A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class islabeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selectionproblems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates thehistogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to the Journa

OPTIMUM MULTILEVEL THRESHOLDING HYBRID GA-PSO BY ALGORITHM

The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm

A Novel Histogram-Based Multi-Threshold Searching Algorithm for Multilevel Color Thresholding

[[abstract]]Image segmentation is an important preliminary process required in object tracking applications. This paper addresses the issue of unsupervised multi‐colour thresholding design for colour‐based multiple objects segmentation. Most of the current unsupervised colour thresholding techniques require adopting a supervised training algorithm or a cluster‐number decision algorithm to obtain optimal threshold values of each colour channel for a colour‐of‐interest. In this paper, a novel unsupervised multi‐threshold searching algorithm is proposed to automatically search the optimal threshold values for segmenting multiple colour objects. To achieve this, a novel ratio‐map image computation method is proposed to efficiently enhance the contrast between colour and non¬colour pixels. The Otsu’s method is then applied to the ratio‐map image to extract all colour objects from the image. Finally, a new histogram‐based multi‐threshold searching algorithm is developed to search the optimal upper‐bound and lower‐bound threshold values of hue, saturation and brightness components for each colour object. Experimental results show that the proposed method not only succeeds in separating all colour objects-of-interest in colour images, but also provides satisfactory colour thresholding results compared with an existing multilevel thresholding method.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子版[[booktype]]紙

A summary of image segmentation techniques

Machine vision systems are often considered to be composed of two subsystems: low-level vision and high-level vision. Low level vision consists primarily of image processing operations performed on the input image to produce another image with more favorable characteristics. These operations may yield images with reduced noise or cause certain features of the image to be emphasized (such as edges). High-level vision includes object recognition and, at the highest level, scene interpretation. The bridge between these two subsystems is the segmentation system. Through segmentation, the enhanced input image is mapped into a description involving regions with common features which can be used by the higher level vision tasks. There is no theory on image segmentation. Instead, image segmentation techniques are basically ad hoc and differ mostly in the way they emphasize one or more of the desired properties of an ideal segmenter and in the way they balance and compromise one desired property against another. These techniques can be categorized in a number of different groups including local vs. global, parallel vs. sequential, contextual vs. noncontextual, interactive vs. automatic. In this paper, we categorize the schemes into three main groups: pixel-based, edge-based, and region-based. Pixel-based segmentation schemes classify pixels based solely on their gray levels. Edge-based schemes first detect local discontinuities (edges) and then use that information to separate the image into regions. Finally, region-based schemes start with a seed pixel (or group of pixels) and then grow or split the seed until the original image is composed of only homogeneous regions. Because there are a number of survey papers available, we will not discuss all segmentation schemes. Rather than a survey, we take the approach of a detailed overview. We focus only on the more common approaches in order to give the reader a flavor for the variety of techniques available yet present enough details to facilitate implementation and experimentation

Multithreshold Segmentation Based on Artificial Immune Systems

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices