一个图象分割评价实例及讨论

利用对图象建模来合成测试图，以原始目标特征值为参考并通过判断实际分割图象质量来评价分割算法优劣的方法对六种不同的阈值选以分割方法进行定量评价，另外还地若干图象分割的评价测度进行了实验比较，所得结果对图象分割的研究有一定的指导作用。     

图象过渡区提取与分割算法评价

对几种基于过渡区提取的图象分割算法进行了较为全面的综合评价,包括分割质量评价、抗噪性评价、运算复杂度评价、目标背景面积比影响及人为参数等方面的评价,评价使用了4种典型的过渡区提取与分割算法,以及两种经典的阈值化分割算法。评价结果表明,过渡区提取与分割算法可以取得优于传统阈值化分割算法的综合性能,是一种值得研究的图象分割方法。     

基于精度依据准则的图像分割算法评价

在图像处理中,图像分割是一类重要的研究方向。图像分割算法的好坏,影响到分割结果的优劣,因此对分割算法的性能评估十分重要。本文提出了一种图像分割算法性能的评价方法——精度依据准则,该准则是对原始特征量值和实际特征量值做比较,通过对绝对值的大小来判断算法的好坏。通过实验比较,该方法具有不错的算法性能评估准确度。     

基于视觉非线性的图象分割新方法

本文分析了在感知目标与其背景时Otsu判别准则与人类视觉机理间的不一致性,并根据视觉的非线性和适应性原理提出了新的目标图象分割计算模型和算法.实验结果表明,与传统的分割法相比,该方法具有优良的从低反差图象中抽取目标的性能.     

图象分割质量评价方法研究

分析研究了图象分割质量的评价方法,将模糊集合的概念应用到分割评价方法中,引入模糊度测度．提出了一个简单有效的映射函数,可以快速有效地将图象从空间域变换到模糊性质域．对几种具有实际意义的图象分割质量评价参数及模糊度做了分析,提出了一个综合评价函数．实验结果表明：引入的模糊度比较准确地反映了分割图象的质量,其反映的分割图象质量差别与人的视觉效果基本一致．     

图象分割质量评价方法研究

分析研究了图象分割质量的评价方法,将模糊集合的概念应用到分割评价方法中,引入模糊度测度,提出了一个简单有效的映射函数,可以快速有铲地将图象从空间域变换到模糊性质域。对几种具有实际意义的图象分割质量评价参数及模糊度做了分析,提出了一个综合评价函数,实验结果表明：引入的模糊度比较准确地反映了分割图象的质量,其反映的分割图象质量差别与人的视觉效果基本一致。     

基于形态开闭滤波二值标记和纹理特征合并的分水岭算法

为了防止分水岭算法过分割问题 ,研究了一种基于形态处理和纹理分析的图象分水岭分割方法 ,该方法是在分水岭算法的基础上 ,首先对形态梯度图象运用形态开闭滤波器组的方法来获得较好的参考图象 ,然后将提取的二值化初始分割结果作为区域标记来进行分割 .另外 ,为了获得整体目标 ,还定义了一个基于纹理特征的一致性和对比度的检验准则 ,并将其作为区域合并的根据 .该方法应用于多个视频序列初始目标分割的结果表明 ,形态滤波器组的引入很好地防止了过分割 ,并证明基于纹理特征均匀性和对比度的准则对分割区域进行进一步的纹理合并是行之有效的 .     

基于相对熵和边缘检测的图象分割算法

利用相对熵选择阈值和检测提出一种图象分割算法。其主要思想是通过相对熵来选择最佳阈值，然后用任何一种边缘检测对图象进行分割。将所提出的算法和基于局部熵的算法分别用于现场颗粒物料图象的分割，实验结果表明，该算法优于基于局部熵的图象分割算法。     

二维遗传算法用于图象动态分割

为了有效地对受噪声影响的图象进行分析,提出了两种基于二维遗传算法的图象动 态分割算法.在这些算法中:1)分别采用了以阈值曲面和模糊隶属度曲面为染色体的二维染 色体编码方式;2)采用了全局阈值化算法和模糊集合理论初始化种群;3)采用Hopfield网络 的能量函数形式,结合FCM算法和现有阈值化算法中的一般性分割准则构造适应度函数. 利用实际图象将所提出的算法与一些典型算法进行了分割比较实验,结果表明所提算法有较 好的抗噪效果.     

基于数学形态学的免疫细胞图象分割

为了实现对免疫细胞图象的分析，首先要对该种图象进行正确分割，针对这一要求，提出了一种有效的免疫细胞图象分割方法，该方法是根据数学形态学的知识，利用直方图势池数来提取标记点，并将这些标记点作为种子点来对梯度图进行Watershed变换，进而实现了细胞图象的分割。该方法是一种谱信息与空间信息相结合的分割方法，根据实验结果和分析可见，该方法不仅解决了细胞在参数测量前的精确分割问题，同时，为水域分割的关键步骤－种子点的选取找到了一种有效而可靠的方法，实践表明，分割的结果与 目视感受相一致，且其分割速度及可重复性都达到了医学临床的要求。     

文本分割综述

文本分割在信息提取、文摘生成、语篇解析及其他多个领域有着极为重要的应用。文本分割的对象包括静态书面文本、语音文本以及动态文本等;分割的粒度因分割的目的不同而有所区别;分割的准确性不仅需要直接评测,更需要间接评测。在大量文献的基础上,对目前常用的分割方法及评测手段进行了全面的归纳和总结,分析了文本分割技术的研究现状,指出尚存在的问题并展望研究前景。     

