Article history: Received 17 May 2011 Accepted 14 May 2012 Available online 24 May 2012

This paper considers the problem of classification of Magnetic Resonance Images using 2D and 3D texture measures. Joint statistics such as co-occurrence matrices are common for analysing texture in 2D since they are simple and effective to implement. However, the computational complexity can be prohibitive especially in 3D. In this work, we develop a texture classification strategy by a sub-ban...

This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal stems from a taxonomy of existing methods which highlights two major approaches, referred to as Signatures and Histograms, inherently emphasizing descriptiveness and robustness respectively. We formulate a comprehensive proposal which encompasses a repeatable local reference frame as well as a 3D descriptor...

In this paper we present a new wavelet domain technique for texture analysis and test of pattern replicability. The main property of the proposed features is that they measure texture quality along the most important perceptual dimensions. In other words, we quantify and classify textures according to their directionality, symmetry, regularity and type of regularity. After the feature extractio...

Human motion can be seen as a type of moving texture pattern. In this paper, we propose a novel approach for activity analysis by describing human activities with texture features. Our approach extracts spatially enhanced local binary pattern (LBP) histograms from temporal templates (Motion History Images and Motion Energy Images) and models their temporal behavior with hidden Markov models. Th...

Texture information plays an important role in image analysis. Although several descriptors have been proposed to extract and analyze texture, the development of automatic systems for image interpretation and object recognition is a difficult task due to the complex aspects of texture. Scale is an important information in texture analysis, since a same texture can be perceived as different text...

This paper focuses on directional textures and proposes a new statistical approach for the second order description of an orientation vector field. We tackle two of the main high level properties that drive the perceptual grouping of texture: directionality and periodicity. Our method is based on the measure of similarity between two orientation vectors separated by a given displacement. We pro...

This paper considers the problem of classification of Magnetic Resonance Images using 2D and 3D texture measures. Joint statistics such as co-occurrence matrices are common for analysing texture in 2D since they are simple and effective to implement. However, the computational complexity can be prohibitive especially in 3D. In this work, we develop a texture classification strategy by a sub-ban...

This paper presents a novel texture description approach, which is robust to variances in rotation, scale and illumination in images, to classify the texture of images. A limitation with traditional methods is that they are more or less sensitive to the mentioned changes in images. To overcome this problem, we propose a novel Local Haar Binary Pattern (LHBP) based framework to ensure invariance...

A multichannel approach to texture description is proposed by approximating joint occurrences of multiple features with marginal distributions, as 1-D histograms, and combining similarity scores for 1-D histograms into an aggregate similarity score. A stepwise feature selection algorithm is used to choose the best feature combination in a particular dimension. In classification experiments with...