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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

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Possibilistic similarity based image classification

Possibilistic similarity based image classification

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2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en sciences et Technologies de l'Information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UE Brest France

ISBN: (纸本)9789898565419

In this study, an approach for image classification based on possibilistic similarity is proposed. This approach, due to the use of possibilistic concepts, enables an important flexibility to integrate both contextual information and a priori knowledge. Possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability density functions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. Several measures of similarity between classes were tested in order to improve the discrimination between classes. The classification is then performed based on the principle of possibilistic similarity. Synthetic and real images are used in order to evaluate the performances of the proposed model.

关键词： Probability density function

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A method of pixel unmixing by classes based on the possibilistic similarity

A method of pixel unmixing by classes based on the possibili...

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3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en Sciences et Technologies de L'information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UEB Brest France

ISBN: (纸本)9789897580185

In this paper, an approach for pixel unmixing based on possibilistic similarity is proposed. This approach uses possibility distributions to express both the expert's semantic knowledge (a priori knowledge) and the contextual information. Dubois-Prade's probability-possibility transformation is used to construct these possibility distributions starting from statistical information (learning areas delimitated by an expert for each thematic class in the analyzed scene) which serve, first, for the estimation of the probability density functions using the kernel density estimation. The pixel unmixing is then performed based on the possibilistic similarity between a local possibility distribution estimated around the considered pixel and the obtained possibility distributions representing the predefined thematic classes. The obtained similarity values are used in order to obtain the abundances of different classes in the considered pixel. Accuracy analysis of pixels unmixing demonstrates that the proposed approach represents an efficient estimator of their abundances of the predefined thematic classes and, in turn, higher classification accuracy is achieved. Synthetic images are used in order to evaluate the performances of the proposed approach. Copyright © 2014 SCITEPRESS.

关键词： Classification (of information)

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Iterative possibility distributions refining in pixel-based images classification framework

Iterative possibility distributions refining in pixel-based ...

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2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en Sciences et Technologies de l'Information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UEB Brest France

ISBN: (纸本)9789898565419

In this study, an incremental and iterative approach for possibility distributions estimation in pixel-based images classification context is proposed. This approach is based on the use of possibilistic reasoning in order to enrich a set of samples serving for the initial estimation of possibility distributions. The use of possibilistic concepts enables an important flexibility for the integration of a context-based additional semantic knowledge source formed by pixels belonging with high certainty to different semantic classes (called possibilistic seeds), into the available knowledge encoded by possibility distributions. Once possibilistic seeds are extracted, possibility distributions are incrementally updated and refined. Synthetic images composed of two thematic classes are generated in order to evaluate the performances of the proposed approach. Initial possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability distributions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. The possibilistic seeds extraction process is conducted through the application of a possibilistic contextual rule using the confidence index used as an uncertainty measure.

关键词： Pixels

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A fractal image coding method combined with compressed sensing algorithm

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu

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Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 2016年第4期 78-83页

作者： Guo, Hui He, Jie Hu, Defa Jiang, Weijin Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University Wuzhou Guangxi China School of Computer and Information Engineering Hunan University of Commerce Changsha Hunan China

Purpose. Since fractal image coding is time-consuming and is prone to causing "blocking artifact", the article aims to combine fractal image coding, wavelet transform and compressed sensing to put forward a method which can shorten the coding time effectively and improve the quality of a reconstructed image. Methodology. The compressed sensing algorithm can quickly compress and highly restore sparse matrixes. The paper made use of this feature to conduct fractal coding for a low-frequency sub-image after wavelet transform, followed by recoding the samples of low-frequency differential sub-graphs and high-frequency sub-images by means of the compressed sensing algorithm for the purpose of compensating the quality of reconstructed images. Findings. Compared to the traditional fractal coding method, the algorithm in the paper (hereinafter referred to as "this Algorithm") can shorten the time considerably and get a maximum speed-up ratio by up to 6.45 times. Compared to the compressed sensing coding method, the quality of the reconstructed images is improved significantly. Originality. The innovation of the paper lies in applying the compressed sensing theory to the fractal coding algorithm to compensate the quality of the reconstructed images obtained by means of fractal coding based on wavelet transform. Practical value. This Algorithm can shorten the coding time on the basis of ensuring the quality of a reconstructed image, and has certain significance for promoting the fractal coding method. © Hui Guo, Jie He, Defa Hu, Weijin Jiang, 2016.

关键词： Wavelet transforms

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An Adaptive Feature-based Quantization Algorithm for Point Cloud Compression

An Adaptive Feature-based Quantization Algorithm for Point C...

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Picture Coding Symposium, PCS

作者： Da Ai Hongying Lu Yurong Yang Ying Liu National Key Lab. of Electronic Information Processing for Applications in Crime Scene Investigation Ministry of Public Security Xi‘an China Center for Image and Information Processing Xi'an University of Posts and Telecommunications Xi'an China The International Joint-Research Center for Wireless Communication and Information Processing Xi‘an University of Posts and Telecommunications Xi‘an China

To reduce over-rasterization distortion caused by global uniform quantization for static surface point cloud, an adaptive quantization coding method based on feature mining is proposed. Combining spatial position and texture feature of point clouds with level of details, the quantization increment is dynamically set according to feature priority, which can reserve the number of effective points to the maximum extent, and reduce the rasterization distortion. Experimental results show that the proposed method can effectively enhance the subjective reconstruction quality of compressed point cloud, gaining better results of rate-distortion optimization.

