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arXiv 2018年

作者： XIONG, FU XIAO, YANG CAO, ZHIGUO GONG, KAICHENG FANG, ZHIWEN ZHOU, JOEY TIANYI National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Automation Huazhong University of Science and Technology School of Energy and Mechanical-electronic Engineering Hunan University of Humanities Science and Technology Loudi China Institute of High Performance Computing A STAR Singapore

Person re-identification is indeed a challenging visual recognition task due to the critical issues of human pose variation, human body occlusion, camera view variation, etc. To address this, most of the state-of-theart approaches are proposed based on deep convolutional neural network (CNN), being leveraged by its strong feature learning power and classification boundary fitting capacity. Although the vital role towards person re-identification, how to build effective CNN baseline model has not been well studied yet. To answer this open question, we propose 3 good practices in this paper from the perspectives of adjusting CNN architecture and training procedure. In particular, they are adding batch normalization after the global pooling layer, executing identity categorization directly using only one fully-connected, and using Adam as optimizer. The extensive experiments on 3 widely-used benchmark datasets demonstrate that, our propositions essentially facilitate the CNN baseline model to achieve the state-of-the-art performance without any other highlevel domain knowledge or low-level technical trick. Copyright © 2018, The Authors. All rights reserved.

关键词： Domain Knowledge

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Action recognition for depth video using multi-view dynamic images

arXiv

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arXiv 2018年

作者： Xiao, Yang Chen, Jun Wang, Yancheng Cao, Zhiguo Zhou, Joey Tianyi Bai, Xiang National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Automation Huazhong University of Science and Technology Wuhan Hubei430074 China Institute of High Performance Computing A*STAR Singapore School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan Hubei430074 China

Dynamic imaging is a recently proposed action description paradigm for simultaneously capturing motion and temporal evolution information, particularly in the context of deep convolutional neural networks (CNNs). Compared with optical flow for motion characterization, dynamic imaging exhibits superior efficiency and compactness. Inspired by the success of dynamic imaging in RGB video, this study extends it to the depth domain. To better exploit three-dimensional (3D) characteristics, multi-view dynamic images are proposed. In particular, the raw depth video is densely projected with respect to different virtual imaging viewpoints by rotating the virtual camera within the 3D space. Subsequently, dynamic images are extracted from the obtained multi-view depth videos and multi-view dynamic images are thus constructed from these images. Accordingly, more view-tolerant visual cues can be involved. A novel CNN model is then proposed to perform feature learning on multi-view dynamic images. Particularly, the dynamic images from different views share the same convolutional layers but correspond to different fully connected layers. This is aimed at enhancing the tuning effectiveness on shallow convolutional layers by alleviating the gradient vanishing problem. Moreover, as the spatial occurrence variation of the actions may impair the CNN, an action proposal approach is also put forth. In experiments, the proposed approach can achieve state-of-the-art performance on three challenging datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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Inter-frame Correlation Based on Moving Vehicle Target Detection in Infrared Image Sequences

Inter-frame Correlation Based on Moving Vehicle Target Detec...

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International Conference on Computer technology, Electronics and Communication

作者： Jin Lin Yihua Tan Jinwen Tian National Key Laboratory of Science & Technology on Multi-Spectral Information Processing Huazhong University of Science & Technology Wuhan China

ISBN: (纸本)9781538657850

Moving vehicle targets detection in infrared image sequences is playing a more and more important role in infrared search and track systems. This paper presents a novel method based on inter-frame correlation to detect moving vehicle target in infrared image sequences reliably. Firstly, for a single frame, the image respectively is sharpened and enhanced after image denoising, and then generating the preprocessed image. Secondly, the vehicle targets in infrared image sequences are detected by a saliency based target detection algorithm. For consecutive frames, features of motion between real vehicle targets and false ones are different, then the inter-frame correlation is operated to suppress the false alarm, making the vehicle targets detection more accurate. The experiments on the image sequences demonstrate that the proposed method has ensure detection effectiveness and robustness for vehicle detection in infrared image sequences under complex backgrounds, and it also improve the reliability.

关键词： Moving vehicle detection Infrared image Inter-frame correlation Saliency

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Small moving target detection model based on improved mixed disturbance gauss model

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Telkomnika (Telecommunication Computing Electronics and Control) 2016年第3A期14卷 229-236页

作者： Bai, Ting Tian, Jinwen Sun, Xiao National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Automation Huazhong University of Science and Technology Wuhan430074 China

In order to obtain ideal moving target detection results, a kind of moving target detection algorithm of improved mixture Gaussian model is proposed in this paper on the basis of analyzing the deficiency of traditional Mixture Gaussian model. Firstly, in this algorithm, the update rate is increased to better adapt to the change of environment and the idea of pixel connected region is introduced to remove the noise, then the invariance property of chroma is adopted to avoid arising of "shadow" phenomenon;finally, simulation comparison experiment is used to test its performance. The experiment results show that this algorithm can generate reliable background image and accurately and completely detect the moving target to obtain more superior detection results of comparison algorithm, which can meet the practical application requirement of target tracking. © 2016 Universitas Ahmad Dahlan. All rights reserved.

关键词： Target tracking

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A Subpixel Target Detection Approach to Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2017年第9期55卷 5093-5114页

作者： Xue, Bai Yu, Chunyan Wang, Yulei Song, Meiping Li, Sen Wang, Lin Chen, Hsian-Min Chang, Chein-I Center for Hyperspectral Imaging in Remote Sensing Information and Technology College Dalian Maritime University Dalian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Key Laboratory of Spectral Imaging Technology Chinese Academy of Sciences Xi'an China State Key Laboratory of Integrated Services Networks Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science and Information Management Providence University Taichung02912 Taiwan

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. © 1980-2012 IEEE.

