A label field fusion model with a variation of information estimator for image segmentation

This paper proposes a new and reliable segmentation approach based on a fusion framework for combining multiple region-based segmentation maps (with any number of regions) to provide a final improved (i.e., accurate and consistent) segmentation result. The core of this new combination model is based on a consensus (cost) function derived from the recent information Theory based variation of information criterion, proposed by Meila, and allowing to quantify the amount of information that is lost or gained in changing from one clustering to another. In this case, the resulting consensus energy-based segmentation fusion model can be efficiently optimized by exploiting an iterative steepest local energy descent strategy combined with a connectivity constraint. This new framework of segmentation combination, relying on the fusion of inaccurate, quickly and roughly calculated, spatial clustering results, emerges as an appealing alternative to the use of complex segmentation models existing nowadays. Experiments on the Berkeley Segmentation Dataset show that the proposed fusion framework compares favorably to previous techniques in terms of reliability scores.     

一种基于纹理特征融合的SAR图像分割方法

在由若干灰度共生矩阵纹理统计量进行特征融合后所生成的图像上,定义多分辨双Markov-GAR模型,采用多分辨MPM参数估计方法及相应的无监督分割算法,对SAR图像进行纹理分割。该方法既利用了像素的灰度信息,也利用了像素的空间位置信息,削弱了斑点噪声对分割的影响。实验表明对于一些高分辨SAR图像,该方法与单纯基于灰度图像上的多分辨双Markov-GAR模型纹理分割相比,分割精度得以提高。     

Multiresolution color image segmentation

Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov random fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed     

A wavelet-based image fusion tutorial

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.     

基于灰度共生矩阵纹理特征的SAR图像分割

同时考虑SAR图像局部灰度均值和方差及像素空间分布特征等统计量,在以灰度共生矩阵产生的纹理统计量为特征所生成的图像上,建立多分辨双Markov-GAR模型,采用多分辨MPM的参数估计方法及相应的无监督分割算法,对SAR图像进行纹理分割。该方法用于一些高分辨SAR图像,其分割精度及分割边缘的平滑度均优于基于灰度图像上的多分辨双Markov-GAR模型纹理分割。     

Corner detection and curve segmentation by multiresolution chain-code linking

In this paper, we propose a new method to characterise a curve by means of the hierarchical computation of a multiresolution structure. This structure, consisting of successive lower resolution versions of the same object, is processed using the linked pyramid approach. We adapt the multiresolution pixel linking algorithm to the processing of curve contours which are described by their chain-code. We also introduce a selective class selection process which allows application of the algorithm to segmentation and detection of contour features. The resulting framework presents good performance for a wide range of object sizes without the need of any parameter tweaking, and allows detection of shape detail at different scales.     

Fuzzy image fusion based on modified Self-Generating Neural Network

A new fusion algorithm for multi-sensor images based on Self-Generating Neural Network (SGNN) and fuzzy logic is proposed in this paper. This study is an extension of the work described in Qin and Bao (2005). First, the order and frequency modifications for the current McKusick and Langley (M–L) optimization are proposed; next, by combining optimization and pruning together, the Pruning-And-One-Optimization-Composite (PAOOC) processing method is raised; and finally, a modified fuzzy fusion scheme using improved SGNN is put forward. Experimental results demonstrate that the posed fuzzy fusion scheme outperforms region-based fusion using wavelet multi-resolution (MR) segmentation, and region-based fusion using tree-structure wavelet MR segmentation, both in visual effect and objective evaluation criteria. In the meantime, simulations also show the effectiveness of our modifications for the current optimization and pruning methods, visually and objectively.     