关键词： Graphics Geometry Three-dimensional displays Quantization (signal) Transform coding Rate-distortion Distortion

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Multifeature distance map based detection of small infrared targets with low contrast in image sequences

Multifeature distance map based detection of small infrared ...

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International Conference on Space Information Technology

作者： Wang, Zhicheng Gao, Chenqiang Tian, Jinwen Liu, Jian Chen, Xiangping State Education Commission Key Lab for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei 430074 China Inst. for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China Electronic and Engineering Department Huazhong University of Science and Technology Wuhan Hubei 430074 China

Target detection techniques play an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. In this paper, a novel method for fusion detection of infrared weak targets based on multifeature distance map (MFDM) in image sequences is proposed. As for small weak targets, there are many features, such as local entropy, average gradient strength. These features depict the characteristics of small infrared targets and can be extracted. Multifeature-based fusion techniques are applied to detect such weak targets. The problem of detecting small targets is converted to search peak values in specified feature space where multifeature vectors space (MFVS) is considered. Distance map (DM) can be derived according to feature vectors and target detection is performed in DM. In order to accumulate energy of targets deeply and suppress background and clutters to a great extent, five distance maps obtained by corresponding five consecutive frames are utilized to fuse with average weight, which results in the fact that the contrast between targets and background including clutters are enlarged and that the feature peaks of targets are obvious different from background and clutters. After these steps, a contrast segmentation method is used to extract targets from complicated background on the fused DM. Actual infrared image sequences in background of sea and sky are applied to validate the proposed approach. Experimental results demonstrate the robustness of the proposed method with high performance.

关键词： Automatic target recognition

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Evolutionary neural architecture search for retinal vessel segmentation

arXiv

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arXiv 2020年

作者： Fan, Zhun Wei, Jiahong Zhu, Guijie Mo, Jiajie Li, Wenji Department of Electronic and Information Engineering Shantou University Shantou515063 China Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou515063 China

The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases. Manually designing a valid neural network architecture for retinal vessel segmentation requires high expertise and a large workload. In order to improve the performance of vessel segmentation and reduce the workload of manually designing neural network, we propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation. A modified evolutionary algorithm is used to evolve the architectures of encoder-decoder framework with limited computing resources. The evolved model obtained by the proposed approach achieves top performance among all compared methods on the three datasets, namely DRIVE, STARE and CHASE_DB1, but with much fewer parameters. Moreover, the results of cross-training show that the evolved model is with considerable scalability, which indicates a great potential for clinical disease diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Ophthalmology

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Looking here or there? Gaze Following in 360-Degree images

Looking here or there? Gaze Following in 360-Degree Images

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International Conference on Computer Vision (ICCV)

作者： Yunhao Li Wei Shen Zhongpai Gao Yucheng Zhu Guangtao Zhai Guodong Guo Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Baidu

ISBN: (纸本)9781665428132

Gaze following, i.e., detecting the gaze target of a human subject, in 2D images has become an active topic in computer vision. However, it usually suffers from the out of frame issue due to the limited field-of-view (FoV) of 2D images. In this paper, we introduce a novel task, gaze following in 360-degree images which provide an omnidirectional FoV and can alleviate the out of frame issue. We collect the first dataset, "GazeFollow360" 1 , for this task, containing around 10,000 360-degree images with complex gaze behaviors under various scenes. Existing 2D gaze following methods suffer from performance degradation in 360degree images since they may use the assumption that a gaze target is in the 2D gaze sight line. However, this assumption is no longer true for long-distance gaze behaviors in 360-degree images, due to the distortion brought by sphere-to-plane projection. To address this challenge, we propose a 3D sight line guided dual-pathway framework, to detect the gaze target within a local region (here) and from a distant region (there), parallelly. Specifically, the local region is obtained as a 2D cone-shaped field along the 2D projection of the sight line starting at the human subject’s head position, and the distant region is obtained by searching along the sight line in 3D sphere space. Finally, the location of the gaze target is determined by fusing the estimations from both the local region and the distant region. Experimental results show that our method achieves significant improvements over previous 2D gaze following methods on our GazeFollow360 dataset.

关键词： Degradation Computer vision Solid modeling Three-dimensional displays Head Estimation Distortion

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Improved Kernel Correlation Filter Tracking with Gaussian scale space

Improved Kernel Correlation Filter Tracking with Gaussian sc...

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International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.12 Infrared Technology and Application

作者： Shukun Tan Yunpeng Liu Yicui Li Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science The Key Lab of Image Understanding and Computer Vision

Visual tracking is one of the most challenging tasks in the field of computer vision and is related to a wide range of applications like surveillance and robotics. Tracking-by-detection methods are wi

ISBN: (纸本)9781510607729

关键词： visual tracking kernel correlation filter Gaussian scale space bilinear interpolation Mean Absolute Difference visual tracking bilinear interpolation images absolute difference imagery (Psychotherapy) Gauss Scale formation scale parameter Targets image Seeds jigs(templates) scales Scale

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