Adaptive Window Selection for Non-uniform Lighting Image Thresholding

Selection of appropriate size of windows or subimages is the most important step for thresholding images with non-uniform lighting. In this paper, a novel criteria function is developed to partition images into different size of sub images appropriate for thresholding. After the partitioning, each subimage is segmented by Otsu's thresholding approaches. The performance of the proposed method is validated on benchmark test images with different degree of uneven lighting. Based on the qualitative and quantitative measures, the proposed method is fully automatic, fast and efficient in comparison to many landmark approaches

Parallel Genetic Algorithm based Thresholding Schemes for Image Segmentation

In this thesis, the problem of image segmentation has been addressed using the notion of thresholding.Since the focus of this work is primarily on object/objects background classification and fault detection in a given scene, the segmentation problem is viewed as a classification problem. In this regard, the notion of thresholding has been used to classify the range of gray values and hence classifies the image. The gray level distributions of the original image or the proposed feature image have been used to obtain the optimal threshold. Initially, PGA based class models have been developed to classify different classes of a nonlinear multimodal function. This problem is formulated where the nonlinear multimodal function is viewed as consisting of multiple class distributions.Each class could be represented by the niche or peaks of that class.Hence, the problem has been formulated to detect the peaks of the functions. PGA based clustering algorithm has been proposed to maintain stable sub-populations in the niches and hence the peaks could be detected. A new interconnection model has been proposed for PGA to accelerate the rate of convergence to the optimal solution. Convergence analysis of the proposed PGA based algorithm has been carried out and is shown to converge to the solution. The proposed PGA based clustering algorithm could successfully be tested for different classes and is found to converge much faster than that of GA based clustering algorithm

Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

In this paper, two novel strategies have been proposed to obtain segmentation of an object and background in a given scene. The first one, known as Featureless(FL) approach, deals with the histogram of the original image where Parallel Genetic Algorithm (PGA) based clustering notion is used to determine the optimal threshold from the discrete nature of the histogram distribution. In this regard, we have proposed a new interconnection model for PGA. The second scheme, the Featured Based(FB) approach, is based on the proposed featured histogram distribution. A feature from the given image is extracted and the histogram corresponding to the derived feature pixels is used to determine the optimal threshold for the original image. The proposed PGA based clustering is used to determine the optimal threshold. The performance of both the schemes is compared with that of Otsu's and Kwon's method and FB method is found to be the best among the three techniques

Adaptive Window Selection for Non-uniform Lighting Image Thresholding

Selection of appropriate size of windows or subimages is the most important step for thresholding images with non-uniform lighting. In this paper, a novel criteria function is developed to partition images into different size of sub images appropriate for thresholding. After the partitioning, each subimage is segmented by Otsu's thresholding approaches. The performance of the proposed method is validated on benchmark test images with different degree of uneven lighting. Based on the qualitative and quantitative measures, the proposed method is fully automatic, fast and efficient in comparison to many landmark approaches

Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

In this paper, two novel strategies have been proposed to obtain segmentation of an object and background in a given scene. The first one, known as Featureless(FL) approach, deals with the histogram of the original image where Parallel Genetic Algorithm (PGA) based clustering notion is used to determine the optimal threshold from the discrete nature of the histogram distribution. In this regard, we have proposed a new interconnection model for PGA. The second scheme, the Featured Based(FB) approach, is based on the proposed featured histogram distribution. A feature from the given image is extracted and the histogram corresponding to the derived feature pixels is used to determine the optimal threshold for the original image. The proposed PGA based clustering is used to determine the optimal threshold. The performance of both the schemes is compared with that of Otsu's and Kwon's method and FB method is found to be the best among the three techniques