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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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Scale-Space Techniques for Fiducial Points Extraction from 3D Faces 18th

Scale-Space Techniques for Fiducial Points Extraction from 3...

引用

18th International conference on Image Analysis and Processing (ICIAP)

作者： De Giorgis, Nikolas Rocca, Luigi Puppo, Enrico Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16146 Genoa Italy

ISBN: (纸本)9783319232317;9783319232300

We propose a method for extracting fiducial points from human faces that uses 3D information only and is based on two key steps: multi-scale curvature analysis, and the reliable tracking of features in a scale-space based on curvature. Our scale-space analysis, coupled to careful use of prior information based on variability boundaries of anthropometric facial proportions, does not require a training step, because it makes direct use of morphological characteristics of the analyzed surface. the proposed method precisely identifies important fiducial points and is able to extract new fiducial points that were previously unrecognized, thus paving the way to more effective recognition algorithms.

关键词： Scale-space Multi-scale Curvature 3D computer vision Shape analysis Fiducial points extraction

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Abnormal Crowd Motion Detection Using Double Sparse Representations 5th

Abnormal Crowd Motion Detection Using Double Sparse Represen...

引用

5th International conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Jiang, Jinzhe Tao, Ye Zhao, Wei Tang, Xianglong Harbin Inst Technol Pattern Recognit Ctr Harbin 150006 Peoples R China

ISBN: (纸本)9783319239897;9783319239873

Sparse representation method has been well used in image analysis, restoration and recognition, and it has also been introduced to analysis of video crowd movements recent years. To improve its accuracy of detecting abnormal events in crowd videos, a double sparse representation method is proposed. the method has two sparse processes, one of them judges whether the region of interest is normal, the other finds out whether the region is abnormal. the two judgments will be processed by fuzzy integral to obtain a final result for this region. Experiments are proceed in different datasets to validate the advantages of our algorithm. the results show that our method achieves higher accuracy than previous methods which are used for analysis of video crowd movements.

关键词： Abnormal event Crowd analysis Double sparse representations

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Deeply Learned Attributes for Crowded Scene Understanding

Deeply Learned Attributes for Crowded Scene Understanding

引用

IEEE conference on computer vision and pattern recognition

作者： Jing Shao Kai Kang Chen Change Loy Xiaogang Wang Department of Electronic Engineering The Chinese University of Hong Kong Department of Information Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781467369657

Crowded scene understanding is a fundamental problem in computer vision. In this study, we develop a multi-task deep model to jointly learn and combine appearance and motion features for crowd understanding. We propose crowd motion channels as the input of the deep model and the channel design is inspired by generic properties of crowd systems. To well demonstrate our deep model, we construct a new large-scale WWW Crowd dataset with 10,000 videos from 8, 257 crowded scenes, and build an attribute set with 94 attributes on WWW. We further measure user study performance on WWW and compare this with the proposed deep models. Extensive experiments show that our deep models display significant performance improvements in cross-scene attribute recognition compared to strong crowd-related feature-based baselines, and the deeply learned features behave a superior performance in multi-task learning.

关键词： World Wide Web Crowding images computer vision Channel design performance boost attribute set

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Research of SF6 Pressure Gauge Automatic Reading Methods Based on Machine vision 8th

Research of SF6 Pressure Gauge Automatic Reading Methods Bas...

引用

8th IFIP WG 5.14 International conference (CCTA)

作者： Song Yao Liu Chunhong Deng Qiao Wang Yixuan China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Educ Modern Precis Agr Syst Integrat Res Key Lab Beijing 100083 Peoples R China Minist Agr Key Lab Agr Informat Acquisit Technol Beijing Beijing 100083 Peoples R China State Grid Beijing Elect Power Co Beijing 100031 Peoples R China

ISBN: (纸本)9783319196206;9783319196190

With the rapid development of artificial intelligence and pattern recognition, digital image processing and recognition technologies become a popular research direction, especially, the using of it is quite extensive in power industry. Among various using, dashboard automatic reading is an important part of routing inspection of substation system by using robot. Automatic reading of SF6 pressure gauge pointer is based on image processing and automatic reading techniques, avoiding the influence of subjective factors of naked eye judgment. Designing and analyzing of identification algorithm of SF6 meter pointer are shown in this paper. First, pre-processing operations were operated on the instrument image by using gray level transformation equalization and binarization to improve image quality, by using Hough line detection to realize pointer line extraction;determining the number by using the straight-line in mathematics. this traditional method of using morphological and Hough line detection method to determine reading have certain bias, so the using of Hough circle detection methods and centroid detection methods were proposed. the results showed that the improved method has greatly improved the accuracy of the readings, the method has better accuracy than traditional standard line Hough detection method.

