This work proposes a texture descriptor based on fractal theory. The method is based on the Bouligand-Minkowski descriptors. We decompose the original image recursively into 4 equal parts. In each recursion step, we estimate the average and the deviation of the Bouligand-Minkowski descriptors computed over each part. Thus, we extract entropy features from both average and deviation. The propose...

This paper addresses an estimation problem of the ball’s state of spin in RoboCup Small Size League (SSL). A spinning ball varies its speed after the ball bounces off the floor. This paper proposes an image-based estimation method of the ball’s state of spin, in particular, by using inertia feature of co-occurrence matrix of the image sequence. The effectiveness of our proposed method is shown ...

Writer identification recently has been studied and it has a wide variety of applications. Most studies are based on English documents with the assumption that the written text is fixed (text-dependent methods) and no research has been reported on Farsi or Arabic documents. In this paper, we have proposed a method for off-line writer identification based on Farsi handwriting, which is text-inde...

This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM computation algorithm to extract discriminant volumetric texture features for classification. As the kernel size of the moving box is the most important factor for the computation of ...

With consumer reviews becoming a mainstream part of ecommerce, a good method of detecting the product or service aspects that are discussed is desirable. This work focuses on detecting aspects that are not literally mentioned in the text, or implicit aspects. To this end, a co-occurrence matrix of synsets from WordNet and implicit aspects is constructed. The semantic relations that exist betwee...

In this paper we present a statistical learning scheme for image classification based on a mixture of old fashioned ideas and state of the art learning tools. We represent input images through large dimensional and usually sparse histograms which, depending on the task, are either color histograms or co-occurrence matrices. Support Vector Machines are trained on these sparse inputs directly to ...

We define the notion of indifferent set with respect to a given class of {0, 1}-sequences. Roughly, for a set A in the class, a set of natural numbers I is indifferent for A with respect to the class if it does not matter how we change A at the positions in I: the new sequence continues to be in the given class. We are especially interested in studying those sets that are indifferent with respe...

SUMMARY The grey level co-occurrence matrix (GLCM) is a measure of the texture of an image. It describes how often different combinations of pixel brightness values occur in an image. Based on this, several textural attributes can be calculated. These directional attributes can be used to determine isotropic and anisotropic areas. In anisotropic areas the information of directional GLCM-based a...

This paper presents a graph-based method for all-word word sense disambiguation of biomedical texts using semantic relatedness as edge weight. Semantic relatedness is derived from a term-topic co-occurrence matrix. The sense inventory is generated by the MetaMap program. Word sense disambiguation is performed on a disambiguation graph via a vertex centrality measure. The proposed method achieve...

Estimation of the Embedding capacity is an important problem specifically in reversible multi-pass watermarking and is required for analysis before any image can be watermarked. In this paper, we propose an efficient method for estimating the embedding capacity of a given cover image under multi-pass embedding, without actually embedding the watermark. We demonstrate this for a class of reversi...