We present a novel approach to multi-texture image segmentation based on the formation of an effective texture feature vector. Texture sub-features are derived from the output of an optimized Gabor filter. The filter’s parameters are selected by an immune genetic algorithm, which aims at maximizing the discrimination between the multi-textured regions. Next the texture features are integrated w...

A patient (HJA) with bilateral occipital lobe damage to ventral cortical areas V2, V3 and V4 was tested on a texture segmentation task involving texture bar detection in an array of oriented lines. Performance detecting a target shape was assessed as the orientations of the background lines had increasing orientation noise. Control participants found the task easier when the background lines ha...

We introduced an unsupervised texture segmentation method , the selectionist relaxation, relying on a Markov Random Field (MRF) texture description and a genetic algorithm based relaxation scheme. It has been shown elsewhere that this method is convenient for achieving a parallel and reliable estimation of MRF parameters and consequently a correct image segmentation. Nevertheless, these results...

The quaternion representation of color is shown here to be effective in the context of segmenting color images into regions of similar color texture. The advantage of using quaternion arithmetic is that a color can be represented and analyzed as a single entity. A lowdimensional basis for the color textures found in a given image is derived via quaternion principal component analysis (QPCA) of ...

This paper details work on the preprocessing of phytoplankton images for classificaltion by neural networks. Firstly there is a review of the Gabor filter set generated for textural feature extraction. Secondly there is a review of discriminant analysis and the main classification techniques The third section of the repoprt details the analysis and the results of data extracted from eight test ...

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper we proposed a textured-based method to segment an input image into regions. In our method an entropy-based textured map of image is extracted, followed by an histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achi...

Texture plays an important role in human visual perception and offers crucial cues for solving a wide range of computer vision problems, such as image segmentation or scene analysis. The segmentation of texture is a key problem in computer vision and image understanding, the objective of which is to partition an image into several regions characterized by homogeneous texture attributes. Over th...

This paper presents a method for the design of multiple Gabor lters for segmenting multi-textured images. Although design methods for a single Gabor lter have been presented recently, the development of general multilter multi-texture design methods largely remains an open problem. Previous multilter design approaches required one lter per texture or were constrained to pairs of textures. Other...

A novel multiscale texture model and a related algorithm for the unsupervised segmentation of medical images to locate tumors are proposed in this project. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are ...

High spatial resolution remote sensing images provide many rich features, such as spectrum, shape, texture, etc. However, only spectral character is adopted in many traditional image segmentation methods, leading to segmentation results unsatisfactory. A multi-feature and multi-band region segmentation algorithm (MM-RSA) is proposed. First, texture image of a band is extracted and is combined i...