Multiresolution-Based Segmentation of Calcifications for the Early Detection of Breast Cancer

Clusters of microcalcifications in a mammogram may be an early indication of breast cancer. Unfortunately, due to size, shape and limited contrast from surrounding normal tissue, microcalcifications can occasionally be hard to detect in computer-aided detection (CAD) systems. These CAD systems can also be slow compared to a radiologist's performance when reviewing film-screen mammography. The research described here investigates a rapid, multiresolution-based approach combined with wavelet analysis to provide an accurate segmentation of potential calcifications. An initial multiresolution approach to fuzzy c -means (FCM) segmentation is employed to rapidly distinguish medically significant tissues. Tissue areas chosen for high-resolution analysis are broken into multiple windows. Within each window, wavelet analysis is used to generate a contrast image, and a local FCM segmentation generates an estimate of local intensity. A simple two-rule fuzzy system then combines intensity and contrast information to derive fuzzy memberships of pixels in the high-contrast, bright pixel class. A double threshold is finally applied to this fuzzy membership to detect and segment calcifications. This sequence of steps is shown to approach detection rates of conventional classifier designs and may therefore be useful as a pre-processing module for these systems to improve speed. Results are reported for 25 images obtained from the Digital Database for Screening Mammography (DDSM).     

基于多分辨率分割的区域图像融合

图像融合是一项综合同一场景的多幅源图像信息的技术.现有的区域图像融合方法或者是只对最高层低频带分割并以此分割信息来指导所有层的融合,或者是其多分辨率分割方法过于复杂难以满足实时性.鉴于此,该文发展了一种基于多分辨率分割的区域图像融合方法.它的主要特点是多分辨率分割.其步骤为:首先对源图像进行小波变换的多分辨率分解,然后对分解后每一层的低频图像都进行区域分割,最后用每一层分割得到的区域信息来分别指导每一层的融合.仿真表明该文发展的基于多分辨率分割的区域图像融合方法的融合性能要优于传统的基于窗口的图像融合方法和只对最高层低频带分割的区域图像融合方法.     

Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part I): A State-of-the-Art Review

Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of the cerebral cortical segmentation, both in 2-D and 3-D, from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. This paper is an attempt to review the state-of-the-art 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on three main classes: region-based, boundary/surface-based and fusion of boundary/surface-based with region-based techniques. In the first class, region-based techniques, we demonstrated more than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions. In the second class, boundary/surface-based, we showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes. Particular emphasis will be placed by presenting four state-of-the-art systems in the third class, based on the fusion of boundary/surface-based with region-based techniques outlined in Part II of the paper, also called regional-geometric deformation models, which take the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy. Received: 25 August 2000, Received in revised form: 28 March 2001, Accepted: 28 March 2001     

多分辨率图像融合的研究与发展

本文系统综述了多分辨率图像融合算法的发展历程和理论研究.首先论述了多分辨率图像融合算法的基本原理及发展状况;然后给出了多分辨率图像融合中的几种典型算法,并做了较详细的分析与讨论;最后对多分辨率图像融合存在的问题及其发展做了探讨.     

Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.     

Game-theoretic integration for image segmentation

Robust segmentation of structures from an image is essential for a variety of image analysis problems. However, the conventional methods of region-based segmentation and gradient-based boundary finding are often frustrated by poor image quality. Here we propose a method to integrate the two approaches using game theory in an effort to form a unified approach that is robust to noise and poor initialization. This combines the perceptual notions of complete boundary information using edge data and shape priors with gray-level homogeneity using two computational modules. The novelty of the method is that this is a bidirectional framework, whereby both computational modules improve their results through mutual information sharing. A number of experiments were performed both on synthetic datasets and datasets of real images to evaluate the new approach and it is shown that the integrated method typically performs better than conventional gradient-based boundary finding     

运动图像多尺度分割中的参数估计方法

将一种新颖的多尺度分割方法引入了多分辨率运动图像分割框架中 ,从而形成了一种新的多尺度运动图像分割方法。该方法主要由多尺度随机场和序贯极大后验估计两个基本概念组成 ,主要是解决多尺度图像分割中仍然存在的计算复杂度问题 ,并可以通过上下文图像模型达到更加准确的图像分割。     

Image bilevel thresholding based on stable transition region set

We utilize the linear system theory to establish a theory model of transition region. With the model, we reveal an important property of transition region, namely the gray level distribution symmetry. Utilizing the property, we propose a new thresholding framework based on stable transition region set. The elements of the stable transition region set are equal or close to each other in the average gray level. As an example of the proposed framework, we have shown that the feature transformation based on the multiscale gradient multiplication technology is an effective means of estimating the threshold. We have performed subjective and objective comparisons on both synthetic and real images. The experimental results show the segmentation quality of the proposed approach is superior to three conventional transition region-based thresholding methods.     

