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Deep Crisp Boundaries 30

Deep Crisp Boundaries

引用

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

作者： Wang, Yupei Zhao, Xin Huang, Kaiqi CASIA 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 Tech Beijing Peoples R China

ISBN: (纸本)9781538604571

Edge detection has made significant progress with the help of deep Convolutional Networks (ConvNet). ConvNet based edge detectors approached human level performance on standard benchmarks. We provide a systematical study of these detector outputs, and show that they failed to accurately localize edges, which can be adversarial for tasks that require crisp edge inputs. In addition, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve promising performance on BSDS500, surpassing human accuracy when using standard criteria, and largely outperforming state-of-the-art methods when using more strict criteria. We further demonstrate the benefit of crisp edge maps for estimating optical flow and generating object proposals.

关键词： Edge detection edges feature map Boundary value problems Grain boundaries boundary layer Refining Religious Missions Edge of a figure or solid Leverage Optic Flow Resolving power standardized benchmark

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Latent Multi-view Subspace Clustering 30

Latent Multi-view Subspace Clustering

引用

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

作者： Zhang, Changqing Hu, Qinghua Fu, Huazhu Zhu, Pengfei Cao, Xiaochun Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore Singapore Chinese Acad Sci IIE State Key Lab Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781538604571

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and simultaneously performs data reconstruction based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict data themselves more comprehensively than each single view individually, accordingly makes subspace representation more accurate and robust as well. the proposed method is intuitive and can be optimized efficiently by using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

关键词： Linear programming Erbium Robustness Clustering methods Clustering algorithms Kernel Optimization

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Fuzzy-aided syntactic scene analysis

引用

7th International conference on Computational Science (ICCS 2007)

作者： Bielecka, Marzena Skomorowski, Marek AGH Univ Sci & Technol Dept Goeinformat & Appl Comp Sci Mickiewicza 30 PL-30059 Krakow Poland Jagiellonian Univ Inst Comp Sci Krakow Poland

ISBN: (纸本)9783540725855

In syntactic pattern recognition a pattern can be described by a graph. the problem of recognition is to determine if a pattern, represented by a describing graph, belongs to a language L(G), generated by a graph grammar G. the so-called IE graphs are used for pattern description. they are generated by so-called ETPL(k) graph grammars. the purpose of this paper is to present an idea of a new approach to syntactic recognition of fuzzy patterns represented by fuzzy IE graphs, followed the example of random IE graphs. this methodology can be used in embodied multi-agent systems for a scene analysis.

关键词： syntactic pattern recognition distorted patterns graph grammars

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Video Desnowing and Deraining Based on Matrix Decomposition 30

Video Desnowing and Deraining Based on Matrix Decomposition

引用

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

作者： Ren, Weihong Tian, Jiandong Han, Zhi Chan, Antoni Tang, Yandong 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

ISBN: (纸本)9781538604571

the existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene. One reason for the failure is due to the assumption that all the snowflakes/rain streaks are sparse in snow/rain scenes. the other is that the existing methods often can not differentiate moving objects and snowflakes/rain streaks. In this paper, we propose a model based on matrix decomposition for video desnowing and deraining to solve the problems mentioned above. We divide snowflakes/rain streaks into two categories: sparse ones and dense ones. With background fluctuations and optical flow information, the detection of moving objects and sparse snowflakes/rain streaks is formulated as a multi-label Markov Random Fields (MRFs). As for dense snowflakes/rain streaks, they are considered to obey Gaussian distribution. the snowflakes/rain streaks, including sparse ones and dense ones, in scene backgrounds are removed by low-rank representation of the backgrounds. Meanwhile, a group sparsity term in our model is designed to filter snow/rain pixels within the moving objects. Experimental results show that our proposed model performs better than the state-of-the-art methods for snow and rain removal.

关键词： Streak matrix decomposition Gaussian distribution Videotapes Moving Dense

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Empirical Minimum Bayes Risk Prediction: How to extract an extra few% performance from vision models with just three more parameters 27

Empirical Minimum Bayes Risk Prediction: How to extract an e...

引用

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

作者： Premachandran, Vittal Tarlow, Daniel Batra, Dhruv Natl Univ Singapore Singapore 117548 Singapore Microsoft Res Redmond WA USA Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9781479951178

When building vision systems that predict structured objects such as image segmentations or human poses, a crucial concern is performance under task-specific evaluation measures (e.g. Jaccard Index or Average Precision). An ongoing research challenge is to optimize predictions so as to maximize performance on such complex measures. In this work, we present a simple meta-algorithm that is surprisingly effective - Empirical Min Bayes Risk. EMBR takes as input a pre-trained model that would normally be the final product and learns three additional parameters so as to optimize performance on the complex high-order task-specific measure. We demonstrate EMBR in several domains, taking existing state-of-the-art algorithms and improving performance up to similar to 7%, simply with three extra parameters.

