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multi-scale image segmentation algorithm based on support vector machine approximation criteria

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2012年第11期24卷 1231-1238页

作者： Wang, Liejun Jia, Zhenhong Xinjiang Univ Coll Informat Sci & Engn Urumqi Peoples R China

A new multi-scale image segmentation algorithm based on support vector machine (SVM) approximation criteria has been discussed in this paper. Most current multi-scale image segmentation algorithms are based on the restricted empirical risk minimization, and the approximation of multi-scale image segmentation was poor. As the SVM theory was one based on the structural risk minimization, the best approximation results could be reached. So, it was combined with multi-scale image segmentation algorithms, and one-image multi-resolution analysis approximation algorithms based on the SVM theory were presented in this paper, which could obtain more accurate multi-scale image segmentation. By numerical results, the algorithm was further verified that more accurate image segmentation results were unfolded. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： support vector machine multi-scale image segmentation multi-resolution analysis

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multi-scale image segmentation method with visual saliency constraints and its application 10

Multi-scale image segmentation method with visual saliency c...

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10th International Symposium on multispectral image Processing and Pattern Recognition (MIPPR) - Remote Sensing image Processing, Geographic Information Systems, and Other Applications

作者： Chen, Yan Yu, Jie Sun, Kaimin WenHua Coll Dept Informat Sci & Technol WenHua Rd Wuhan 430074 Hubei Peoples R China Wuhan Univ Off Sci Res & Dev Wuhan 430074 Hubei Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non-saliency background areas. Experiments show that this method works bet

关键词： Visual Saliency multi-scale image segmentation target detection over-segmentation

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A multi-scale image segmentation algorithm based on mean shift

A multi-scale image segmentation algorithm based on mean shi...

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2nd International Conference on image and Graphics

作者： Liu, F Song, XD Luo, YP Hu, DC Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)0819446564

In this paper, we propose a multi-scale image segmentation algorithm based on mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, which has been proven to be a mode-seeking process on a surface constructed with a "shadow" kernel. In the presented algorithm, not only the color features, but Also the space relationship of each pixel are considered in multiple scales, thus getting a more reasonable clustering sequence, furthermore, center candidates are validated by contour map. Experimental examples are illustrated and compared to show that the approach is effective not only in segmentation, but also in denoising.

关键词： multi-scale image segmentation denoising mean shift

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Automatic image Registration Based on Shape Features and multi-scale image segmentation 2

Automatic Image Registration Based on Shape Features and Mul...

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2nd International Conference on multimedia and image Processing (ICMIP)

作者： Sui, Haigang Song, Zhina Gao, Dongsheng Hua, Li Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Hubei Peoples R China Huazhong Agr Univ Coll Resources & Environm Wuhan Hubei Peoples R China

ISBN: (纸本)9781509059546

image registration plays an important role in many practical problems in diverse fields such as medical, remote sensing, and computer vision. The basic purpose of image registration is to obtain the finest geometrical and radio-metrically aligned image from temporal or multi-modal image sensors. In this paper, a novel stable shape feature-based image registration method has been proposed by matching the stable region with a set of rotations, scale invariant features, and multi-scale image segmentation is used to obtain the matching areas. This algorithm firstly converts images into image objects by multi-scale segmentation and convexity model restriction. Then these reliable and stable image regions are used as matching unit rather than points or lines. Experiments show the algorithm proposed in this paper is not sensitive to rotation and resolution distortion, which can accomplish image registration automatically.

关键词： image registration convexity model multi-scale image segmentation shape matching

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B-Spline based globally optimal segmentation combining low-level and high-level information

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PATTERN RECOGNITION 2018年 73卷 144-157页

作者： Zheng, Shenhai Fang, Bin Li, Laquan Gao, Mingqi Chen, Rui Peng, Kaiyi Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China

image segmentation is an important step for large-scale image analysis and object recognition. Variational-based segmentation methods are widely studied due to their good performance, but they still suffer from incapability to deal with images bearing weak contrast, overlapped noise and cluttered texture. To tackle this problem, we propose a new statistical information analysis based multi-scale and global optimization method for image segmentation. This multi-scale processing which is consistent with human's cognition mechanism enables us identify target at coarse scale. The high-level prior is obtained by the multiple Gaussian kernel gray equalization and used as shape constraint in following fine-scale. An efficient energy functional is proposed with convexity and improved TV regularization in order to segment inhomogeneous target from noisy background. A convex relaxation function is explicitly represented by cubic B-Spline basis for fast convergence and intrinsic smooth segmentation. Finally, the energy functional is minimized by standard methods of Split Bregman, Gradient Descent Flow and the corresponding Euler-Lagrange Equation. Experimental results on synthetic and real world images validate the robustness and high accuracy boundaries detection for low contrast, noisy and texture images. (C) 2017 Published by Elsevier Ltd.

关键词： multi-scale image segmentation Prior shape Global optimization B-Spline Split Bregman TV regularization

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multi-scale segmentation USING DEEP GRAPH CUTS: ROBUST LUNG TUMOR DELINEATION IN MVCBCT 15

MULTI-SCALE SEGMENTATION USING DEEP GRAPH CUTS: ROBUST LUNG ...

