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Blurring Length Estimation Using Ringing Artifacts in a Deblurred image

Blurring Length Estimation Using Ringing Artifacts in a Debl...

2011 International Conference on image Analysis and Signal processing(2011第三届图像分析与信号处理国际会议 IASP 2011)

作者： Hong Liu Shuangxue Yang Benyong Liu Xiang Liao School of Computer Science and Information Institute of Intelligent Information Processing Guizhou Key Lab of Sound-Image Material Examination and Authentication The Ministry of Public Security of PR

ISBN: (纸本)9781612848792

The restoration quality of a motion-blurred image is highly dependent on the estimation accuracy of the motion blurring parameter. This manuscript presents a novel and precise method for estimation of the motion blurring length, wherein the ringing artifact amount of a deblurred image is measured by energy proportion contained in appropriate frequency bands, and a blurring length with minimum ringing artifact amount is taken as the optimal estimation of the true blurring length. Experimental results show good performance of the proposed algorithm in motion blurring length estimation.

关键词： blurred image restoration blurring kernel motion blur ringing artifacts

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SAR image multiclass segmentation using a multiscale and multidirection triplet Markov fields model in nonsubsampled contourlet transform domain

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Information Fusion 2013年第4期14卷 441-449页

作者： Yan Wu Peng Zhang Ming Li Qiang Zhang Fan Wang Lu Jia Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University P.O. Box 140 Xi’an Shaanxi 710071 China National Key Lab. of Radar Signal Processing Xidian University Xi’an 710071 China

Triplet Markov fields (TMFs) model recently proposed is to deal with nonstationary image segmentation and has achieved promising results. In this paper, we propose a multiscale and multidirection TMF model for nonstationary synthetic aperture radar (SAR) image multiclass segmentation in nonsubsampled contourlet transform (NSCT) domain, named as NSCT-TMF model. NSCT-TMF model is capable of capturing the contextual information of image content in the spatial and scale spaces effectively by the construction of multiscale energy functions. And the derived multiscale and multidirection likelihoods of NSCT-TMF model can capture the dependencies of NSCT coefficients across scale and directions. In this way, the proposed model is able to achieve multiscale information fusion in terms of image configuration and features in underlying labeling process. Experimental results demonstrate that due to the effective propagation of the contextual information, NSCT-TMF model turns out to be more robust against speckle noise and improves the performance of nonstationary SAR image segmentation.

关键词： SAR image multiclass segmentation NSCT-TMF model NSCT-HMT NSCT-TMF energy function Multiscale information fusion

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Data Fusion and Efficient Algorithm for Moving Target Tracking

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IFAC Proceedings Volumes 2008年第2期41卷 2406-2411页

作者： Guocheng Liu Yongji Wang Chinese Education Ministry Key Lab. for Image Processing and Intelligence Control Department of Control Science and Engineering Huazhong University of Science and Technology P.R. China (Tel: 0086-27-68772267

The principle of target tracking and data fusion techniques are discussed. To resolve high uncertainty that exists in sensors of mobile robots, one multi-sensor data fusion algorithm is presented. The algorithm is based on particle filter techniques, fuses the information coming from multiple sensors and merges different state space models. So it can be used to eliminate system and measurement noise and estimate value of position and headings of mobile robot. On simulation experiments, we compare different cases such as single sensors and multi-sensor data fusion, the results demonstrate the feasibility and effectiveness of this algorithm and exhibits good tracking performance.

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AN IKONOS image FUSION PROCESS USING SUPPORT VALUE TRANSFORM

AN IKONOS IMAGE FUSION PROCESS USING SUPPORT VALUE TRANSFORM

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： SHENG ZHENG WENZHONG SHI JIAN LIU Institute of Intelligent Vision and Image Information Best College of Electrical Engineering and In Advanced Research Center for Spatial Information Technology Department of Land Surveying and Geo-In State Education Commission Key Lab for Image Processing and Intelligent Control HUST Wuhan 430074

Panchromatic (Pan)-sharpening of multispectral (MS) bands is an important technique in various applications of satellite remote *** this paper, we apply the support value transform (SVT) to Ikonos image *** fused saliency features are represented by support values and extracted by *** low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan *** fusing results on Ikonos MS + Pan data demonstrate that the proposed image fusion method is effective and efficient.

关键词： Mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) Ikonos image fusion

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Advancing 3D Mesh Segmentation with Regularization Losses: A Semi-Supervised Approach Using MeshCNN

Advancing 3D Mesh Segmentation with Regularization Losses: A...

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International Conference on Digital Home (ICDH)

作者： Yan Xing Shuaijun Lv Daolun Li Wanliang Zhuang Jieqing Tan Rushi Lan School of Mathematics Hefei University of Technology Hefei China Guangxi Key Lab of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

The application of deep learning in 3D mesh segmentation has attracted increasing attention. In supervisedlearning based mesh segmentation, back propagation is often based on cross entropy loss. Some prior knowledge, such as the structure similarity or geometric consistency of data, can be used to improve the segmentation effect. However, in the literature for mesh segmentation, such prior knowledge is usually either ignored in deep learning or only considered at post-processing stage. In this work, we add prior knowledge as a regularization term into the loss function for optimization, which is applied in the semi-supervised 3D mesh segmentation based on MeshCNN for the first time. A large number of experiments show that, for both semi-supervised segmentation with 30% and 70% labeled data and fully supervised segmentation, the proposed regularization term improves the segmentation performance.

