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

共 2178 条记录，以下是281-290 订阅

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Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction 27

Separation of Line Drawings Based on Split Faces for 3D Obje...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zou, Changqing Yang, Heng Liu, Jianzhuang Shenzhen Inst Adv Technol Shenzhen Key Lab CVPR Shenzhen Peoples R China Hengyang Normal Univ Dept Phys & Elect Informat Sci Hengyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Queen Mary Univ London London England Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Huawei Technol Co Ltd Media Lab Shenzhen Peoples R China

ISBN: (纸本)9781479951178

Reconstructing 3D objects from single line drawings is often desirable in computer vision and graphics applications. If the line drawing of a complex 3D object is decomposed into primitives of simple shape, the object can be easily reconstructed. We propose an effective method to conduct the line drawing separation and turn a complex line drawing into parametric 3D models. this is achieved by recursively separating the line drawing using two types of split faces. Our experiments show that the proposed separation method can generate more basic and simple line drawings, and its combination with the example-based reconstruction can robustly recover wider range of complex parametric 3D objects than previous methods.

关键词： 3D reconstruction Line drawing decomposition Line drawing separation Split face

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Learning the Change for Automatic Image Cropping

Learning the Change for Automatic Image Cropping

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Yan, Jianzhou Lin, Stephen Kang, Sing Bing Tang, Xiaoou Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9780769549897

Image cropping is a common operation used to improve the visual quality of photographs. In this paper, we present an automatic cropping technique that accounts for the two primary considerations of people when they crop: removal of distracting content, and enhancement of overall composition. Our approach utilizes a large training set consisting of photos before and after cropping by expert photographers to learn how to evaluate these two factors in a crop. In contrast to the many methods that exist for general assessment of image quality, ours specifically examines differences between the original and cropped photo in solving for the crop parameters. To this end, several novel image features are proposed to model the changes in image content and composition when a crop is applied. Our experiments demonstrate improvements of our method over recent cropping algorithms on a broad range of images.

关键词： image processing

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DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal 30

DeshadowNet: A Multi-context Embedding Deep Network for Shad...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qu, Liangqiong Tian, Jiandong He, Shengfeng Tang, Yandong Lau, Rynson W. H. Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538604571

Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. the first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. these two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.

关键词： Semantics Feature extraction Lighting Image color analysis Benchmark testing computer vision Benchmark testing image colour analysis shadows Semantics images computer vision Lighting Feature extraction Online resources parallel network semiotics BENCHMARKS embedding

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Switchable Deep Network for Pedestrian Detection 27

Switchable Deep Network for Pedestrian Detection

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Luo, Ping Tian, Yonglong Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection. the SDN automatically learns hierarchical features, salience maps, and mixture representations of different body parts. Pedestrian detection faces the challenges of background clutter and large variations of pedestrian appearance due to pose and viewpoint changes and other factors. One of our key contributions is to propose a Switchable Restricted Boltzmann Machine (SRBM) to explicitly model the complex mixture of visual variations at multiple levels. At the feature levels, it automatically estimates saliency maps for each test sample in order to separate background clutters from discriminative regions for pedestrian detection. At the part and body levels, it is able to infer the most appropriate template for the mixture models of each part and the whole body. We have devised a new generative algorithm to effectively pretrain the SDN and then fine-tune it with back-propagation. Our approach is evaluated on the Caltech and Eth datasets and achieves the state-of-the-art detection performance.

关键词： Pedestrians test sample clutter AUTOMATIC Boltzmann machines fine tuning

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New approach of gait recognition for human ID

New approach of gait recognition for human ID

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7th International conference on Signal Processing

作者： Xue, WM Liu, Y Han, HZ Xie, L Wang, ZL Chen, FJ Beijing Union Univ Beijing 100101 Peoples R China

ISBN: (纸本)7121002159

A new gait recognition approach for human ID based on Fourier descriptors is presented in this paper. Body silhouettes are extracted by background subtraction, and cyclic gait analysis is performed to extract key frames. the body contours are processed by Fourier descriptors with data dimension being reduced, and templates are created. the recognition is achieved by nearest neighbor algorithm. We evaluate the proposed gait recognition method on the SOTON gait database, and the experimental results demonstrate that our approach has a good recognition performance.

关键词： gait recognition Fourier descriptors image processing computer vision

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pattern recognition: 7th Mexican conference, MCPR 2015 Mexico City, Mexico, June 24-27, 2015 Proceedings 7

Pattern Recognition: 7th Mexican Conference, MCPR 2015 Mexic...

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7th Mexican conference on pattern recognition, MCPR 2015

作者： Carrasco-Ochoa, Jesús Ariel Martínez-Trinidad, José Francisco Sossa-Azuela, Juan Humberto Olvera López, José Arturo Famili, Fazel National Institute of Astrophysics Optics and Electronics Puebla Mexico National Polytechnic Institute of Mexico Ciudad de México Mexico Autonomous University of Puebla Puebla Mexico University of Ottawa OttawaON Canada

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Learning Non-Lambertian Object Intrinsics across ShapeNet Categories 30

Learning Non-Lambertian Object Intrinsics across ShapeNet Ca...