基于深度学习的点云语义分割研究综述

近年来,深度传感器和三维激光扫描仪的普及推动了三维点云处理方法的快速发展。点云语义分割作为理解三维场景的关键步骤,受到了研究者的广泛关注。随着深度学习的迅速发展并广泛应用到三维语义分割领域,点云语义分割效果得到了显著提升。主要对基于深度学习的点云语义分割方法和研究现状进行了详细的综述。将基于深度学习的点云语义分割方法分为间接语义分割方法和直接语义分割方法,根据各方法的研究内容进一步细分,对每类方法中代表性算法进行分析介绍,总结每类方法的基本思想和优缺点,并系统地阐述了深度学习对语义分割领域的贡献。然后,归纳了当前主流的公共数据集和遥感数据集,并在此基础上对比主流点云语义分割方法的实验结果。最后,对语义分割技术未来的发展方向进行了展望。     

An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision

Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push -relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.     

Benchmarking and comparing multi-exposure image fusion algorithms

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to MEF. However, although many efforts have been made on developing MEF algorithms, the lack of benchmarking studies makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus hindering the development of this field significantly. In this paper, we fill this gap by proposing a benchmark of multi-exposure image fusion (MEFB), which consists of a test set of 100 image pairs, a code library of 21 algorithms, 20 evaluation metrics, 2100 fused images, and a software toolkit. To the best of our knowledge, this is the first benchmarking study in the field of MEF. This paper also gives a literature review on MEF methods with a focus on deep learning-based algorithms. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare the performance of MEF algorithms.     

Toward objective evaluation of image segmentation algorithms

Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm evaluation through an example evaluation of some familiar algorithms - the mean-shift-based algorithm, an efficient graph-based segmentation algorithm, a hybrid algorithm that combines the strengths of both methods, and expectation maximization. Results are presented on the 300 images in the publicly available Berkeley segmentation data set     

The Design of Image Compression Algorithms Testing and Analyzing Tool

This paper studies the evaluation methods for image compression algorithms and proposes test methods for compression algo- rithms including horizontal comparison test and vertical decomposition test. On the base of this, we design and realize a testing and analyzing tool for performance of image compression algorithms. This tool can test and analyze compression algorithms and generate kinds of analysis chart automatically, provides a lot of convenience for users and has very important practical value. In order to im- prove efficiency, veracity and perfectibility of the tool, this paper presents selection method for test images and analysis method for test results which have certain theoretical meaning.     

图像压缩算法性能的测试与分析工具

本文研究了图像压缩算法性能的评价方法,提出了图像压缩算法性能的测试算法,包括横向比较测试和纵向分解测试,并在此基础上设计并实现了压缩算法性能的测试与分析工具。该工具能够测试和分析压缩算法的性能,并自动生成各种分析图表,为用户提供了方便,具有较大的实用价值。为了提高评价的效率、准确性和全面性,文中提出了测试图像的选择方法和测试结果的分析方法,具有一定的理论意义。     

A COMPARATIVE STUDY OF CONTEXTUAL SEGMENTATION METHODS FOR DIGITAL ANGIOGRAM ANALYSIS

This paper presents a comparative study of several well-known and thoroughly tested techniques for the segmentation of textured images, including two algorithms belonging to the adaptive Bayesian family of restoration and segmentation methods, and a novel approach based on the recently introduced concept of the frequency histogram of connected elements (FHCE). The paper first introduces the parameters that define a connected element and then details the sensitivity analysis of these parameters, showing that the grayscale intensity histogram of a digital image is a particular case of the FHCE. The application domain chosen for comparison purposes is the problem of medical images segmentation and, more specifically, as a particularly illustrative case the segmentation of digital angiograms is analyzed in detail. To get a comparative evaluation of FHCE performance, two well-established adaptive or contextual Bayesian segmentation algorithms have been applied to the segmentation of digital angiograms as well. The paper ends with a brief discussion of the comparative performances.     

Kapur’s entropy based optimal multilevel image segmentation using Crow Search Algorithm

Image segmentation is an essential part of image analysis, which has a direct impact on the quality of image analysis results. Thresholding is one of the simplest and widely used methods for image segmentation. Thresholding can be either bi-level, which involves partitioning of an image into two segments, or multilevel, which partitions an image into multiple segments using multiple thresholds values. This paper focuses on multilevel thresholding. A good segmentation scheme through multilevel thresholding identifies suitable threshold values to optimize between-class variance or entropy criterion. For such optimizations, nature inspired metaheuristic algorithms are commonly used. This paper presents a Kapur’s entropy based Crow Search Algorithm (CSA) to estimate optimal values of multilevel thresholds. Crow Search Algorithm is based on the intelligent behavior of crow flock. Crow Search Algorithm have shown better results because of less number of parameters, no premature convergence, and better exploration–exploitation balance in the search strategy. Kapur’s entropy is used as an objective function during the optimization process. The experiments have been performed on benchmarked images for different threshold values (i.e. 2, 4, 8, 16, 32 thresholds). The proposed method has been assessed and performance is compared with well-known metaheuristic optimization methods like Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO) and Cuckoo Search (CS). Experimental results have been evaluated qualitatively and quantitatively by using well-performed evaluation methods namely PSNR, SSIM, and FSIM. Computational time and Wilcoxon p-type value also compared. Experimental results show that proposed algorithm performed better than PSO, DE, GWO, MFO and CS in terms of quality and consistency.