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arXiv 2018年

作者： Huang, Yi-Jie Dou, Qi Wang, Zi-Xian Liu, Li-Zhi Jin, Ying Li, Chao-Feng Wang, Lisheng Chen, Hao Xu, Rui-Hua Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Imsight Medical Technology Co. Ltd. China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China

Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localization-segmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detail-preserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available. Copyright ©

关键词： Signal encoding

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Convolution neural network for automatic facial expression recognition

Convolution neural network for automatic facial expression r...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Chen, Xiaoguang Yang, Xuan Wang, Maosen Zou, Jiancheng Institute of Image Processing and Pattern Recognition North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing China School of Compute Science and Techonology Beijing University of Posts and Telecommunications No.10 Xitucheng Road Haidian District Beijing China

ISBN: (纸本)9781509048977

Facial expression recognition is a hot research direction of pattern recognition and computer vision. It has been increasingly used in artificial intelligence, human-computer interaction and security monitoring in recent years. Convolution neural network (CNN) as a depth learning architecture can extract the essential features of the image, and in the case of large changes in shooting conditions, its effect is better than the traditional methods of Support Vector Machines(SVM) and Principal Component Analysis(PCA). Therefore, an improved method of facial expression recognition based on CNN is proposed in this paper. The purpose is to classify each facial image as one of the seven facial expressions considered in this study. According to the characteristics of facial expression recognition, a new convolution neural network structure is designed which uses convolution kernel to extract implicit features and max pooling to reduce the dimensions of the extracted implicit features. In comparison to AlexNet network, we can improve the recognition accuracy about 4% higher on the CK+ facial expression database by the aid of Batch Normalization (BN) layer to our network. A facial expression recognition system is constructed in this paper for the convenience of application, and the experimental results show that the system could reach the real-Time needs. © 2017 IEEE.

关键词： Principal component analysis

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A reduced-reference image quality assessment model based on joint-distribution of neighboring LOG signals 12

A reduced-reference image quality assessment model based on ...

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Digital Photography and Mobile Imaging XII 2016

作者： Chen, Congmin Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University China

Previous work have validated that the output of retinal ganglion cells in human visual pathway, which can be modeled as an LOG (Laplacian of Gaussian) filtration, can whiten the power spectrum of not only the natural images, but also the distorted images, hence the first-order (average luminance) and the secondorder (contrast) redundancies have been removed when applying the LOG filtration. Considering the fact that human vision system (HVS) always ignores the first-order and the second-order information when sensing image local structures, the LOG signals should be efficient features in IQA (image quality assessment) task and a lot of LOG based IQA models have been proposed. In this paper, we focus on an interesting question that has not been investigated carefully yet: what is an efficient way to represent image structure features that is perceptual quality aware based on the LOG signals. We examine the relationship between neighboring LOG signals and propose to represent the relationship by computing the joint distribution of neighboring LOG signals, and thus propose a set of simple but efficient RR IQA feature and consequently yield an excellent RR IQA model. Experimental results on three large scale subjective IQA databases show that our proposed method works robustly across different databases and stay in the state-of-the-art RR IQA models. © 2016 Society for Imaging Science and Technology.

关键词： Laplace transforms

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Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

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Procedia Computer Science 2017年 115卷 440-447页

作者： Gajanan M. Galshetwar Laxman M. Waghmare Anil B. Gonde Subrahmanyam Murala Center of Excellence in Signal and Image Processing (COESIP) Department of ECE SGGSIET Nanded Maharashtra 431606 India Computer Vision and Pattern Recognition Laboratory Department of Electrical Engineering IIT Ropar Rupnagar 140001 India

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary patterns (LBPs) are calculated using the salient point as a center pixel. Our approach enhanced the results as we used color features in combination with LBP features. Experimentation is carried out on MESSIDOR database of 1200 retinal images, proposed approach has average precision of 57.82% as compared to the earlier approach whose average precision is 53.70%.

关键词： Content-Based image retrieval (CBIR) Diabetic Retinopathy (DR) Edgy Salient points Local Binary patterns (LBPs)

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Person Re-identification by Integrating Static Texture and Shape Cues 12th

Person Re-identification by Integrating Static Texture and S...

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12th Chinese Conference on Biometric recognition, CCBR 2017

作者： Madongo, Canaan Tinotenda Huang, Di Chen, Jiaxin Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9783319699226

Person Re-Identification (Re-ID) is a challenging task with wide ranging applications in various fields. This paper presents a novel hand-crafted method for this issue, enhancing the state of the art ones in literature in two aspects. In contrast to most current studies that analyze texture features, we propose a discriminative and compact shape feature by applying Procrustes shape analysis. It not only retains shape distinctiveness of an individual sample, but also alleviates cross-view impacts. Furthermore, we combine the shape feature with some current popular texture features, namely LOMO and mid-level filters, so that the advantages of multiple clues can be jointly used. A score level fusion strategy is finally adopted to optimally integrate their credits. Evaluated on two public benchmarks, i.e. VIPeR and CUHK03, the proposed method achieves very competitive results, indicating its effectiveness in person Re-ID. © 2017, Springer International Publishing AG.