关键词： Image segmentation

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Sparse Quantization for Patch Description

Sparse Quantization for Patch Description

引用

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

作者： Boix, Xavier Gygli, Michael Roig, Gemma Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9780769549897

the representation of local image patches is crucial for the good performance and efficiency of many vision tasks. Patch descriptors have been designed to generalize towards diverse variations, depending on the application, as well as the desired compromise between accuracy and efficiency. We present a novel formulation of patch description, that serves such issues well. Sparse quantization lies at its heart. this allows for efficient encodings, leading to powerful, novel binary descriptors, yet also to the generalization of existing descriptors like SIFT or BRIEF. We demonstrate the capabilities of our formulation for both keypoint matching and image classification. Our binary descriptors achieve state-of-the-art results for two keypoint matching benchmarks, namely those by Brown [6] and Mikolajczyk [18]. For image classification, we propose new descriptors that perform similar to SIFT on Caltech101 [10] and PASCAL VOC07 [9].

关键词： compressed sensing computer vision image classification image coding image representation

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Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Description 30

Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Desc...

引用

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

作者： Zhang, Xishan Gao, Ke Zhang, Yongdong Zhang, Dongming Li, Jintao Tian, Qi Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX USA

ISBN: (纸本)9781538604571

Integrating complementary features from multiple channels is expected to solve the description ambiguity problem in video captioning, whereas inappropriate fusion strategies often harm rather than help the performance. Existing static fusion methods in video captioning such as concatenation and summation cannot attend to appropriate feature channels, thus fail to adaptively support the recognition of various kinds of visual entities such as actions and objects. this paper contributes to: 1) the first in-depth study of the weakness inherent in data-driven static fusion methods for video captioning. 2) the establishment of a task-driven dynamic fusion (TDDF) method. It can adaptively choose different fusion patterns according to model status. 3) the improvement of video captioning. Extensive experiments conducted on two well-known benchmarks demonstrate that our dynamic fusion method outperforms the state-of-the-art results on MSVD with METEOR scores 0.333, and achieves superior METEOR scores 0.278 on MSR-VTT-10K. Compared to single features, the relative improvement derived from our fusion method are 10.0% and 5.7% respectively on two datasets.

关键词： Visualization Dynamics Feature extraction Adaptation models Artificial neural networks Decoding

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7th chinese conference on pattern recognition, CCPR 2016

7th Chinese Conference on Pattern Recognition, CCPR 2016

引用

7th chinese conference on pattern recognition, CCPR 2016

ISBN: (纸本)9789811030017

the proceedings contain 62 papers. the special focus in this conference is on Robotics, computer vision and Basic theory of pattern recognition. the topics include: Constrained spectral clustering on face annotation system;axial-decoupled indoor positioning based on location fingerprints;an ensemble boosting clustering algorithm on uncertain objects;robust features of finger regions based hand gesture recognition using kinect sensor;circular object detection in polar coordinates for 2D LIDAR Data;intensity estimation of the real-world facial expression;spontaneous smile recognition for interest detection;road extraction based on direction consistency segmentation;an attention-based method for real-time hand tracking and fingertip detection in egocentric videos;recognition of social touch gestures using 3D convolutional neural networks;pose-invariant face recognition based on a flexible camera calibration;crowd collectiveness measure via path integral descriptor;robust face frontalization in unconstrained images;convolutional neural networks with neural cascade classifier for pedestrian detection;omega-shape feature learning for robust human detection;video object detection and segmentation based on proposal boxes;dual camera based feature for face spoofing detection;an improved background subtraction method based on vibe;a deep hashing method for people retrieval in video;direct discriminant analysis using volterra kernels for face recognition;real-time object tracking using dynamic measurement matrix;non-rigid 3D model retrieval based on weighted bags-of-phrases and LDA;a color model based fire flame detection system;online adaptive multiple appearances model for long-term tracking and kernel learning with Hilbert-Schmidt independence criterion.

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Object recognition from local scale-invariant features

Object recognition from local scale-invariant features

引用

Proceedings of the 1999 7th IEEE International conference on computer vision (ICCV'99)

作者： Lowe, David G. Univ of British Columbia Vancouver BC Canada

An object recognition system has been developed that uses a new class of local image features. the features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. these features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. the keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.

关键词： pattern recognition systems

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Robust Stereo Matching with an Unfixed and Adaptive Disparity Search Range 26

Robust Stereo Matching with an Unfixed and Adaptive Disparit...

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Liu, Jiazhi Liu, Feng Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Stereo matching is an essential basis for various applications in computer vision, but currently, most stereo matching methods have poor generalization performance and require a fixed disparity search range. In this work, we propose to adopt the operation "sub-pixel three-around-maximum", which supports an unfixed disparity search range, to replace the currently popular operation soft argmax. We also propose to directly supervise the feature extractor by three loss functions. Just depending on the feature extractor, we can obtain an accurate semi-dense disparity map before cost aggregation, which can help to remove adaptively the redundancy part of the predefined disparity search range. An adaptive disparity search range for each stereo pair can save much time and memory. the proposed architecture achieves state-of-the-art cross-domain generalization performance in the datasets KITTI2012&2015, and experimental results demonstrate that our method supports unfixed and adaptive disparity search range.

关键词： computer vision Costs Redundancy Memory architecture Feature extraction pattern recognition

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