关键词： SF6 pressure gauges Hough detection centroid detection automatic identification

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JOTS: Joint Online Tracking and Segmentation

JOTS: Joint Online Tracking and Segmentation

引用

IEEE conference on computer vision and pattern recognition

作者： Longyin Wen Dawei Du Zhen Lei Stan Z. Li Ming-Hsuan Yang NLPR Institute of Automation Chinese Academy of Sciences SCCE University of Chinese Academy of Sciences School of Engineering University of California at Merced

ISBN: (纸本)9781467369657

We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. the multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. the multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

关键词： segmentation Segmentation tracking Objective function Joints Joints

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Deep LAC: Deep Localization, Alignment and Classification for Fine-grained recognition

Deep LAC: Deep Localization, Alignment and Classification fo...

引用

IEEE conference on computer vision and pattern recognition

作者： Di Lin Xiaoyong Shen Cewu Lu Jiaya Jia Chinese Univ. of Hong Kong Hong Kong China

ISBN: (纸本)9781467369657

We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. this is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function (VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system. the VLF can adaptively compromise the errors of classification and alignment when training the LAC model. It in turn helps update localization. the performance on fine-grained object data bears out the effectiveness of our LAC system.

关键词： sort module shellac recognition system Alignment Taxonomy Very low frequencies back propagation

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Learning from Massive Noisy Labeled Data for Image Classification

Learning from Massive Noisy Labeled Data for Image Classific...

引用

IEEE conference on computer vision and pattern recognition

作者： Tong Xiao Tian Xia Yi Yang Chang Huang Xiaogang Wang The Chinese University of Hong Kong Baidu Research

ISBN: (纸本)9781467369657

Large-scale supervised datasets are crucial to train convolutional neural networks (CNNs) for various computer vision problems. However, obtaining a massive amount of well-labeled data is usually very expensive and time consuming. In this paper, we introduce a general framework to train CNNs with only a limited number of clean labels and millions of easily obtained noisy labels. We model the relationships between images, class labels and label noises with a probabilistic graphical model and further integrate it into an end-to-end deep learning system. To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels. Experiments on this dataset indicate that our approach can better correct the noisy labels and improves the performance of trained CNNs.

关键词： Noise Image classification Dataset images computer vision Learning TRAIN

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UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints

UniHIST: A Unified Framework for Image Restoration With Marg...

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IEEE conference on computer vision and pattern recognition

作者： Xing Mei Weiming Dong Bao-Gang Hu Siwei Lyu Computer Science Department University at Albany NLPR Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467369657

Marginal histograms provide valuable information for various computer vision problems. However, current image restoration methods do not fully exploit the potential of marginal histograms, in particular, their role as ensemble constraints on the marginal statistics of the restored image. In this paper, we introduce a new framework, Uni-HIST, to incorporate marginal histogram constraints into image restoration. the key idea of UniHIST is to minimize the discrepancy between the marginal histograms of the restored image and the reference histograms in pixel or gradient domains using the quadratic Wasserstein (W_2) distance. the W_2 distance can be computed directly from data without resorting to density estimation. It provides a differentiable metric between marginal histograms and allows easy integration with existing image restoration methods. We demonstrate the effectiveness of UniHIST through denoising of pattern images and non-blind deconvolution of natural images. We show that UniHIST enhances restoration performance and leads to visual and quantitative improvements over existing state-of-the-art methods.

关键词： image restoration technique histograms images Noise reduction computer vision pixel

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Face Alignment by Coarse-to-Fine Shape Searching

Face Alignment by Coarse-to-Fine Shape Searching

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IEEE conference on computer vision and pattern recognition

作者： Shizhan Zhu Cheng Li Chen Change Loy Xiaoou Tang Department of Information Engineering The Chinese University of Hong Kong SenseTime Group

ISBN: (纸本)9781467369657

We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. the unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches;and ii) improves the robustness in coping with large pose variations. the framework demonstrates real-time performance and state-of-the-art results on various benchmarks including the challenging 300-W dataset.

关键词： cascades Shape Face Face BENCHMARKS wall coping Searches

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Online Sketching Hashing

Online Sketching Hashing

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IEEE conference on computer vision and pattern recognition

作者： Cong Leng Jiaxiang Wu Jian Cheng Xiao Bai Hanqing Lu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467369657

Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.

关键词： hashing Dataset storage spaces Sketches complexity classes hashing method small size

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