In this paper, a fused feature vector consisting of raw time series and texture feature information is proposed for space object classification. The time series data includes historical orbit trajectories and asteroid light curves. The texture feature is derived from recurrence plots using Gabor filters for both unsupervised learning and supervised learning algorithms. The simulation results sh...

Image texture is a complex visual perception. With the everincreasing spatial resolution of remotely sensed data, the role of image texture in image classification has increased. Current approaches to image texture analysis rely on a single band of spatial information to characterize texture. This paper presents a multiscale approach to image texture where first and secondorder statistical meas...

We consider the problem of zone class$cation in document image processing. Document blocks are labelled as text or non-text using texture features derived from a feature based interaction map (FBIM), a recently introduced general tool for texture analysis [3, 41. The zone classijication procedure proposed is tested on the comprehensive document image database UW-I created at the University of W...

A new multi-feature fusion technique based on Dempster-Shafer's evidential reasoning for classification of image texture is presented. The proposed technique is divided into three main steps. In the first step, the fractal dimension and gray co-occurrence matrix entropy are extracted from a texture image. In the second step, we focus on how to design a probability assignment function m(A) repre...

Multichannel Gabor filters (MGFs) and Markov random fields (MRFs) are two common methods for texture classification. However, the two above methods make the implicit assumption that textures are acquired in the same viewpoint, which is unsuitable for rotation-invariant texture classification. In this paper, rotation-invariant (RI) texture features are developed based on MGF and MRF. A novel alg...

This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architec...

submitted for the degree of Doctor of Philosophy Trinity Term 2004 This thesis investigates the problem of classifying textures from their imaged appearance without imposing any constraints on, or requiring any a priori knowledge of, the viewing or illumination conditions under which the images were obtained. Weak classification algorithms based on the statistical distribution of texton primiti...

by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent non-Gaussian densities. The algorithm estimates the data density in each class by using parametric nonlinear functions that fit to the non-Gaussian structure of the data. This improves classification accuracy compared with standard Gaussian mixture models. W...

Texture classification is an important and challenging problem in many image processing applications. While convolutional neural networks (CNNs) achieved significant successes for image classification, texture classification remains a difficult problem since textures usually do not contain enough information regarding the shape of object. In image processing, texture classification has been tra...

In this paper we demonstrate the efficacy of using joint probabilities on the values (pixel intensities/wavelet coefficients) for neighbouring sites (pixels/spatially neighbouring wavelet coefficients), to classify images based on texture. The classification capacity for this type of joint distribution, used as a feature, is tested using a first nearest neighbour (NN1) method, which counts the ...