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Edge-Enhanced Cascaded MRF for SAR image Segmentation

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Mengmeng Shang, Ronghua Liu, Kang Feng, Jie Wang, Chao Xu, Songhua Li, Yangyang Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Shaanxi Xi’an710071 China Xidian University School of Computer Science and Technology Shaanxi Xi’an710071 China Research Center for Data Hub and Security Zhejiang Lab Hangzhou311100 China The Second Affiliated Hospital of Xi’an Jiaotong University Department of Health Management Institute of Medical Artificial Intelligence Xi’an710004 China

Markov random fields (MRFs) effectively capture local contextual information by modeling the spatial dependencies between pixels, which helps highlight details and enhances segmentation smoothness. To fully exploit MRF for synthetic aperture radar (SAR) image segmentation, we propose a novel edge-enhanced cascaded MRF (ECMRF) approach. Specifically, we introduce multiple edge-constrained filters to emphasize SAR image boundaries and provide relatively clean features. Building on this, we present a cascaded MRF framework that sequentially integrates region-level and pixel-level segmentations with feature perturbation and fusion to generate the final segmentation output. The framework comprises four key components: 1) a region-level MRF (RMRF), regulated by edge features, to achieve precise region segmentation;2) a pixel-level MRF (PMRF) with selective label smoothing to refine edges and reduce noise clusters;3) equal-channel feature perturbation (EC-FP) to increase feature diversity;and 4) a random probability-based feature fusion (RP-FF) scheme to merge the input features. The experimental results demonstrate that our ECMRF outperforms six state-of-the-art comparable methods, underscoring its competitive performance. © 1980-2012 IEEE.

关键词： image segmentation

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： image quality Quantization (signal) image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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Computational Intelligence in Remote Sensing image Registration:A survey

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International Journal of Automation and computing 2021年第1期18卷 1-17页

作者： Yue Wu Jun-Wei Liu Chen-Zhuo Zhu Zhuang-Fei Bai Qi-Guang Miao Wen-Ping Ma Mao-Guo Gong Key Laboratory of Big Data and Intelligent Vision School of Computer Science and TechnologyXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic EngineeringXidian UniversityXi′an 710071China

In recent years,computational intelligence has been widely used in many fields and achieved remarkable *** computing and deep learning are important branches of computational *** methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image *** paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep *** the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is *** application of deep learning in remote sensing image registration is also *** the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

关键词： Computational intelligence evolutionary computation neural network deep learning remote sensing image registration

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The effect of stochastic acceleration and delay probability on the velocity and the gap between vehicles in traffic flow

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Chinese Physics B 2009年第8期18卷 3347-3354页

作者：盛鹏赵树龙王俊峰唐鹏高琳 School of Computer Science Sichuan University Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University

This paper proposes a new combined cellular automaton （CA） model considering the driver behavior of stochastic acceleration and delay with the velocity of the preceding vehicle and the gap between the successive vehicles based on the WWH model and the noise-first NaSch model. It introduces the delay probability varying with the gap, adds the anticipation headway and increases the acceleration with a certain probability. Through these simulations, not only can the metastable state and start-stop wave be obtained but also the synchronized flow which the wide moving jam results in. Moreover, the effect of stochastic acceleration and delay on traffic flow is discussed by analyzing the correlation of traffic data. This indicates that synchronized flow easily emerges in the critical area between free flow and synchronized flow when acceleration and delay are synchronized or their probability is close to 0.5.

关键词： traffic flow noise-first NaSch model synchronized flow long-range correlation

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Uncertainty Quantification in Medical image Segmentation 6

Uncertainty Quantification in Medical Image Segmentation

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6th IEEE International Conference on computer and Communications, ICCC 2020

作者： Li, Haixing Luo, Haibo The Key Lab of Image Understanding and Computer Vision Shenyang City Liaoning Province China

ISBN: (纸本)9781728186351

In medical images, the observer's manual description of different structures is very different, and it spans a wide range of various structures and pathologies. This variability (which is a characteristic of biological issues, imaging modality and expert annotators) has not been fully considered in the design of computer algorithms for medical image quantification. So far, few people predict the uncertainty of medical image segmentation. In this paper, we designed a V-shaped network to quantify the uncertainty in prostate MRI image segmentation. We have embedded a feature pyramid attention module in the backbone network, which can extract high-level semantic context information at different scales and provide a pixel-level attention to the decoder. At the same time, the module will not bring a large computational burden. In our experiments, we tested the performance of the proposed method on 55 clinical subjects. © 2020 IEEE.

关键词： Magnetic resonance imaging

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SAR image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain

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Chinese Journal of Electronics 2015年第1期24卷 205-211页

作者： CHANG Xia JIAO Licheng LIU Fang SHA Yuheng Institute of Image Processing and Understanding School of Mathematics and Information Science Beifang University of Nationalities Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China International Research Center for Intelligent Perception and Computation Xidian University School of Computer Science and Technology Xidian University

The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method — Nonsubsampled contourlet transform(NSCT) is able to capture the geometric information of SAR images effectively. Describing the aggregation behavior of the neighborhoods coefficients, the scale mixtures of Gaussians model has exhibited favorable performances. A novel SAR image despeckling method is presented by constructing the scale mixtures of Gaussians model of NSCT. This method models the SAR images using the multiscale and multidirection information in NSCT domain. The dependency relationship of NSCT neighborhoods coefficients are also taken into consideration in our model. The speckle noise coefficients are shrinkaged by statistical prior estimation based on SAR image model constructed. Experimental results demonstrate that our method is advantageous at directional information preservation and the speckle restraint.

