Unsupervised color image segmentation using Markov Random Fields Model

We propose a novel approach to investigate and implement unsupervised segmentation of color images particularly natural color images. The aim is to devise a robust unsu- pervised segmentation approach that can segment a color textured image accurately. Here, the color and texture information of each individual pixel along with the pixel's spatial relationship within its neighborhood have been considered for producing precise segmentation of color images. Precise segmentation of images has tremendous potential in various application domains like bioinformatics, forensics, security and surveillance, the mining and material industry and medical imaging where subtle information related to color and texture is required to analyze an image accurately. We intend to implement a robust unsupervised segmentation approach for color im- ages using a newly developed multidimensional spatially variant ¯nite mixture model (MSVFMM) using a Markov Random Fields (MRF) model for improving the over- all accuracy in segmentation and Haar wavelet transform for increasing the texture sensitivity of the proposed approach. [...]Master of Computin

Color Textured Image Segmentation Based on Spatial Dependence Using 3D Co-occurrence Matrices and Markov Random Fields

Image segmentation is a primary step in many computer vision tasks. Although many segmentation methods based on either color or texture have been proposed in the last decades, there have been only few approaches combining both these features. This work presents a new image segmentation method using color texture features extracted from 3D co-occurrence matrices combined with spatial dependence, this modeled by a Markov random field. The 3D co-occurrence matrices provide features which summarize statistical interaction both between pixels and different color bands, which is not usually accomplished by other segmentation methods. After a preliminary segmentation of the image into homogeneous regions, the ICM method is applied only to pixels located in the boundaries between regions, providing a fine segmentation with a reduced computational cost, since a small portion of the image is considered in the last stage. A set of synthetic and natural color images is used to show the results by applying the proposed method