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Hardware-Efficient Guided Image Filtering For Multi-Label Problem 30

Hardware-Efficient Guided Image Filtering For Multi-Label Pr...

引用

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

作者： Dai, Longquan Yuan, Mengke Li, Zechao Zhang, Xiaopeng Tang, Jinhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781538604571

the Guided Filter (GF) is well-known for its linear complexity. However, when filtering an image with an n-channel guidance, GF needs to invert an nxn matrix for each pixel. To the best of our knowledge existing matrix inverse algorithms are inefficient on current hardwares. this shortcoming limits applications of multichannel guidance in computation intensive system such as multi-label system. We need a new GF-like filter that can perform fast multichannel image guided filtering. Since the optimal linear complexity of GF cannot be minimized further, the only way thus is to bring all potentialities of current parallel computing hardwares into full play. In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance. Specifically, in order to boost the filtering performance, HGF takes a new matrix inverse algorithm which only involves two hardware-efficient operations: element-wise arithmetic calculations and box filtering. In order to break the linear model restriction, HGF synthesizes a polynomial multichannel guidance to introduce nonlinearity. Benefiting from our polynomial guidance and hardware-efficient matrix inverse algorithm, HGF not only is more sensitive to the underlying structure of guidance but also achieves the fastest computing speed. Due to these merits, HGF obtains state-of-the-art results in terms of accuracy and efficiency in the computation intensive multi-label systems.

关键词： image filtering Guidance Interleukin-6 SOS1 gene HGF gene Hepatocyte Growth Factor Filtration Inverse matrix Filters arithmetic Economic Resources linear complexity

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Multi-Context Attention for Human Pose Estimation 30

Multi-Context Attention for Human Pose Estimation

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

作者： Chu, Xiao Yang, Wei Ouyang, Wanli Ma, Cheng Yuille, Alan L. Wang, Xiaogang Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China Johns Hopkins Univ Baltimore MD USA Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781538604571

In this paper, we propose to incorporate convolutional neural networks with a multi-context attention mechanism into an end-to-end framework for human pose estimation. We adopt stacked hourglass networks to generate attention maps from features at multiple resolutions with various semantics. the Conditional Random Field (CRF) is utilized to model the correlations among neighboring regions in the attention map. We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on detailed descriptions for different body parts. Hence our model has the ability to focus on different granularity from local salient regions to global semantic-consistent spaces. Additionally, we design novel Hourglass Residual Units (HRUs) to increase the receptive field of the network. these units are extensions of residual units with a side branch incorporating filters with larger receptive field, hence features with various scales are learned and combined within the HRUs. the effectiveness of the proposed multi-context attention mechanism and the hourglass residual units is evaluated on two widely used human pose estimation benchmarks. Our approach outperforms all existing methods on both benchmarks over all the body parts. Code has been made publicly available.

关键词： Pose estimation Visualization Semantics Correlation Biological system modeling Convolutional codes

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Machine vision for Coin recognition with ANNs: Effect of Training and Testing Parameters 18th

引用

18th International conference on Engineering Applications of Neural Networks (EANN)

作者： Chauhan, Vedang Joshi, Keyur D. Surgenor, Brian Queens Univ Kingston ON K7L 3N6 Canada

ISBN: (纸本)9783319651729;9783319651712

pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data for object recognition, classification and computer vision segmentation. Features are extracted from input data and used for object classification purposes. Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) are popular tools for pattern recognition applications. the performance of the networks is usually defined in terms of the classification accuracy. However, there are no real design guidelines for training and testing protocols. this research set out to evaluate the effect on accuracy of the design parameters, including: size of the database, number of classes, quality of images, type of network, nature of training and testing strategy. A coin recognition task was used for the evaluation. A set of guidelines for part recognition tasks is presented based on experience with this task.

关键词： pattern recognition Machine learning Object detection and classification ANNs DNNs Testing protocols

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CNet: Context-Aware Network for Semantic Segmentation 4

CNet: Context-Aware Network for Semantic Segmentation

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4th IAPR Asian conference on pattern recognition (ACPR)

作者： Cheng, Rongliang Zhang, Junge Yang, Peipei Liu, Kangwei Zhang, Shujun Qingdao Univ Sci & Technol Coll Informat Sci & Technol Qingdao Peoples R China Chinese Acad Sci CASIA FF & LeFuture AI Inst Beijing Peoples R China Chinese Acad Sci CASIA CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA NLPR Inst Automat Beijing Peoples R China

ISBN: (纸本)9781538633540

Semantic segmentation is one of the great challenges in computer vision. Recently, deep convolutional neural networks (DCNNs) have achieved great success in most of the computer vision tasks. However, in terms of semantic segmentation, it is still difficult for the DCNN methods to take full advantage of context information and determine the fine boundaries of objects. In this paper, we propose a Context-aware Network (CNet), which utilizes robust context information to improve segmentation results. CNet has two significant components: 1) a feature collection module (FCM), which is constructed to extract low-level contextual features including texture, layout, boundary, local and global relationships by different receptive fields to complement high-level feature learning, and 2) a novel layer named ResGate, which is developed to select robust contextual features from the FCM. the two combined components can thoroughly explore context information to improve boundary segmentation accuracy. We evaluate the proposed method on the popular PASCAL VOC2012 dataset, and obtain promising performance compared with related methods, especially in the situation of similar objects or objects in complex scene.

