatiotemporal transformer module with recurrent connections between neighboring time slices which can actively transform a 3D feature map into a canonical view in both spatial and temporal dimensions. To validate our method, we introduce a new dataset with sufficient size, variation and reality, which contains 83 gestures designed for interaction with wearable devices, and more than 24,000 RGB-D gesture samples from 50 subjects captured in 6 scenes. On this dataset, we show that the proposed network outperforms competing state-of-the-art algorithms. Moreover, our method can achieve state-of-the-art performance on the challenging GTEA egocentric action dataset.

关键词： three-dimensional displays Gesture recognition Spatiotemporal phenomena Videos Cameras Two dimensional displays

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Fast Vein pattern Extraction Based on a Binary Filter 7th

Fast Vein Pattern Extraction Based on a Binary Filter

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7th International conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Li, Shidong Sun, Shuang Guo, Zhenhua Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9783319677774;9783319677767

Vein pattern extraction from Near-infrared (NIR) images is essential in most vein recognition algorithms. Among many vein recognition systems, the matched filter has been widely applied because it works well in enhancing vein images. However, the matched filter is time-consuming as it uses multi-scale and multi-orientation Gauss or Gabor filters to generate the Matched Filter Response (MFR) images. In this paper, we propose a binary filter for vein pattern extraction which can achieve similar results as the Gauss or Gabor filter but with fewer parameters and faster processing speed. the proposed method could process 27 images with image resolution of 320 * 240 per second, which is about three times faster than Gauss or Gabor filter.

关键词： Vein extraction Binary filter Matched filter Fast convolution

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Visual Place recognition with CNNs: From Global to Partial 7

Visual Place Recognition with CNNs: From Global to Partial

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7th International conference on Image Processing theory, Tools and Applications (IPTA)

作者： Xin, Zhe Cui, Xiaoguang Zhang, Jixiang Yang, Yiping Wang, Yanqing Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538618424

Visual place recognition is one of the most challenging problems in computer vision, due to the large diversities that real-world places can represent. Recently, visual place recognition has become a key part of loop closure detection and topological localization in long-term mobile robot autonomy. In this work, we build up a novel visual place recognition pipeline composed of a first filtering stage followed by a partial reranking process. In the filtering stage, image-wise features are utilized to find a small set of potential places. Afterwards, stable region-wise landmarks are extracted for more accurate matching in the partial reranking process. All global and partial image representations are derived from pre-trained Convolutional Neural Networks (CNNs), and the landmarks are extracted by object proposal techniques. Moreover, a new similarity measurement is provided by considering both spatial and scale distribution of landmarks. Compared with current methods only considering scale distribution, the presented similarity measurement can benefit recognition precision and robustness effectively. Experiments with varied viewpoints and environmental conditions demonstrate that the proposed method achieves superior performance against state-of-the-art methods.

关键词： visual place recognition localization convolutional neural networks long-term environment

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From Classification to Regression: Model Transfer for Visual Aesthetic Quality Assessment 4

From Classification to Regression: Model Transfer for Visual...

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4th IAPR Asian conference on pattern recognition (ACPR)

作者： Huang, Wenzhen Yang, Peipei Huang, Kaiqi CRIPAC Beijing Peoples R China CASIA NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9781538633540

Visual aesthetic quality assessment has played an important role in increasing number of computer vision applications. Particularly, estimating the quality score precisely is a main task of aesthetic quality assessment, but the training samples labeled with score are usually expensive to obtain. In this paper, we propose a transfer learning method which can improve the performance of aesthetic score prediction by using the coarse labeled samples, which are much easier to obtain. the proposed method incorporates the coarse information from source domain into the target domain by a novel multi-task framework, which can revise the model in target task. the effectiveness of our method is proven by experimental results that the error is reduced obviously with the help of source domain.

关键词： Transfer Learning Multi-task Learning Visual Aesthetic Quality Assessment

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GLOBAL FOR COARSE AND PART FOR FINE: A HIERARCHICAL ACTION recognition FRAMEWORK

GLOBAL FOR COARSE AND PART FOR FINE: A HIERARCHICAL ACTION R...

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IEEE International conference on Image Processing

作者： Weiwei Liu Chongyang Zhang Jiaying Zhang Zhonghao Wu School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

Action recognition is one significant yet challenging task in computer vision. Recent methods mainly model an end-to-end one-stage non-deep or deep learning networks to distinguish different action categories. In this paper we introduce one novel hierarchical action classification framework: Unlike existing one-stage recognition models, the proposed work improves the recognition accuracy by: 1) developing a hierarchical coarse-to-fine action classification framework by dividing the recognition processing into two stages: coarsegrained classification and fine-grained classification, and 2) representing actions in different stages with different granularity features representation: global features are utilized for coarse classifiers while more body parts patterns for fine-grained classifiers are aggregated. Experiments on two widely-tested benchmark datasets show that our method can achieve state-of-the-art or competitive performance compared with existing results using one-stage models, with advantages regarding the recognition accuracy and robustness.

关键词： Feature extraction pattern recognition Measurement Optical imaging Testing Task analysis Computational modeling

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Completed local structure patterns on three orthogonal planes for dynamic texture recognition 7

Completed local structure patterns on three orthogonal plane...

