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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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Perception Enhancement for Bionic vision Preliminary Study on Object Classification with Subretinal Implants 7

Perception Enhancement for Bionic Vision <i>Preliminary Stud...

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7th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Steffen, Johannes Napp, Jonathan Pollmann, Stefan Toennies, Klaus Otto von Guericke Univ Dept Simulat & Graph Magdeburg Germany Otto von Guericke Univ Dept Expt Psychol Magdeburg Germany Otto von Guericke Univ Ctr Brain & Behav Sci Magdeburg Germany

ISBN: (纸本)9789897582769

the restored vision by using subretinal implants of patients suffering from a loss of photoreceptors, e.g., in retinitis pigmentosa and age-related macular degeneration, is, compared to healthy subjects, very limited. therefore, we investigated, whether it is possible to enhance the perception of such patients by transforming the input images in a systematic manner. To this end, we propose a new image transformation network that is capable to learn plausible image transformations in an end-to-end fashion in order to enhance the perception of (virtual) patients with simulated subretinal implants. As a proof of concept, we test our method on an object classification task with three classes. Our results are promising. Compared to a baseline model, the overall object classification accuracy increased significantly from 67.4% to 81.1%. Furthermore, we discuss implications and limitations of our proof of concept and outline aspects of our work that can be improved and need to be subject of further research.

关键词： Bionic vision Retinal Implants Artificial Neural Networks Object Classification

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Face recognition using DRLBP and SIFT Feature Extraction 7

Face Recognition using DRLBP and SIFT Feature Extraction

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7th IEEE International conference on Communication and Signal Processing (IEEE ICCSP)

作者： Sushama, M. Rajinikanth, E. Sathyabama Inst Sci & Technol Chennai Tamil Nadu India

ISBN: (纸本)9781538635216

the detection of human face from images plays a vital role in computer vision, cognitive science and Forensic Science. the various computational and mathematical models, for classifying face including Scale Invariant Feature Transform (SIFT) and Dominant Rotated Local Binary pattern (DRLBP) have been proposed to yield better performance. this paper proposes a novel method of classifying the human face using Artificial Neural Network. this is done by pre-processing the face image at first and then extracting the face features using SIFT. then the detection of human faces is done using Back Propagation Network (BPN). the process of combining SIFT and DRLBP perform better accuracy rather using separately.

关键词： Face recognition Dominant local binary pattern Histogram and Feature Extraction SIFT

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Joint Detection and Identification Feature Learning for Person Search 30

Joint Detection and Identification Feature Learning for Pers...

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

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

ISBN: (纸本)9781538604571

Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be searched from a gallery of whole scene images. To close the gap, we propose a new deep learning framework for person search. Instead of breaking it down into two separate tasks-pedestrian detection and person re-identification, we jointly handle both aspects in a single convolutional neural network. An Online Instance Matching (OIM) loss function is proposed to train the network effectively, which is scalable to datasets with numerous identities. To validate our approach, we collect and annotate a large-scale benchmark dataset for person search. It contains 18, 184 images, 8, 432 identities, and 96, 143 pedestrian bounding boxes. Experiments show that our framework outperforms other separate approaches, and the proposed OIM loss function converges much faster and better than the conventional Softmax loss.

关键词： Proposals Feature extraction Training Benchmark testing Machine learning Detectors Search problems

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Binary Code Ranking with Weighted Hamming Distance

Binary Code Ranking with Weighted Hamming Distance

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

作者： Zhang, Lei Zhang, Yongdong Tang, Jinhui Lu, Ke Tian, Qi Chinese Acad Sci Inst Comp Technol 6 Kexueyuan South Rd Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China Univ Texas San Antonio San Antonio TX USA

ISBN: (纸本)9780769549897

Binary hashing has been widely used for efficient similarity search due to its query and storage efficiency. In most existing binary hashing methods, the high-dimensional data are embedded into Hamming space and the distance or similarity of two points are approximated by the Hamming distance between their binary codes. the Hamming distance calculation is efficient, however, in practice, there are often lots of results sharing the same Hamming distance to a query, which makes this distance measure ambiguous and poses a critical issue for similarity search where ranking is important. In this paper, we propose a weighted Hamming distance ranking algorithm (WhRank) to rank the binary codes of hashing methods. By assigning different bit-level weights to different hash bits, the returned binary codes are ranked at a finer-grained binary code level. We give an algorithm to learn the data-adaptive and query-sensitive weight for each hash bit. Evaluations on two large-scale image data sets demonstrate the efficacy of our weighted Hamming distance for binary code ranking.

关键词： Hamming codes approximation theory binary codes cryptography

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Matching images using invariant level-line primitives under projective transformation

Matching images using invariant level-line primitives under ...

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7th Canadian conference on computer and Robot vision, CRV 2010

作者： Almehio, Yasser Bouchafa, Samia Institut d'Electronique Fondamentale University Paris-Sud XI 91405 Orsay France

ISBN: (纸本)9780769540405

this paper deals with a new registration method based on a specific level-line grouping. Because of its contrast-change invariance, our approach is an appropriate method for matching outdoor image sequences. Moreover, it does not require any estimation of the unknown transformation between images and handle well the critical cases that usually lead to pairing ambiguities, such as repetitive patterns in the images. this study focuses on invariants primitive construction under projective transformation, using level-lines. the registration by itself is performed through an efficient level-line cumulative matching based on a multi-stage primitive election procedure. Each stage provides a coarse estimate of the transformation that the next stage gets to refine. Experiments on real outdoor road scene show the accuracy and efficiency of this approach, using several image sequences covering different pertaining cases with different type of motion. © 2010 IEEE.