关键词： Semantic Segmentation CNN Contextual Feature

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Multimodal Gesture recognition Based on the ResC3D Network 16

Multimodal Gesture Recognition Based on the ResC3D Network

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16th IEEE International conference on computer vision (ICCV)

作者： Miao, Qiguang Li, Yunan Ouyang, Wanli Ma, Zhenxin Xu, Xin Shi, Weikang Cao, Xiaochun Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China

ISBN: (纸本)9781538610343

Gesture recognition is an important issue in computer vision. Recognizing gestures with videos remains a challenging task due to the barriers of gesture-irrelevant factors. In this paper, we propose a multimodal gesture recognition method based on a ResC3D network. One key idea is to find a compact and effective representation of video sequences. therefore, the video enhancement techniques, such as Retinex and median filter are applied to eliminate the illumination variation and noise in the input video, and a weighted frame unification strategy is utilized to sample key frames. Upon these representations, a ResC3D network, which leverages the advantages of both residual and C3D model, is developed to extract features, together with a canonical correlation analysis based fusion scheme for blending features. the performance of our method is evaluated in the Chalearn LAP isolated gesture recognition challenge. It reaches 67.71% accuracy and ranks the 1st place in this challenge.

关键词： Videos Feature extraction Gesture recognition Lighting Correlation Hidden Markov models

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A COMPLETE CANONICAL CORRELATION ANALYSIS FOR MULTIVIEW LEARNING

A COMPLETE CANONICAL CORRELATION ANALYSIS FOR MULTIVIEW LEAR...

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IEEE International conference on Image Processing

作者： Yan Liu Yun Li Yun-Hao Yuan School of Information Engineering Yangzhou University Yangzhou China

Canonical correlation analysis (CCA) is an effective feature learning method, which has wide applications in pattern recognition and computer vision. However, CCA considers the correlation only between the one-to-one aligned samples in two views, ignoring the correlation between all the samples sharing the same label. In this paper, we propose a deep complete canonical correlation analysis (Deep Complete-CCA), which learns the relationships between all pairwise correspondences of sample points in the same classes. Unlike CCA, our method can learn discriminant representations that maximize the correlation between the two views while segregating the different classes on the learned space. We test Deep Complete-CCA on handwriting recognition and speech based emotion recognition using two popular MNIST and RAVDESS datasets. Experimental results show that our proposed method can obtain better performances than several related algorithms.

关键词： Correlation Machine learning Kernel Optimization Eigenvalues and eigenfunctions Learning systems pattern recognition

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Using Original Face Image and Its Virtual Image for Face recognition 7th

Using Original Face Image and Its Virtual Image for Face Rec...

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

作者： Wang, Jianguo Fu, Shucai Tangshan Coll Tangshan 063000 Peoples R China

ISBN: (纸本)9783319677774;9783319677767

In this paper, we propose an approach to produce the half image of the face and integrate the original face image and its half image for representation-based face recognition. Because we take the geometrical nature of faces into full account, the proposed approach not only is quite simple but also can well represent possible changes in illuminations, facial expressions and poses of faces. Extensive experiments conducted on face databases (Yale, FERET face databases) show that the proposed one can greatly improve the accuracy of the representation-based classification approaches, particularly in small sample sizes.

关键词： Face recognition pattern recognition Half image Sparse representation

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Research on pedestrian detection technology based on improved DPM model 7

Research on pedestrian detection technology based on improve...

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

作者： Cai, Yingfeng Liu, Ze Sun, Xiaoqiang Chen, Long Wang, Hai Jiangsu Univ Inst Automot Engn Zhenjiang Jiangsu Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781538604908

Pedestrian detection plays an important role in unmanned technology. Because of the high efficiency and robustness of the deformable part model, it has been widely applied to the field of pedestrian detection. At present, how to effectively reduce the risk of partial screening of pedestrians has been based on the pattern recognition of pedestrian detection technology in the hot spots. Aiming at this problem, after analyzing the deformable part model deeply, this paper creatively proposes a pedestrian detection method with improved deformable part model. By training the two-pedestrian deformable part model, the method is adopted to reduce the pedestrian detection in the pedestrian detection by matching the image sub-region and matching the matching result. It is shown that the method can improve the detection efficiency while ensuring the detection efficiency, while ensuring the effectiveness of the whole algorithm to meet the real-time requirements of unmanned technology.

关键词： Deformable models Feature extraction Training conferences pattern recognition computer vision Classification algorithms"

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Recent Progress of Face Image Synthesis 4

Recent Progress of Face Image Synthesis

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4th IAPR Asian conference on pattern recognition (ACPR)

作者： Lu, Zhihe Li, Zhihang Cao, Jie He, Ran Sun, Zhenan CASIA Natl Lab Pattern Recognit Beijing Peoples R China CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781538633540

Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photorealistic face images via given semantic domain. It has been a crucial prepossessing step of main-stream face recognition approaches and an excellent test of AI ability to use complicated probability distributions. In this paper, we provide a comprehensive review of typical face synthesis works that involve traditional methods as well as advanced deep learning approaches. Particularly, Generative Adversarial Net (GAN) is highlighted to generate photo-realistic and identity preserving results. Furthermore, the public available databases and evaluation metrics are introduced in details. We end the review with discussing unsolved difficulties and promising directions for future research.

关键词： Face Gallium nitride three-dimensional displays Hidden Markov models Generative adversarial networks Computational modeling Shape

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Local Fractional Order Derivative Vector Quantization pattern for Face recognition

Local Fractional Order Derivative Vector Quantization Patter...

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13th Asian conference on computer vision (ACCV)

作者： Li, Jing Sang, Nong Gao, Changxin Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Sch Automat Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783319541877;9783319541860

Previous works have shown that fractional order derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel local image descriptor, local fractional order derivative vector quantization pattern (fVQP), based on image local directional fractional order derivative feature vector and vector quantization method for face recognition. Compared with image integral order derivative information based local binary pattern (LBP), local derivative pattern (LDP) and local directional derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.

关键词： Vector quantization

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