The main key point of preferential image segmentation is to segment object of user interest based on intensities, boundaries and texture and ignoring the remaining portions. It explains the trees of shapes to represent image content. In the tree of shape we use algorithms called as FLLT (Fast Level Line Transform) and JSEG (Jsegmentation). Here both the algorithm are compared and an analysis is...

A novel method for adaptively selecting texture features is presented. We use genetic algorithm to search for an optimal set of structuring elements which provides the best discrimination of textures. Moreover, a tree structure containing the selected set of structuring elements has been set up for classification. Experiments show that by the proposed method can achieve high classification accu...

Mobile mapping is a hot issue in the communities of photogrammetry and computer vision nowadays. Mobile mapping systems represent a significant advance in integrated, multi-sensor digital mapping technology, providing an innovative path towards rapid and cost-effective collection of high-quality and up-to-date spatial information. Therefore how to acquire geometric and texture information from ...

A Gabor filtering method for texture based classification of color images is presented. The algorithm is robust and can be used with different color representations. It involves a filter selection process based on texture smoothness. Unichannel and interchannel correlation features are computed. Two types of color representations have been considered: (1) computing chromaticity values from xyY,...

COMPLEX DIRECTIONAL WAVELET TRANSFORMS: REPRESENTATION, STATISTICAL MODELING AND APPLICATIONS AN PHUOC NHU VO, Ph.D. The University of Texas at Arlington, 2008 Supervising Professor: Soontorn Oraintara The thesis presents an new image decomposition for feature extraction, which is called the pyramidal dual-tree directional filter bank (PDTDFB). The image representation has an overcomplete ratio...

Feature extraction is the method of defining a set of features, which will most effectively represent the information that is important for analysis and concurrence. Gray level cooccurrence matrix (GLCM) is an important method to take out the texture features in medical image. Gray level co-occurrence matrix can simply take out the texture under single scale and single direction. However its de...

This paper presents a method for identification and classification of images of medicinal plants such as herbs, shrubs and trees based on color and texture feature using SVM and neural network classifier. The tribal people in India classify plants according to their medicinal values. In the system of medicine called Ayurveda, identification of medicinal plants is considered an important activit...

This work on texture detection was inspired by the general problem of object recognition in two dimensional still images. One of the crucial challenges associated with the object recognition is selecting and obtaining discriminative features. In analysis of real scenes, like nature or urban places, many objects contain textures, which can be considered as one of the object features. Texture det...

A method of decomposition of overlapping reflections in a powder pattern with the aid of preferred orientation is presented. Empirical preferred orientation correction with axial symmetry is used. The method is tested on several sets of simulated powder patterns. Introduction Powder patterns collected at various values of some parameter which influences either the position or the intensity of t...

This paper develops an approach for the splat-based visualization of large scale, non-uniform data. A hierarchical structure is generated that permits detailed treatment at the leaf nodes of the non-uniform distribution. A set of levels of detail (LODs) are generated based on the levels of the hierarchy. These yield two metrics, one in terms of the spatial extent of the bounding box containing ...