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IEEE International Conference on image processing

作者： Shaoyong Jia Shijie Geng Yun Gu Jie Yang Pengfei Shi Yu Qiao Institue of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781479983407

Superpixels become more and more popular as image preprocessing step in computer vision applications. In this paper, we propose an improved simple linear iterative clustering (SLIC) superpixel approach based on nonstationarity measure (NSM), which is called nSLIC. An adjustive distance measure is developed in the five-dimensional [labxy] space. The nSLIC superpixel replaces the predefined fixed value of compactness parameter by the nonstationarity measure map of each image, which exploits the image information and is therefore adaptive to the color feature of the image. It also avoids the difficulty of pre-setting compactness parameter and reduces the parameters needed setting to only one indeed. The nSLIC superpixel improves not only segmentation quality bust also computational efficiency by the way of achieving faster convergence. Experiments done on BSD500 dataset show that nSLIC adheres better to image edges meanwhile producing regular and compact superpixels as much as possible, compared to various popular versions of SLIC.

关键词： Nonstationarity measure NSLIC Superpixel images color feature imagery (Psychotherapy) image information distance measurement compactness computer vision image edge image Self tuning slicking NSM

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RERANKING OF PERSON RE-IDENTIFICATION BY MANIFOLD-BASED APPROACH

RERANKING OF PERSON RE-IDENTIFICATION BY MANIFOLD-BASED APPR...

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IEEE International Conference on image processing

作者： Shuai Huang Yun Gu Jie Yang Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781479983407

Person re-identification (RE-ID) aims at associating the same pedestrian over non-overlapping surveillance scenes. A large number of approaches have emerged in recent years, and they mainly focus on designing middle or high level features to highlight the most discriminative aspects of pedestrians. Due to the nonrigid structure of pedestrians, it is difficult to reidentify pedestrians by low-level features. We investigate the results of conventional person RE-ID approaches, and find that the inadequate utilization of low-level features lead to the poor performance. In this work, we propose a novel framework to utilize the low-level visual features in a more effective way. Given a result obtained from the conventional person RE-ID method, the framework returns a more reasonable result. The framework is extended from the manifold ranking method, and several adjustments are made taking the requirements of person RE-ID into consideration. Our framework is validated through experiments on two person RE-ID datasets (VIPeR and ETHZ), and results from four different conventional approaches show significant improvement.

关键词： Re-identification Manifold Reranking Manifolds Pedestrians LOW LEVEL Persons Frameworks

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A novel fast full frame video stabilization via three-layer model 21

A novel fast full frame video stabilization via three-layer ...

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Long, Wei Yang, Jie Song, Dacheng Chen, Xiaogang He, Xiangjian Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China University of Shanghai for Science and Technology Shanghai China School of Computing and Communications University of Technology Sydney Australia

ISBN: (纸本)9783319144443

Video stabilization is an important video enhancement technology which aims at removing undesired shaking from input videos. A challenging task in stabilization is to inpaint the missing pixels of undefined areas in the motion-compensated frames. This paper describes a new video stabilization method. It adopts a multi-layer model to improve the efficiency of the video stabilization. The undefined areas can be inpainted in real-time. Compared with traditional methods, our proposed algorithm only need maintain a single updated mosaic image for video completion, while previous methods require to store all neighboring frames and then registered with the current frame. The experimental results demonstrated the effectiveness of the proposed approach. © Springer International Publishing Switzerland 2015.

关键词： Motion compensation

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RGB-D saliency detection via mutual guided manifold ranking

RGB-D saliency detection via mutual guided manifold ranking

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IEEE International Conference on image processing

作者： Haoyang Xue Yun Gu Yijun Li Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Visual saliency detection has gained its popularity in computer vision in recent years. Depth information is proven as a fundamental element of human vision while it is underutilized in existing saliency detection approaches. In this paper, an effective visual object saliency detection model via RGB and depth cues mutual guided manifold ranking is proposed. The depth features are extracted to guide the saliency ranking of RGB image while the RGB saliency is used as the guide of depth map ranking as well. We obtain the final result by fusing the RGB and depth saliency maps. The experimental result on a benchmark dataset which contains 1000 RGB-D images demonstrates the effectiveness and superior performance compared with several state-of-art methods.

关键词： Feature extraction image color analysis Manifolds Visualization Shape image segmentation Weight measurement

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CO-SALIENCY DETECTION VIA SIMILARITY-BASED SALIENCY PROPAGATION

CO-SALIENCY DETECTION VIA SIMILARITY-BASED SALIENCY PROPAGAT...