关键词： Deep learning Backpropagation Three-dimensional displays Propagation losses Entropy Optimization

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Attentive Semantic Exploring for Manipulated Face Detection

Attentive Semantic Exploring for Manipulated Face Detection

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Zehao Chen Hua Yang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

Face manipulation methods develop rapidly in recent years, whose potential risk to society accounts for the emerging of researches on detection methods. However, due to the diversity of manipulation methods and the high quality of fake images, detection methods suffer from a lack of generalization ability. To solve the problem, we find that segmenting images into semantic fragments could be effective, as discriminative defects and distortions are closely related to such fragments. Besides, to highlight discriminative regions in fragments and to measure contribution to the final prediction of each fragment is efficient for the improvement of generalization ability. Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. To evaluate our method, we reconstruct two datasets: GGFI and FFMI, and also collect two open-source datasets. Experiments on four datasets verify the advantages of our approach against other state-of-the-arts, especially its generalization ability.

关键词： image segmentation Semantics Diversity methods Feature extraction Face detection Distortion measurement Speech processing

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Graph Convolutional Network-based Functional-structural Sub-region Framework for Head and Neck Cancer Prognosis with PET/CT Imaging

Graph Convolutional Network-based Functional-structural Sub-...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Zhou, Zidong Peng, Junyi Lin, Guoyu Wu, Huiqin Lv, Wenbing Lu, Lijun Southern Medical University School of Biomedical Engineering Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Guangzhou510515 China Yunnan University School of Information Science and Engineering Kunming650504 China Pazhou Lab Guangzhou510330 China

ISBN: (纸本)9781665488723

In order to improve the behavior of the prognostic model for patients with head and neck cancer (HNC), we constructed a graph convolutional network (GCN), characterizing the interaction of functional and structural information among different sub-regions of tumors in both PET and CT images. Sub-regions were obtained by using the Watershed-based method and were regarded as vertexes in the graph. The element of 1 in the adjacency matrix indicates corresponding two vertices are spatially contiguous, while the element of 0 indicates they are separated. The selected radiomics features were used to characterize each vertex. 642 HNC patients from 8 different centers were used. The best C-index of 0.767 was achieved by the combination of GCN and clinical parameters compared to the radiomics model (0.747) or GCN model (0.702). the proposed approach has great potential in aiding treatment decision-making. © 2022 IEEE.

关键词： Graph convolutional neural network Head and neck cancer Prognosis Sub-region

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Fast algorithm of discrete Walsta-Haar transformation

Fast algorithm of discrete Walsta-Haar transformation

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International Conference on Space Information Technology

作者： Lu, Li Shi, Baochang Tian, Jinwen Liu, Jian 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 Department of Mathematics Huazhong University of Science and Technology Wuhan Hubei 430074 China

Walsh-Haar function system that was first intruoduced by us is a new kind of function systems, and has a good global/local property. This function system is called Walsh ordering function system since its generation kernel functions belong to Walsh ordering Walsh function system. We worked out a recursive property of the matrix WHKRm+1 corresponding to the first KR m+1 Walsh-Haar functions in Walsh-Haar function system, and we also proved that Walsh-Haar function system is perfect and orthogonal similar to Walsh function system and Haar function system. Thus, discrete Walsh-Haar transformation (DW-HT) is an orthogonal transformation that can be widely used in signal processing. In this paper, using the recursive property of the matrix WHKRm+1 and the fast algorithm of discrete Walsh transformation (DWT) in Walsh ordering, we have designed a fast algorithm of Walsh ordering DW-HT based on the bisection technique. The idea and method used in this paper can be used for designing fast algorithms of other ordering DW-HTs and other discrete orthogonal transformations.

关键词： Mathematical transformations

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Automatic diagnosis of disc herniation based on DenseNet fusion model 8

Automatic diagnosis of disc herniation based on DenseNet fus...

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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： image segmentation

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An Ikonos image fusion process using support value transform

An Ikonos image fusion process using support value transform

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6th International Conference on Machine Learning and Cybernetics

作者： Zheng, Sheng Shi, Wenzhong Liu, Jian China Three Gorges Univ Inst Intelligent Vis & Image Informat Best Coll Elect Engn & Informat Technol Yichang 443002 Peoples R China Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Peoples R China Hong Kong Polytech Univ Adv Res Ctr Spatial Informat Technol Dept Land Surveying & Geo Informat Hong Kong Peoples R China Huazhong Univ Sci & Technol State Educ Commiss Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9781424409723

Panchromatic (Pan)-sharpening of multispectral (MS) bands is an important technique in various applications of satellite remote sensing. In this paper, We apply the support value transform (SVT) to Ikonos image fusion. The fused saliency features are represented by support values and extracted by SVT. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The fusing results on Ikonos MS + Pan data demonstrate that the proposed image fusion method is effective and efficient.

关键词： mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) Ikonos image fusion

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