Two new texture features, based on morphological scale-space processors are introduced. The new methods are shown to have good performance over a variety of tests. We demonstrate that if texture classifies are to be used in real world scenes, then the choice of test is critical and that Brodatz-like tests are unlikely to represent reality.

Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger realizations of texture visually indistinguishable from the training texture.

Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature....

In this paper, we present the results of a study designed to investigate the visual factors which contribute to the perceived quality of synthesized textures. A psychophysical experiment was performed in which subjects rated the quality of textures synthesized from a variety of modern texture-synthesis algorithms. The ratings were given in terms of how well each synthesized texture represented ...

Texture remains largely underutilized in the analysis of remote sensed datasets compared to descriptors based on the orthogonal spectral dimension. This paper describes our recent efforts towards using texture to automate the annotation of remote sensed imagery Two applications are described that use the homogeneous texture descriptor recently standardized by MPEG-7. In the Jirst, higher-level ...

We propose a texture model construction method for human tracking based on a statistical texture-free shape model (distance map). In practical vision systems, a priori knowledge of the target object is often limited. In human tracking, since human figures involve a variety of images due to different clothes, the detected pixel values (color, brightness) can vary significantly from person to per...

In this work, a heterogeneity feature, calculable from SAR images on a per-pixel basis, but relying on global image statistics, is described and discussed. Starting from the multiplicative speckle and texture models relating the amount of texture and speckle to the local mean and variance at every pixel, such a feature is rigorously derived from Shannon’s information theory as the conditional i...

This paper investigates novel LBP-guided active contour approaches to texture segmentation. The Local Binary Pattern (LBP) operator is well suited for texture representation, combining efficiency and effectiveness for a variety of applications. In this light, two LBP-guided active contours have been formulated, namely the scalar-LBP Active Contour (s-LAC) and the vector-LBP Active Contour (v-LA...

The human’s capability to distinguish perceptually different textures is difficult to reproduce using machine vision due the variety of textural patterns and illumination conditions. Bovik et al. proposed a multi-channel texture analysis technique that relies on 2-D Gabor filters to isolate regions of perceptually homogeneous texture in an image. Textures are modeled as a pattern dominated by a...

A wide variety of texture feature extraction methods have been proposed for texture based image classification and segmentation. These methods are typically evaluated over windows of the same size, the latter being usually chosen for each particular method on an experimental basis. This paper shows that pixel-based texture classification can be significantly improved by evaluating a given textu...