分区域去运动模糊

去运动模糊一直是计算机视觉领域中面向画质增强的一个热点研究方向。模糊核的估算是去运动模糊中的关键问题。提出一种新的思路,即首先将模糊图像按照模糊核的相似度进行图像分割,再对分割后的图像应用空间不变去模糊算法。本文方法主要包含以下几个步骤:分离输入图像中的光照、颜色和纹理信息;分割图像;分区域估算模糊核,计算重叠区域模糊核,并根据计算出的模糊核进行分区域单核去模糊;利用重叠区域整合拼接去模糊结果并还原光照和颜色信息。实验结果表明本文方法比基于单核的去运动模糊算法效果要好。     

交通场景中车辆的运动检测与阴影消除

提出一种算法框架实现对交通场景中运动车辆的分割。首先,提出一种基于颜色空间的浮动气球模型,用以解决监控场景的自适应背景建模问题,该方法解决了基于参数模型的背景建模方法无法检测驻留物体的问题,并可有效适应监控场景中的光照变化以实现自适应更新;其次,针对通过背景建模和背景差分得到的运动前景区域包含运动车辆阴影问题,提出一种新的阴影检测算法,该算法采用多特征融合的方法实现了对运动车辆的分割。实验结果分析表明,与其他方法相比,该算法框架在背景建模和阴影检测方法具有较好的效果。     

Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques.

In this paper, a method is proposed for the segmentation of color images using a multiresolution-based signature subspace classifier (MSSC) with application to psoriasis images. The essential techniques consist of feature extraction and image segmentation (classification) methods. In this approach, the fuzzy texture spectrum and the two-dimensional fuzzy color histogram in the hue-saturation space are first adopted as the feature vector to locate homogeneous regions in the image. Then these regions are used to compute the signature matrices for the orthogonal subspace classifier to obtain a more accurate segmentation. To reduce the computational requirement, the MSSC has been developed. In the experiments, the method is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed algorithm can effectively segment psoriasis images. The proposed approach can also be applied to general color texture segmentation applications.     

利用分水岭分割的多分辨率遥感图像融合算法

对全色图像和多光谱图像进行融合可以获得更加清晰的图像信息。提出了一种基于分水岭分割和小波变换的多分辨率图像融合算法。利用正交小波变换得到原图像的小波金字塔表示。对近似图像进行分水岭分割,并且用小波逆变换把原始分割结果逐步映射回更高的分辨率层。通过联合区域分析,得到各层的联合区域分割图,并用此图来指导各层小波系数的融合。对融合系数进行小波逆变换,得到融合的图像。实验结果表明,该法对遥感图像的融合十分有效,能很好地兼顾融合图像的光谱质量和空间清晰度。     

Images thresholding using ISODATA technique with gamma distribution

Image segmentation is a fundamental step in many applications of image processing. Many image segmentation techniques exist based on different methods such as classification-based methods, edge-based methods, region-based methods, and hybrid methods. The principal approach of segmentation is based on thresholding (classification) that is related to thresholds estimation problem. The ISODATA (Iterative Self-Organizing Data Analysis Technique) method is one of the classification-based methods in image segmentation. We assumed that the data in images is modeled by Gamma distribution. The objective of this paper is to explain a new method that combines Gamma distribution with the technique of ISODATA. The algorithm has two phases: splitting using Gamma distribution then merging which are done based on some predefined parameters. Experimental results showed good segmentation for artificial and real images.