关键词： Support vector machines

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Research on Adaptation Criteria Generation based on Large Data Mining 7

Research on Adaptation Criteria Generation based on Large Da...

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2016 IEEE Chinese Guidance, Navigation and Control Conference (IEEE CGNCC2016)

作者： Yun Hong-quan XU Li DOU Hao MING De-lie National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute State Key Laboratory for Multi-spectral Information Processing Technologies School of AutomationHuazhong University of Science and Technology

ISBN: (纸本)9781467383196

This paper performs research on adaptation analysis based on large amount of multi-source remote sensing *** view of different demands from different task background,the research is firstly focused on how to analyze the data in computer *** achieve this,the feature parameters of target areas are extracted from different target area geographic *** combination of ORACLE database engine,data mining technology is used to carry out the target area adaptation assessment,and extract corresponding adaptation *** test the trained adaptation criteria on multi-source geographic information data of different target *** results show that the resulting criterion has certain coincidence rate and robustness.

关键词： image Research on Adaptation Criteria Generation based on Large Data Mining

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TasselNet: Counting maize tassels in the wild via local counts regression network

arXiv

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arXiv 2017年

作者： Lu, Hao Cao, Zhiguo Xiao, Yang Zhuang, Bohan Shen, Chunhua National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Automation Huazhong University of Science and Technology Wuhan430074 China School of Computer Science University of Adelaide AdelaideSA5005 Australia

Background: Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. Results: This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed Tasselnet is proposed. Tasselnet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that Tasselnet outperforms other state-of-the-art approaches by large margins and achieves the over

关键词： Computer vision

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Millimeter-wave emission characteristics of bilayer radar-infrared compound stealth material

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Chinese Optics Letters 2016年第6期14卷 100-104页

作者：程亚运胡飞贺锋吴量何小琴 School of EIC Huazhong University of Science and Technology Wuhan 430074 China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Wuhan 430074 China

To achieve radar and infrared stealth, an infrared stealth layer is usually added to the radar absorbing material（RAM） of stealth aircraft. By analyzing the millimeter-wave（MMW） emissivities of three stealth materials, this Letter investigates the impact of the added infrared stealth layer on the originally ＂hot＂ MMW emission of RAM. The theoretical and measured results indicate that, compared with the monolayer RAM, the MMW emission of the bilayer material is still strong and its emissivity is reduced by 0.1–0.2 at almost every incident *** results partially demonstrate the feasibility of detecting stealth aircraft coated with this bilayer stealth material.

关键词： aircraft radiometer originally detecting coated millimeter incident monolayer partially brightness

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Passive millimeter-wave scene imaging simulation based on fast ray-tracing

Passive millimeter-wave scene imaging simulation based on fa...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bo Qi Liang Lang Yayun Cheng Siyuan Liu Fei Hu Xiaoqin He Pengying Deng Liangqi Gui School of Electronic Information and Communications National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Wuhan China

Scene imaging simulation is an indispensable work in passive millimeter-wave object detection and remote sensing. In this paper, a 3-D scene simulation model of passive millimeter-wave imaging based on ray-tracing is presented, with consideration of the essential affecting factors such as multiple reflections, sky radiation, polarization rotation and antenna pattern smoothing. In order to accelerate the ray-tracing process in simulation model, a fast ray-triangle intersection algorithm is adopted. The outdoor imaging experiment and scene imaging simulation are carried out to validate the presented method at 94GHz. From the results, it is shown that the speed of scene imaging simulation process is significantly improved and the simulated image is in good agreement with the measured brightness temperature image.

关键词： Imaging Brightness temperature Millimeter wave technology Radiometry Ray tracing Atmospheric modeling Temperature measurement

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FINE-GRAINED MAIZE CULTIVAR IDENTIFICATION USING FILTER-SPECIFIC CONVOLUTIONAL ACTIVATIONS

FINE-GRAINED MAIZE CULTIVAR IDENTIFICATION USING FILTER-SPEC...

引用

IEEE International Conference on Image processing

作者： Hao Lu Zhiguo Cao Yang Xiao Zhiwen Fang Yanjun Zhu National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Automation Huazhong University of Science and Technology

ISBN: (纸本)9781467399623

Cultivar identification is an important aspect in agriculture and also a typical task of fine-grained visual categorization (FGVC). In comparison with other common topics in FGVC, studies on this problem are somewhat lagged and limited. In this paper, targeting four Chinese maize cultivars of Jundan No.20, Wuyue No.3, Nongda No.108, and Zhengdan No.958, we first consider the problem of identifying the maize cultivar based on its tassel characteristics. Technically, an effective convolutional neural network (CNN) based feature encoding pipeline that allows integration of deep CNN based column feature extraction, filter-specific Fisher vector (FV) encoding and mutual information (MI) based filter selection is proposed to better address this problem. In particular, a novel fine-grained maize cultivar identification dataset termed MCI-4000 that contains 4000 images is first constructed by our team. Experimental results demonstrate that our method outperforms other stat-of-the-art approaches by at least 5% in accuracy. We also show that, there exists redundant filters in the last convolutional layer, and high accuracy can be achieved with only relatively low-dimensional column features and a small number of Gaussian components in FV.

关键词： Fine-grained visual categorization Maize cultivar identification Convolutional neural network Fisher vector Mutual information Zea mays cultivar identification Activation

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