Texture segmentation based on local morphological pattern spectra provides an attractive alternative to linear scale spaces as the latter suffer from blurring and does not preserve the shape of image features. However, for successful segmentation, pattern spectra derived using a number of structuring elements, often at different orientations, are required. This paper addresses this problem by t...

The Nth order probability density function for pixels in a restricted neighborhood may be characterized by a set of N histograms (or some corresponding moments) computed along appropriately chosen axes. The projections on those axes are obtained from a local linear transform of the local neighborhood vector. This approach is closely related to filter bank analysis methods and gives a statistica...

Orthogonal moment functions based on Tchebichef polynomials have found several applications in the eld of image analysis because of their superior feature representation capabilities. Local features represented by such moments could also be used in the design of e cient texture descriptors. This chapter introduces a novel method of constructing feature vectors from orthonormal Tchebichef moment...

Inspired by the Probability of boundary (Pb) algorithm, a simplified texture gradient method has been developed to locate texture boundaries within gray-scale images. Despite considerable simplification, the proposed algorithm’s ability to locate texture boundaries is comparable with Pb’s textureboundary method. The proposed texture gradient method is also integrated with a biologicallyinspired...

In this paper a combined statistical and structural approach has been employed for texture representation. A set of Texture Primitives has been suggested. These primitives are basically tested for the presence of texture by conducting a suitable statistical test called Nair’s test. The set of universal primitives are labeled as local descriptor and the frequency of occurrences of these primitiv...

It is known that the human visual system, unsurpassed in its ability to reconstruct surfaces, employs different cues to solve this difficult task. The prevailing method in digital photogrammetry is stereopsis. However, texture may provide valuable information about the shape of surfaces. In this paper we employ Laws' method of texture energy transforms to extract texture information from digita...

Texture is an important spatial feature which plays a vital role in content based image retrieval. The enormous growth of the internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. This paper describes a new approach for texture classification by combining statistical texture features of Local Binary Pattern and Text...

In this paper, we propose new texture features, block difference of inverse probabilities (BDIP) and block variation of local correlation coefficients (BVLC), for content-based image retrieval and then present an image retrieval method based on the combination of BDIP and BVLC moments. BDIP uses local probabilities in image blocks to measure local brightness variations of an image well. BVLC us...

A generalization of our Gaussian distribution mixtures based method to Bidirectional Texture Function (BTF) modelling is discussed. We estimate the local statistical properties of the monospectral version of the fixed view target BTF texture in the form of a Gaussian mixture of product components. The synthesized texture is obtained by means of a step-wise prediction of the whole fixed view BTF...

This work presents an approach to the extraction of rotation invariant features for texture segmentation using multiscale wavelet frame analysis. The texture is decomposed into a set of bandpass channels by a circularly symmetric wavelet lter, which then gives a measure of edge magnitudes of the texture at di erent scales. The texture is characterized by local energies over small overlapping wi...