关键词： pattern recognition

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Adaptive Object Retrieval with Kernel Reconstructive Hashing 27

Adaptive Object Retrieval with Kernel Reconstructive Hashing

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

作者： Yang, Haichuan Bai, Xiao Zhou, Jun Ren, Peng Zhang, Zhihong Cheng, Jian Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia China Univ Petr Coll Informat & Control Engn Beijing Peoples R China Xiamen Univ Software Sch Xiamen Peoples R China Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Hashing is very useful for fast approximate similarity search on large database. In the unsupervised settings, most hashing methods aim at preserving the similarity defined by Euclidean distance. Hash codes generated by these approaches only keep their Hamming distance corresponding to the pairwise Euclidean distance, ignoring the local distribution of each data point. this objective does not hold for k-nearest neighbors search. In this paper, we firstly propose a new adaptive similarity measure which is consistent with k-NN search, and prove that it leads to a valid kernel. then we propose a hashing scheme which uses binary codes to preserve the kernel function. Using low-rank approximation, our hashing framework is more effective than existing methods that preserve similarity over arbitrary kernel. the proposed kernel function, hashing framework, and their combination have demonstrated significant advantages compared with several state-of-the-art methods.

关键词： Approximate nearest neighbor search adaptive similarity binary code binary code hashing Nearest neighbor query Seeds KERNEL FUNCTIONS Kernel Hamming distance

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Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment 30

Leveraging Intra and Inter-Dataset Variations for Robust Fac...

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

作者： Wu, Wenyan Yang, Shuo Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538607336

Face alignment is a critical topic in the computer vision community. Numerous efforts have been made and various benchmark datasets have been released in recent decades. However, two significant issues remain in recent datasets, e.g., Intra-Dataset Variation and Inter-Dataset Variation. Inter-Dataset Variation refers to bias on expression, head pose, etc. inside one certain dataset, while Intra-Dataset Variation refers to different bias across different datasets. In this study, we show that model robustness can be significantly improved by leveraging rich variations within and between different datasets. this is non-trivial because of inconsistent landmark definitions between different datasets and the serious data bias within one certain dataset. To address the mentioned problems, we proposed a novel Deep Variation Leveraging Network (DVLN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). In particular, DA-Net takes advantage of different characteristics and distributions across different datasets, while CD-Net makes a final decision on candidate hypotheses given by DA-Net to leverage variations within one certain dataset. Extensive evaluations show that our approach demonstrates real-time performance and dramatically outperforms state-of-the-art methods on the challenging 300-W dataset.

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

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Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions

Image Tag Completion via Image-Specific and Tag-Specific Lin...

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

作者： Lin, Zijia Ding, Guiguang Hu, Mingqing Wang, Jianmin Ye, Xiaojun Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9780769549897

though widely utilized for facilitating image management, user-provided image tags are usually incomplete and insufficient to describe the whole semantic content of corresponding images, resulting in performance degradations in tag-dependent applications and thus necessitating effective tag completion methods. In this paper, we propose a novel scheme denoted as LSR for automatic image tag completion via image-specific and tag-specific Linear Sparse Reconstructions. Given an incomplete initial tagging matrix with each row representing an image and each column representing a tag, LSR optimally reconstructs each image (i.e. row) and each tag (i.e. column) with remaining ones under constraints of sparsity, considering image-image similarity, image-tag association and tag-tag concurrence. then both image-specific and tag-specific reconstruction values are normalized and merged for selecting missing related tags. Extensive experiments conducted on both benchmark dataset and web images well demonstrate the effectiveness of the proposed LSR.

关键词： image reconstruction matrix algebra

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DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification 27

DeepReID: Deep Filter Pairing Neural Network for Person Re-I...

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

作者： Li, Wei Zhao, Rui Xiao, Tong Wang, Xiaogang Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detectors. Challenges are presented in the form of complex variations of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter across camera views. In addition, misalignment introduced by the pedestrian detector will affect most existing person re-identification methods that use manually cropped pedestrian images and assume perfect detection. In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. All the key components are jointly optimized to maximize the strength of each component when cooperating with others. In contrast to existing works that use handcrafted features, our method automatically learns features optimal for the re-identification task from data. the learned filter pairs encode photometric transforms. Its deep architecture makes it possible to model a mixture of complex photometric and geometric transforms. We build the largest benchmark re-id dataset with 13,164 images of 1,360 pedestrians. Unlike existing datasets, which only provide manually cropped pedestrian images, our dataset provides automatically detected bounding boxes for evaluation close to practical applications. Our neural network significantly outperforms state-of-the-art methods on this dataset.

关键词： Person Re-Identification

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Sparse Dictionary Learning for Edit Propagation of High-resolution Images 27

Sparse Dictionary Learning for Edit Propagation of High-reso...

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

作者： Chen, Xiaowu Zou, Dongqing Li, Jianwei Cao, Xiaochun Zhao, Qinping Zhang, Hao Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Simon Fraser Univ Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9781479951178

We introduce a method of sparse dictionary learning for edit propagation of high-resolution images or video. Previous approaches for edit propagation typically employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory and time consumption for high-resolution images. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a compact set of representative samples (or features) and perform edit propagation on the samples instead. the sparse set of samples provides an intrinsic basis for an input image, and the coding coefficients capture the linear relationship between all pixels and the samples. the representative set of samples is then optimized by a novel scheme which maximizes the KL-divergence between each sample pair to remove redundant samples. We show several applications of sparsity-based edit propagation including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. We demonstrate that with a sample-to-pixel ratio in the order of 0.01%, signifying a significant reduction on memory consumption, our method still maintains a high-degree of visual fidelity.

关键词： Pixels

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