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7th International conference on Image Processing theory, Tools and Applications, IPTA 2017

作者： Nguyen, thanh Tuan Nguyen, thanh Phuong Bouchara, Frederic Université de Toulon CNRS LSIS UMR 7296 La Garde83957 France Aix Marseille Université CNRS ENSAM LSIS UMR 7296 Marseille13397 France

ISBN: (纸本)9781538618417

Dynamic texture (DT) is a challenging problem in computer vision because of the chaotic motion of textures. We address in this paper a new dynamic texture operator by considering local structure patterns (LSP) and completed local binary patterns (CLBP) for static images in three orthogonal planes to capture spatial-temporal texture structures. Since the typical operator of local binary patterns (LBP), which uses center pixel for thresholding, has some limitations such as sensitivity to noise and near uniform regions, the proposed approach can deal with these drawbacks by using global and local texture information for adaptive thresholding and CLBP for exploiting complementary texture information in three orthogonal planes. Evaluations on different datasets of dynamic textures (UCLA, DynTex, DynTex++) show that our proposal significantly outperforms recent results in the state-of-the-art approaches. © 2017 IEEE.

关键词： Local binary pattern

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View Adaptive Recurrent Neural Networks for High Performance Human Action recognition from Skeleton Data 16

View Adaptive Recurrent Neural Networks for High Performance...

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16th IEEE International conference on computer vision (ICCV)

作者： Zhang, Pengfei Lan, Cuiling Xing, Junliang Zeng, Wenjun Xue, Jianru Zheng, Nanning Xi An Jiao Tong Univ Shannxi Peoples R China Microsoft Res Asia Beijing Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781538610329

Skeleton-based human action recognition has recently attracted increasing attention due to the popularity of 3D skeleton data. One main challenge lies in the large view variations in captured human actions. We propose a novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action. Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end. Extensive experiment analyses show that the proposed view adaptive RNN model strives to (1) transform the skeletons of various views to much more consistent viewpoints and (2) maintain the continuity of the action rather than transforming every frame to the same position with the same body orientation. Our model achieves significant improvement over the state-of-the-art approaches on three benchmark datasets.

关键词： Skeleton Cameras three-dimensional displays computer architecture Image color analysis Logic gates Adaptation models

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Visual Remote Monitoring and Control System for Rod Braking on Hot Rolling Mills 9th

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9th Mexican conference on pattern recognition (MCPR)

作者： Starostenko, Oleg Trygub, Irina G. Cruz-Perez, Claudia Alarcon-Aquino, Vicente Potap, Oleg E. Univ Americas Puebla Dept Comp Elect & Mechatron Cholula 72820 Mexico Natl Met Acad Ukraine Dept Automat Prod Proc Dnipro Ukraine

ISBN: (纸本)9783319592268;9783319592251

In steel production the finishing process on hot rolling mill includes a set of essential operations managed by complex control mechanical, electrical and hydraulic equipment. However, accuracy of mill automating mechanisms and sensors is still low due to hot hostile environment with strong vibration and shock. the proposed solution is a computer vision application that exploits morphological filtering and discontinuity masks for detection and separation of rods on rolling mill and provides fast recognition and tracking rod front ends during their deceleration on cooler. the proposed algorithm has been implemented and evaluated in real time conditions achieving precision of rod front end recognition in range of 90-98% on artificial and daylight illumination, respectively.

关键词： computer vision Morphological filtering pattern recognition and tracking Steel manufacturing on rolling mill

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Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis 16

Beyond Face Rotation: Global and Local Perception GAN for Ph...

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16th IEEE International conference on computer vision (ICCV)

作者： Huang, Rui Zhang, Shu Li, Tianyu He, Ran CASIA Natl Lab Pattern Recognit Beijing Peoples R China CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538610329

Photorealistic frontal view synthesis from a single face image has a wide range of applications in the field of face recognition. Although data-driven deep learning methods have been proposed to address this problem by seeking solutions from ample face data, this problem is still challenging because it is intrinsically ill-posed. this paper proposes a Two-Pathway Generative Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by simultaneously perceiving global structures and local details. Four landmark located patch networks are proposed to attend to local textures in addition to the commonly used global encoder-decoder network. Except for the novel architecture, we make this ill-posed problem well constrained by introducing a combination of adversarial loss, symmetry loss and identity preserving loss. the combined loss function leverages both frontal face distribution and pre-trained discriminative deep face models to guide an identity preserving inference of frontal views from profiles. Different from previous deep learning methods that mainly rely on intermediate features for recognition, our method directly leverages the synthesized identity preserving image for downstream tasks like face recognition and attribution estimation. Experimental results demonstrate that our method not only presents compelling perceptual results but also outperforms state-of-theart results on large pose face recognition.

关键词： Face Gallium nitride Face recognition Machine learning Training Tensile stress

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Combing Structured Light Measurement Technology with Binocular Stereo vision 7

Combing Structured Light Measurement Technology with Binocul...

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7th IEEE Annual International conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Zhou, Qi Yang, Yongming Wang, Zhenzhou Chinese Acad Sci Shenyang Inst Automat Beijing Peoples R China

ISBN: (纸本)9781538604908

3D measurement technology is one of the important research fields in computer vision. 3D measurement based on binocular stereo vision is a typical measurement technique. In this paper, the structured light technology is combined with binocular stereo technology. the structured light can increase the feature of object's surface, so we can reconstruct the surface of object more robustly. In this paper, we establish the 3D imaging system, obtain the point of 3D coordinates on the imaging plane, and then extract the center of the stripe image. Based on the stripe center, we propose a new method of matching. At last, we reconstruct the 3D surface of the object by calculating the interception points of two rays with analytical solutions. the experimental results shorn that the method is feasible and efficient.

关键词： 3D measurement structured light imaging plane stripe image matching

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