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Medical image segmentation is an important application in medical image processing. In order to improve the efficiency of Medical image segmentation, a method to select optimal threshold by PSO algorithm with Dynamic Inertia Weight(DW-PSO) is proposed. It makes the proportion of the local and global searching ability can be effectively controlled in the whole process of optimal searching. Compared...
Hybrid methods of fuzzy clustering and particle swarm optimization (PSO) are important techniques for image segmentation. The spatial credibilistic clustering (SCC) shows better performance than traditional fuzzy clustering, because of the “typicality” represented by credibility memberships degree is much more accurate than the “sharing” represented by probability membership degree to characterize...
This paper selects OTSU segmentation method. In order to verify the superiority of Chaos Particle Swarm Optimization, before segmentation, use test function detects chaos particle swarm (PSO) algorithm accuracy and efficiency. Then OTSU method were optimized and contrasted by four kinds of optimization algorithms. In order to select the best image segmentation, it provides a scalable processing platform...
Thresholding is a popular image segmentation method that converts gray-level image into binary image. The problem of thresholding has been quite extensively studied for many years in order to get an optimum threshold value. The multi-level thresholding becomes very computationally challenges. In this paper, a novel multilevel thresholding method based on particle swarm optimization (PSO) algorithm...
Wood defect detection has an important influence on the automation of wood industry. In view of the complexity of wood defect segmentation, this paper proposes the hybrid algorithm of genetic algorithm and particle swarm optimization algorithm. Firstly, the contrast of the image is enhanced by the linear transformation function. Then, applying genetic algorithm and particle swarm-genetic hybrid algorithm...
Image segmentation has key influence in numerous medical imaging uses. In this paper, we present a new algorithm for spatial fuzzy segmentation using modified particle swarm optimization of medical & multimedia data. The algorithm is realized by modifying the scaling parameters in the conventional fuzzy C-means (FCM) algorithm using Modified Particle Swarm Optimization (MPSO). Spatial coordinates...
In the process of analyzing the document images, the text separation from the background plays a vital role. If the quality of document image is good, then text can be separated very easily by applying simple techniques of thresholding. Whereas, in noisy images it requires very accurate analysis. Various thresholding techniques that are available are efficient and produce better results, but they...
DPSO-FOHA I and DPSO-FOHA II algorithms, based on multilevel thresholding are proposed in this paper. Optimal multilevel thresholds for colored images are maximized by using Otsu's between class variance functions. The Darwinian principle has been used to improve the value of fitness function along with the concept of fractional calculus, which optimizes it in lesser number of search iterations. Comparative...
Although dispensing technology plays an important part in the chip packaging process, most dispensing methods are inefficient. A visual positioning system for dispensing machines will greatly improve the efficiency. The key to visual positioning is an image processing technique that, among other things, includes image preprocessing and image segmentation. In this paper, we use the Otsu algorithm to...
This paper presents an improved discrete quantum particle swarm optimization (IDQPSO) for 2-D maximum entropic multi-threshold image segmentation algorithm. Firstly, particle swarm binary-encoded method based on 2-D threshold is proposed. Additionally, new particle evolution strategy is proposed to avoid converging on local optimum and accelerate searching progress. Additionally, experiments are conducted...
In order to improve the accuracy of medical image segmentation and overcome the shortcomings of maximum entropy segmentation algorithm, the paper proposes the medical image segmentation based on maximum entropy multi-threshold segmentation optimized by improved cuckoo search algorithm (MCS). Firstly, the maximum entropy method is adopted to find the optimization objective function, then the improved...
Quantum-behaved particle swarm optimization (QPSO) algorithm simulates quantum mechanics among individuals. For improving the local search ability of QPSO and guiding the search, an improved QPSO algorithm based on combining the dynamic mutation and cooperative background (MCQPSO) is proposed in this paper. The dynamic Cauchy mutation strategy is introduced to enhance the global search ability. The...
Image captured by two-dimensional camera contains no depth information. However in many applications we need depth information, for example such as in satellite imaging, robotic vision and target tracking. Stereo matching is used to extract depth information from images. The main aim of our project is to use stereo matching algorithms to plot the disparity map of segmented images which gives the depth...
An effective PSO fuzzy clustering edge detection algorithm is proposed. PSO algorithm and Fuzzy C-Mean algorithm are combined to overcome two shortcomings, namely the initialization sensitivity and the local minimum of standard FCM algorithm in image edge detection. At first, a vector is constructed to describe edge point information, which includes neighborhood homogeneity information measure, orientation...
In this article, the particle swarm optimization and differential evolution algorithms inspired by the intrinsic principles of quantum mechanics are presented. These quantum versions of meta-heuristic algorithms, namely quantum inspired particle swarm optimization and quantum inspired differential evolution for multi-level thresholding have been designed to find optimal thresholds of colour images...
The Otsu algorithm is one of the most widely applied threshold-based image segmentation algorithms. However, its rather large calculation amount and poor real-time quality has limited its further application. In this paper, a new segmentation method combined Otsu and particle swarm optimization is proposed. An improved particle swarm optimization with the improvements of particle's best fitness value...
A new hybrid algorithm for image segmentation based on k-mean and particle swarm optimization algorithm is proposed in the paper. K-mean clustering algorithm is a local search algorithm because it is easily to be trapped in local optimum and is sensitive to initial value effectively. On the other hand, particle swarm optimization algorithm is a global optimization algorithm. Because of taking the...
Research in motion segmentation and robust tracking have been getting more attention recently. In video sequence, motion segmentation is considered as multi-objective problem. Better representation and processing of the standard image in video sequence, with efficient segmentation algorithm is required. Thus, multi-objective optimization approach is an appropriate method to solve the optimization...
In order to overcome the poor anti-noise performance of traditional fuzzy C-Means (FCM) algorithm in image segmentation, a novel improved FCM algorithm was proposed in this paper based on Particle Swarm Optimization (PSO) algorithm and Markov Random Field (MRF) model, which can make full use of the global searching ability of PSO and the spatial information integrating ability of MRF for image segmentation...
Image segmentation is a fundamental step for many image analysis and preprocessing tasks. In segmentation, minimum cross entropy (MCE) based multilevel thresholding is regarded as an effective improvement over the bi-level method. However, it is very time consuming for real-time applications. In this paper, a fast threshold selection method based on bacterial foraging optimization (BFO) algorithm...
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