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shi, Jian Dong, Yue Su, Hao Yu, Stella X. Univ Chinese Acad Sci Chinese Acad Sci Inst Software SKLCS Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Univ Calif Berkeley ICSI Berkeley CA USA

ISBN: (纸本)9781538604571

We focus on the non-Lambertian object-level intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. Based on existing 3D models in the ShapeNet database, a large-scale object intrinsics database is rendered with HDR environment maps. Millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN, which can decompose an image into the product of albedo and shading components along with an additive specular component. Our CNN delivers accurate and sharp results in this classical inverse problem of computer vision. Evaluated on our realistically synthetic dataset, our method consistently outperforms the state-of-the-art by a large margin. We train and test our CNN across different object categories. Perhaps surprising especially from the CNN classification perspective, our intrinsics CNN generalizes very well across categories. Our analysis shows that feature learning at the encoder stage is more crucial for developing a universal representation across categories. We apply our model to real images and videos from Internet, and observe robust and realistic intrinsics results. Quality non-Lambertian intrinsics could open up many interesting applications such as realistic product search based on material properties and image-based albedo / specular editing.

关键词： images computer vision albedo Category artificial shade Inverse method Videotapes Imagery (Psychotherapy)

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Real-time Mobile Facial Expression recognition System - A Case Study

Real-time Mobile Facial Expression Recognition System - A Ca...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Suk, Myunghoon Prabhakaran, Balakrishnan Univ Texas Dallas Dept Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9781479943098

this paper presents a mobile application for real time facial expression recognition running on a smart phone with a camera. the proposed system uses a set of Support Vector Machines (SVMs) for classifying 6 basic emotions and neutral expression along with checking mouth status. the facial expression features for emotion recognition are extracted by Active Shape Model (ASM) fitting landmarks on a face and then dynamic features are generated by the displacement between neutral and expression features. We show experimental results with 86% of accuracy with 10 folds cross validation in 309 video samples of the extended Cohn-Kanade (CK+) dataset. Using the same SVM models, the mobile app is running on Samsung Galaxy S3 with 2.4 fps. the accuracy of real-time mobile emotion recognition is about 72% for 6 posed basic emotions and neutral expression by 7 subjects who are not professional actors.

关键词： active shape model emotion recognition facial expression recognition mobile vision support vector machine

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Learning Object Interactions and Descriptions for Semantic Image Segmentation 30

Learning Object Interactions and Descriptions for Semantic I...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Guangrun Luo, Ping Lin, Liang Wang, Xiaogang Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China SenseTime Grp Ltd Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China

ISBN: (纸本)9781538604571

Recent advanced deep convolutional networks (CNNs) achieved great successes in many computer vision tasks, because of their compelling learning complexity and the presences of large-scale labeled data. However, as obtaining per-pixel annotations is expensive, performances of CNNs in semantic image segmentation are not fully exploited. this work significantly increases segmentation accuracy of CNNs by learning from an Image Descriptions in the Wild (IDW) dataset. Unlike previous image captioning datasets, where captions were manually and densely annotated, images and their descriptions in IDW are automatically downloaded from Internet without any manual cleaning and refinement. An IDW-CNN is proposed to jointly train IDW and existing image segmentation dataset such as Pascal VOC 2012 (VOC). It has two appealing properties. First, knowledge from different datasets can be fully explored and transferred from each other to improve performance. Second, segmentation accuracy in VOC can be constantly increased when selecting more data from IDW. Extensive experiments demonstrate the effectiveness and scalability of IDW-CNN, which outperforms existing best-performing system by 12% on VOC12 test set.

关键词： image segmentation captions Volatile Organic Compounds images Dataset Learning Objects Semantics computer vision Scalability

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Unconstrained Face Alignment without Face Detection 30

Unconstrained Face Alignment without Face Detection

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30th IEEE/CVF conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Shao, Xiaohu Xing, Junliang Lv, Jiangjing Xiao, Chunlin Liu, Pengcheng Feng, Youji Cheng, Cheng Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China CloudWalk Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538607336

this paper introduces our submission to the 2nd Facial Landmark Localisation Competition. We present a deep architecture to directly detect facial landmarks without using face detection as an initialization. the architecture consists of two stages, a Basic Landmark Prediction Stage and a Whole Landmark Regression Stage. At the former stage, given an input image, the basic landmarks of all faces are detected by a sub-network of landmark heatmap and affinity field prediction. At the latter stage, the coarse canonical face and the pose can be generated by a Pose Splitting Layer based on the visible basic landmarks. According to its pose, each canonical state is distributed to the corresponding branch of the shape regression sub-networks for the whole landmark detection. Experimental results show that our method obtains promising results on the 300-W dataset, and achieves superior performances over the baselines of the semi-frontal and the profile categories in this competition.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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