关键词： Synthetic aperture radar(SAR) image despeckling Stationary wavelet transform Nonsubsampled contourlet transform Scale mixtures of Gaussians model.

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Synthesizing and editing dynamic flames with color temperature

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Chinese Science Bulletin 2014年第36期59卷 5379-5392页

作者： Zhaohui Wu Zhong Zhou Fei Dou Ye Duan Wei Wu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Computer Graphics and Image Understanding Laboratory University of Missouri Columbia MO 65211 USA

Generating computer animated flames is a difficult and computationally expensive problem. Dynamic textures provide an effective means for extrapolating and synthesizing dynamic flames, but aggravates color distortion due to the high correlation of RGB components. A novel method for dynamic flame texture synthesis using color temperature is proposed in this paper. Firstly, the colortemperature mapping is calculated by using the Planck's law and two-color pyrometric technique to avoid color distortion. Secondly, a novel dynamic texture model is presented to transform the RGB space into temperature space. Finally,the dynamic flames editing is presented to support physical temperature adjustment. Experimental results illustrate that our approach is effective to synthesize visually plausible dynamic flames without color distortions and to edit dynamic flames with intuitive physical interpretation.

关键词：纹理合成火焰色温编辑 RGB空间计算问题彩色失真温度调节

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Static human behavior classification based on LLC features and GIST features 7

Static human behavior classification based on LLC features a...

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

作者： Ende, Wang Xukui, Hou Xuepeng, Li Shenyang Institute of Automation Chinese Academic of Sciences Key Lab of Image Understanding and Computer Vision Key Laboratory of Optical Electrical Image Processing Shenhe District Shenyang Liaoning China

ISBN: (纸本)9781538604892

This paper presents a method for recognizing human behavior in static images based on LLC and GIST features. The feature points in the image is densely located in sub-region of images and we extract SIFT feature from each sub-region. Then using the LLC method to encode the extracted dense SIFT features of each sub-region and each feature point descriptor is assigned to several nearest words, so that each descriptor can be expressed by the linear correlation coefficient of vocabulary. In order to increase the spatial information, we use the SPM model to divide the image into many blocks under different levels. We can get the pooled feature from each sub-region using the max pooling method. Then these pooled features from sub-region are concatenated and normalized as the final pooled features. In order to get GIST features, we divide the image into blocks and construct the Gabor filter group of 32 different scales and directions. Then each Gabor filter in the Gabor filter group does convolution operation with image blocks and we take the mean value of each blocks under each Gabor filter as the feature description. The features from each block under each Gabor filter are concatenated and normalized as the final GIST feature representation of the image. Then concatenating the final pooled features and the GIST features and take as the final feature representation. Finally, the intersection kernel function of SVM is used for classification. We performed experiments on several databases. By contrast, this algorithm has a good effect in accuracy and efficiency. © 2017 IEEE.

关键词： Gabor filters

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A Shape Retentive Filtering Algorithm for Post-processing of Instance Contour of Cervical Cell Based on Level Set Method 6

A Shape Retentive Filtering Algorithm for Post-processing of...

引用

6th IEEE International Conference on computer and Communications, ICCC 2020

作者： Liu, Guangqi Ding, Qinghai Ju, Moran Luo, Haibo Jin, Tianming He, Miao The Key Laboratory of Image Understanding and Computer Vision Shenyang110016 China Space Star Technology Company Ltd. Beijing100086 China

ISBN: (纸本)9781728186351

A novel filtering algorithm is proposed based on level set method (LSM) and linear time Euclidean distance transform (LET) algorithm in this paper, which has the property of shape retention and thus is suitable for post-processing of the initial contours for contacting instances in digital Pap image. As one of our contributions, we propose two new metrics based on the pixel-level average false positive rate and false negative rate that used by baseline method. A significant decrease in pixel-level average false positive rate (FP) by 62% can obtain by our proposed method. The result of quantitative and qualitative evaluation shows that our proposed shape retentive filtering algorithm (SRFA) can effectively filter out the false positive fragments of the initial instance contour of cervical cells from the ISBI-2014 dataset. © 2020 IEEE.

关键词： Numerical methods

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Fast-Slow-Thinking: Complex Task Solving with Large Language Models

arXiv

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arXiv 2025年

作者： Sun, Yiliu Zhang, Yanfang Zhao, Zicheng Wan, Sheng Tao, Dacheng Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China The University of Sydney Australia

Nowadays, Large Language Models (LLMs) have been gradually employed to solve complex tasks. To face the challenge, task decomposition has become an effective way, which proposes to divide a complex task into multiple simpler subtasks and then solve them separately so that the difficulty of the original task can be reduced. However, the performance of existing task decomposition methods can be suboptimal when the task contains overly complex logic and constraints. In this situation, the solution generated by LLMs may deviate from the original purpose of the task, or contain redundant or even erroneous content. Therefore, inspired by the fact that humans possess two thinking systems including fast thinking and slow thinking, this paper introduces a new task decomposition method termed "Fast-Slow-Thinking" (FST), which stimulates LLMs to solve tasks through the cooperation of Fast Thinking (FT) and Slow Thinking (ST) steps. Here FT focuses more on the general and concise aspect of the task, and ST focuses more on the details of the task. In FT, LLMs are prompted to remove the constraints of the original task, therefore simplifying it to a general and concise one. In ST, we recall the constraints removed in FT, so that LLMs can improve the answer generated in FT to meet the requirements of the original task. Therefore, our FST method enables LLMs to consider a complex problem via a human-like cognition process from coarse to fine, the effectiveness of which has been well demonstrated by the experiments on three types of tasks. Copyright © 2025, The Authors. All rights reserved.

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