Segmentation with Learning Automata (original) (raw)
Related papers
Framework for efficient optimal multilevel image thresholding
Journal of Electronic Imaging, 2009
Image thresholding is a very common image processing operation, since almost all image processing schemes need some sort of separation of the pixels into different classes. In order to determine the thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. By defining two classes of objective functions for which the optimal thresholds can be found by efficient algorithms, this paper provides a framework for determining the solution approach for current and future multilevel thresholding algorithms. We show, for example, that the method proposed by Otsu and other well-known methods have objective functions belonging to these classes. By implementing the algorithms in ANSI C and comparing their execution times, we can also make quantitative statements about their performance.
A survey of thresholding techniques
Graphical Models /graphical Models and Image Processing /computer Vision, Graphics, and Image Processing, 1988
In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics 62 Image Process 7, 1978, 259-265) and Fu and Mu (Pattern Recognit. 13, 1981, 3-16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images. 0 1988 Academic press, IX. f(K Y)-Let t E G be a threshold and B = {b,, b,} be a pair of binary gray levels and b,,b, E G. The result of thresholding an image function f(. , a) at gray level t is a
A novel context sensitive multilevel thresholding for image segmentation
Most of the traditional histogram-based thresholding techniques are effective for bi-level thresholding and unable to consider spatial contextual information of the image for selecting optimal threshold. In this article a novel thresholding technique is presented by proposing an energy function to generate the energy curve of an image by taking into an account the spatial contextual information of the image. The behavior of this energy curve is very much similar to the histogram of the image. To incorporate spatial contextual information of the image for threshold selection process, this energy curve is used as an input of our technique instead of histogram. Moreover, to mitigate multilevel thresholding problem the properties of genetic algorithm are exploited. The proposed algorithm is evaluated on the number of different types of images using a validity measure. The results of the proposed technique are compared with those obtained by using histogram of the image and also with an existing genetic algorithm based context sensitive technique. The comparisons confirmed the effectiveness of the proposed technique.
MULTILEVEL IMAGE THRESHOLDING USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Analysis and comparison of various multilevel segmentation techniques to efficiently detect the tumourous region from MR Brain Images in early stages.To extract the abnormal tissues using multilevel image thresholding based on PSO. In the area of image processing, segmentation of an image into multiple regions is very important for classification and recognition steps. In this project we describe a novel method for segmentation of images based on Particle Swarm optimization for determining multilevel threshold for a given image. The proposed method is compared with other known multilevel segmentation methods to demonstrate its efficiency.
New Results on Efficient Optimal Multilevel Image Thresholding
2006 International Conference on Image Processing, 2006
Image thresholding is one of the most common image processing operations, since almost all image processing schemes need some sort of separation of the pixels into different classes. In order to find the thresholds, almost all methods analyze the histogram of the image. In most cases, the optimal thresholds are found by either minimazing or maximazing an objective function, which depends on the positions of the thresholds. We identify two classes of objective functions for which the optimal thresholds can be found by algorithms with low time complexity. We show, that for example the method proposed by Otsu [1] and other well known methods have objective functions belonging to these classes. By implementing the algorithms in ANSI C and comparing their execution times, we can make a quantitative statement about their performance.
An Efficient Method Based on Abc for Optimal Multilevel Thresholding
2012
Many efficient bi-level thresholding techniques have been proposed in recent years. Usually, the objective functions, which are used by them, are not appropriate for the multilevel thresholding owing to exponential growth of computational complexity. This work presents a new multilevel thresholding algorithm using Artificial Bee Colony algorithm (ABC) with the Otsu's objective function. Also, a strategy is used to guess suitable thresholds for initializing the proposed method. This initializing phase used the bi-level Otsu method to find the initial thresholds. These guessed thresholds are used to create a food source around each of them for use in the ABC algorithm as initial population. The presented thresholding method is tested on four popular images. The results show that this method has competitive performance compared to other wellknown methods such as Gaussian-smoothing, Symmetry-duality, GA-based and PSO-based algorithms.
Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm
2006
A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time.
A Hybrid Approach Using Gaussian Smoothing and Genetic Algorithm for Multilevel Thresholding
International Journal of Hybrid Intelligent Systems, 2005
In this paper, a hybrid approach, which is based on Gaussian smoothing and a genetic algorithm (GA), is proposed for automatic multilevel image thresholding. Using a mixture probability density function of several Gaussian functions to fit an image histogram and then find the optimal threshold(s) is a well-known optimal thresholding method. In the proposed approach, the Gaussian kernel smoothing is used to estimate the number of classes in an image. Since the parameter estimation in the method is typically a nonlinear optimization problem, the parameters used in the mixture of Gaussian functions that give the best fit to the processed histogram are determined using GA. In experiments, synthetic data and real images were processed to evaluate the thresholding performance. The experimental results to confirm the proposed approach are also included.
Computer Vision and Image Understanding, 2008
In this paper, a multilevel thresholding method which allows the determination of the appropriate number of thresholds as well as the adequate threshold values is proposed. This method combines a genetic algorithm with a wavelet transform. First, the length of the original histogram is reduced by using the wavelet transform. Based on this lower resolution version of the histogram, the number of thresholds and the threshold values are determined by using a genetic algorithm. The thresholds are then projected onto the original space. In this step, a refinement procedure may be added to detect accurate threshold values. Experiments and comparative results with multilevel thresholding methods over a synthetic histogram and real images show the efficiency of the proposed method.