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15th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Wu, Xiaodong Zhong, Zisha Buatti, John Bai, Junjie Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Dept Radiat Oncol Iowa City IA 52242 USA

ISBN: (纸本)9781538636367

Deep networks have been used in a growing trend in medical image analysis with the remarkable progress in deep learning. In this paper, we formulate the multi-scale segmentation as a Markov Random Field (MRF) energy minimization problem in a deep network (graph), which can be efficiently and exactly solved by computing a minimum s-t cut in an appropriately constructed graph. The performance of the proposed method is assessed on the application of lung tumor segmentation in 38 mega-voltage cone-beam computed tomography datasets.

关键词： Deep graph cuts multi-scale image segmentation deep networks lung tumor segmentation

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Object-oriented Technology Research of High Resolution Remote Sensing image Classification

Object-oriented Technology Research of High Resolution Remot...

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Computer and Computational Sciences (ICCCS)

作者： Zhou, Yang Yao, Guoqing Li, Meng China Univ Geosci Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9781479918195

As the application of high resolution remote sensing image is more and more popular, traditional pixel-based classification method reduces the classification accuracy, wastes a large amount of redundant spatial data and generates image which contains noise classification results. This paper focuses on the object-oriented technology research of high-resolution remote sensing image classification. Its core processing steps include image segmentation and image classification.

关键词： fuzzy classification high-resolution remote sensing image multi-scale image segmentation Object-oriented

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Extracting Urban Ground Object Information from images and LiDAR Data 8

Extracting Urban Ground Object Information from Images and L...

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Proceedings 2015 8 International Congress on image and Signal Processing (CISP)

作者： Li, Luan Zhao, Xuesheng Yi, Lina Li, Yanhua China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing Peoples R China

ISBN: (纸本)9781467390989

To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, we use ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon. Finally, we use the multiple classifier combination method to get the classification result. To validate the method, we classify two research regions (region A covers around 0.21km2, and region B about 1.1km2) in Stuttgart, Germany. The first experiment on region A is to select the optimal segmentation scales and classification features. The overall accuracy of the classification reaches to 93.3%. The experiment on region B is implemented to validate the application-ability of this method for large area, which achieves 88.42% overall accuracy. It can be concluded that the proposed method improves the accuracy and efficiency of urban ground object information extraction and has a high application value.

关键词： component : urban ground objects multi-scale image segmentation high resolution image LiDAR object-oriented classification

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Understanding of hand motion in sentence level using HMM

Understanding of hand motion in sentence level using HMM

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Conference on Human Vision and Electronic Imaging IX

作者： Lee, B Han, Y Chung, H Hahn, H Cheonan Natl Tech Coll Cheonan South Korea

ISBN: (纸本)0819451959

This paper proposes a new method that can recognize a sequence of hand motion expressing a sentence in sign language. Recognition procedure is divided into two steps: separation of the sequence of hand motions into the sub-sequences each of which expresses one word and combination of the words in order to construct a sentence having a meaning. In the first step, sequences of hand motion images are segmented by testing the continuity of the hand motions and by the multiscale image segmentation scheme. The trajectory of the hand motions are estimated by the affine transformation. Each sign in the sentence is represented by the extended chereme analysis model and each chereme is represented by the status vector for determining the transition in the HMM. In the second step, each sentence is also represented by the HMM. The Viterbi algorithm and context-dependent HMM are used to Find the best state sequence in the HMM. The proposed algorithm has been tested with ten sequences of images, each of which expresses a sentence in Korean sign language. The experimental results have shown that the proposed algorithm can separate the sentence level image sequence into the word level sub-sequences with the success rate of 75% on average and recognize the sentence with the success rate of 80%.

关键词： hand motion multi-scale image segmentation affine transform chereme analysis model HMM Viterbi algorithm

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Extracting Urban Ground Object Information from images and LiDAR Data 23

Extracting Urban Ground Object Information from Images and L...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Yi, Lina Zhao, Xuesheng Li, Luan Zhang, Guifeng China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Ding 11 Xueyuan Rd Beijing 100083 Peoples R China Chinese Acad Sci Acad Optoelect Beijing 100864 Peoples R China

To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, this paper uses ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon. Eventually, the multiple classifier combination method is applied to get the outcome of the classification. In order to validate the feasible of this method, this paper selects two experimental regions in Stuttgart and Germany (Region A and B, covers 0.211cm2 and 1.11cm2 respectively). The aim of the first experiment on the Region A is to get the optimal segmentation scales and classification features. The overall accuracy of the classification reaches to 93.3%. The purpose of the experiment on region B is to validate the application-ability of this method for a large area, which is turned out to be reaches 88.4% overall accuracy. In the end of this paper, the conclusion shows that the proposed method can be performed accurately and efficiently in terms of urban ground information extraction and be of high application value.

关键词： Urban Ground Objects multi-scale image segmentation High Resolution image Object-oriented Classification LiDAR

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