Road tracking is a promising technique to increase the efficiency of road mapping. In this paper an improved road tracker, based on cooperation between angular texture signature and template matching, is presented. Our tracker uses parabola to model the road trajectory and to predict the position of next road centreline point. It employs angular texture signature to get the exact moving directi...

PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL PDCL Abstract This article gives a brief review of the fundamentals of the texture mapping, and presents a brief survey of the algorithms used to map a texture onto an arbitrary surface without distortion. Algorithm for texture mapping a scanned image onto curved surfaces using the piecewise attening ...

The new isometric mapping dimensionality reduction algorithm with Incremental Generalized Regression Network has been primarily recognized for stripe surface defects images with the typical characteristics of complex texture, non-uniform image size, asymmetrical number of sample classes, variation illumination environment. This method is suitable to resolve the problem of “short circuit”, store...

Texture can be considered as a repeating pattern of local variation of pixel intensities. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. One of the difficulties in texture classification was the lack of tools that characterize textures. Classification of textures has received attention during last few decades. As DCT works on gray leve...

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives...

Texture images are a special class of images that are spatially homogeneous and consist of repeated elements, often subject some randomization in their location, size, color, orientation, etc. Textures can be classified into different classes or groups based on their structure and origin. Figure 1 gives some example textures. Textures are widely used in varied fields ranging from bio medical im...

The computer vision strategies used to recognize a fruit rely on four basic features which characterize the object: intensity, color, shape and texture. This paper proposes an efficient fusion of color and texture features for fruit recognition. The recognition is done by the minimum distance classifier based upon the statistical and co-occurrence features derived from the Wavelet transformed s...

A problem of using mixture-of-Gaussian models for unsupervised texture segmentation is that “multimodal” textures (such as can often be encountered in natural images) cannot be well represented by a single Gaussian cluster. We propose a divide-andconquer method that groups together Gaussian clusters (estimated via Expectation Maximization) into homogeneous texture classes. This method allows to...

A couple of supervised classifiers to segment optical multispectral images and textured radar images has been developed. In both classifiers, an automated regiongrowing algorithm delineates the training sets. Optimum statistics for defined classes are derived from the training sets. This algorithm handles three parameters: an initial pixel seed, a window and a threshold for each class. A suitab...

A couple of supervised classifiers to segment optical multispectral images and textured radar images has been developed. In both classifiers, an automated regiongrowing algorithm delineates the training sets. Optimum statistics for defined classes are derived from the training sets. This algorithm handles three parameters: an initial pixel seed, a window and a threshold for each class. A suitab...