关键词： Computers

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A mammography-based classification method for breast masses using SVM

Boletin Tecnico/Technical Bulletin

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Boletin Tecnico/Technical Bulletin 2017年第12期55卷 1-9页

作者： Liang, Jingjing Lan, Yihua Li, Zhengyan Bai, Kun Zhang, Xingang School of Computer and Information Technology Nanyang Normal University Nanyang473061 China Institute of Image Processing and Pattern Recognition Nanyang Normal University Nanyang473061 China

A significant step in the computer-aided diagnosis of mammary cancer is identifying the suspected masses. The sample data is used for training the classifier before the cases to be measured are being identified. Therefore, the design of classifier greatly affects the overall performance of the system. Since the number of samples is commonly limited, we extract high-precision feature values to better identify the suspected masses. A nonlinear classifier SVM is introduced into this system, and it is compared with Fisher in classifying effects by carrying out a series of experiments. The Receiver Operating Characteristic Curve (ROC) shows that Az value has reached up to *** experiment results prove that the application of this classifier in identifying and classifying suspected breast lumps helps improve the overall performance of the computer-aided diagnosis system.

关键词： Computer aided diagnosis

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SAR and optical image registration based on edge features

SAR and optical image registration based on edge features

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International Conference on Systems and Informatics (ICSAI)

作者： Donghao Shen Junhao Zhang Jie Yang Deying Feng Jiang Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Liaocheng University China Shanghai Xinyue Instrument Factory China

As synthetic aperture radar (SAR) data is more and more popular in recent days, the fusion of SAR and optical image become inevitable and desirable problem. image Registration is the fundamental for data fusion, and has been wildly studied. However, literature on registration between SAR and optical image remain little, which means the problem is still challenge and open. On one hand, the SAR and optical image are from totally different sensor with variant imaging mechanism. On the other hand, Simple features could not enough to maintain the accuracy and efficiency of multi-modal registration. We propose a new method for SAR and optical image registration. Different from current methods mainly based on intensity feature or point feature, our method matches two images by their boundary features. A globalized boundary detection algorithm are used for feature extraction. The extracted boundaries were then matched by coherence point drift algorithm. Experiments prove that our method could achieve pixel-level precision, and perform robust for different resolutions.

关键词： Optical imaging Synthetic aperture radar Optical sensors Adaptive optics image registration image edge detection Feature extraction

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A novel color image encryption algorithm based on genetic recombination and the four-dimensional memristive hyperchaotic system

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Chinese Physics B 2016年第10期25卷 76-88页

作者：柴秀丽甘志华路杨张苗辉陈怡然 School of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Department of Electrical and Computer Engineering University of Pittsburgh School of Software Henan University Research Department Henan University

Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.

关键词： genetic recombination memristive hyperchaotic system color image encryption confusion diffusion

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Fast KNN search for big data with set compression tree and best bin first 2

Fast KNN search for big data with set compression tree and b...

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2nd International Conference on Cloud Computing and Internet of Things, CCIOT 2016

作者： Chen, Zhenjie Yan, Jingqi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of Ministry of Education for System Control and Information Processing China Shanghai China

ISBN: (纸本)9781467398213

This paper proposes k nearest neighbors (kNN) search based on set compression tree (SCT) and best bin first (BBF) to deal with the problem for big data. The large compression rate by set compression tree is achieved by compressing the set of descriptors jointly instead of compressing on a per-descript or basis. So set compression tree has a good performance in kNN search at a low bit rate. At the same time, the best bin first (BBF) is a very efficient algorithm to find the approximately kNN from a large number of high dimensional feature descriptors. SCT-BBF is a novel exploration and it improves search performance in three aspects: First, SCT-BBF requires less memory footprint, which is important in big data age. Second, it increases accuracy compared traditional method like KD-Tree and original SCT. SCT-BBF can be used with other data processing methods like PCA and SIFT to perform better. Third, this paper adopts finer search to increase accuracy at a slight loss of speed. And it can extend to big data easily. © 2016 IEEE.

关键词： Big data

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Dictionary learning based statistical interior reconstruction without a prior knowledge 10

Dictionary learning based statistical interior reconstructio...

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Developments in X-Ray Tomography X

作者： Shi, Yongyi Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an Shaanxi710049 China

ISBN: (纸本)9781510603257

Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper, we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography. Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%, corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed images with the constraint of the estimated DC value exhibit greatly superior image quality to that without DC value constraint. © Copyright 2016 SPIE.

关键词： image segmentation

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