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IEEE International Conference on image processing

作者： Chenjie Ge Keren Fu Yijun Li Jie Yang Pengfei Shi Li Bai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University University of Nottingham

ISBN: (纸本)9781479983407

In this paper, we present a method for discovering the common salient objects from a set of images. We treat co-saliency detection as a pairwise saliency propagation problem, which utilizes the similarity between each pair of images to measure the common property with the guidance of a single saliency map image. Given the pairwise co-salient foreground maps, pairwise saliency is optimized by combining the initial background cues. Pairwise co-salient maps are then fused according to a novel fusion strategy based on the focus of human attention. Finally we adopt an integrated multi-scale scheme to obtain the pixel-level saliency map. Our proposed model makes the existing single saliency model perform well in co-saliency detection and is not overly sensitive to the initial saliency model selected. Extensive experiments on two benchmark databases show the superiority of our co-saliency model against the state-of-the-art methods both subjectively and objectively.

关键词： Co-saliency detection Pairwise saliency propagation Optimization Attention based fusion Pairwise images maps Common property resources propagation focused attention Guidance

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GRIB Parallel Design of Civil Aviation Meteorological Data processing System

GRIB Parallel Design of Civil Aviation Meteorological Data P...

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第十四届分布式计算及其应用国际学术研讨会

作者： Zhengwei Guo Yongwei Gao Yafei Jiang Guang Xue School of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University

With the increasing volume of business the civil aviation data processing system, the data processing efficiency requirements become higher and higher. In order to improve the speed of data processing, we design a parallel scheme of GRIB according to the GRIB data processing flow. We achieved the parallel processing of GRIB data reading and decoding process, and carried out the related test. It is significantly improve the speed of data loading.

关键词： Civil Aviation Meteorological Data processing System GRIB Multithreading Parallel design

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Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

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High Technology Letters 2014年第1期20卷 34-41页

作者：胡振涛 Liu Xianxing Li Jie Institute of Image Processing and Pattern Recognition Henan University

The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： multi-sensor information fusion particle filter weight optimization predictionand update

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A MAP framework for single-image deblurring based on sparse priors

A MAP framework for single-image deblurring based on sparse ...

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Chinese Automation Congress (CAC)

作者： Cheng Zhu Yue Zhou Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Blind image restoration is a typically ill-posed problem, many methods tend to construct the loss function using the latent image and blur kernel priors. In this paper, we propose a MAP framework for single image motion deblurring by introducing a constrained regularization of approximate L0 and L1 sparsity respectively for latent image and motion kernel, and the optimization is conducted by fast numerical approaches. The proposed scheme is shown to be robust and effective by the experiments on both synthesized and real images. The results and comparisons to the state-of-the-art methods will be displayed.

关键词： Kernel Mathematical model image restoration Convolution Estimation Optimization Standards

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Combining local binary patterns for scene recognition

Journal of Software

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Journal of Software 2014年第1期9卷 203-210页

作者： Song, Minguang Guo, Ping Image Processing Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Difference Binary pattern (LDBP) also was proposed and performed better than that of PACT. LDBP is based on the comparisons between center pixel and its neighboring pixels, but the relationship among neighbor pixels is not considered. In this paper, we propose to combine Local Neighbor Binary pattern (LNBP) with LDBP to construct a spatial representation for scene recognition, because that LNBP can provide complementary information regarding neighboring pixels for LDBP. Experiments on widely used datasets demonstrate that the performance of image recognition is further improved with proposed method. © 2014 ACADEMY PUBLISHER.

关键词： Pixels

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Framelet-based sparse regularization for uneven intensity correction of remote sensing images in a retinex variational framework

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Optik 2016年第3期127卷 1184-1189页

作者： Xia Lan Zhiyong Zuo Huanfeng Shen Liangpei Zhang Jing Hu The 10th Institute of China Electronics Technology Group Corporation Chengdu 610036 China The School of Resource and Environmental Sciences Wuhan University Wuhan 430079 China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China National Key Laboratory of Science and Technology on Multispectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

Correcting uneven intensity distribution from a single image has long been a challenging problem with remote sensing image. In this paper, an analysis-based sparse prior is employed in the retinex variational framework for the uneven intensity correction of remote sensing images. This sparse regularization model is used to adjust uneven intensity by regularizing the sparsity of the reflectance component under framelet transform. Furthermore, the alternating minimization algorithm and split Bregman method are adopted to solve the framelet-based sparse regularization model. The experiments, with both simulated images and real-life images, show that the proposed model can effectively correct the uneven intensity distribution.

关键词： Framelet Retinex Uneven intensity correction Remote sensing image Split Bregman algorithm

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