Anomaly detection is a promising approach to detecting intruders masquerading as valid users (called masqueraders). It creates a user profile and labels any behavior that deviates from the profile as anomalous. In anomaly detection, a challenging task is modeling a user’s dynamic behavior based on sequential data collected from computer systems. In this paper, we propose a novel method, called ...

In this paper a novel method that automatically detects the lumenintima border on an intravascular ultrasound sequence (IVUS) is presented. First, a 3D co-occurrence matrix was used to efficiently extract the texture information of the IVUS images through the temporal sequence. By extracting several co-occurrence matrices a complete characterization feature space was determined. Secondly, using...

Most of the natural textures are non-homogenous and stochastic by nature. In many cases these textures are also directional. In this paper we present a method for the retrieval of the non-homogenous directional textures. This method is directional histogram and it represents the directional distribution of the texture. The histogram can be formed using either directional filtering method or Hou...

The purpose of this work was to apply and test Haralick’s gray level co-occurrence matrix (GLCM) technique for automatic calculation and segmentation of the ischemic stroke volume from CT images. For this task, the 3nearest neighbors classifier was trained to perform stroke and non-stroke area classification. The segmentation and classification results were compared versus a manual segmentation...

A n-dimensional classification problem may be visualized in (n+1) dimensions using the class label as the (n + 1)th dimension. In such visualization, the class label provides a surface which is smooth in regions where classes are non-interlaced and rough in regions where classes are interlaced. The texture of the “class label surface” thus provides an intuitive measure of pattern classifiabilit...

Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such as Pattern recognition, object tracking, and shape recognition. So far, there have been so many methods offered to solve this problem. Near all these methods have tried to extract ...

A colour texture segmentation method which unifies region and boundary information is presented in this paper. The fusion of several approaches which integrate both information sources allows us to exploit the benefits of each one. We propose a segmentation method which uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of ...

We present results on the relation discovery task, which addresses some of the shortcomings of supervised relation extraction by applying minimally supervised methods. We describe a detailed experimental design that compares various configurations of conceptual representations and similarity measures across six different subsets of the ACE relation extraction data. Previous work on relation dis...

A method is presented for estimating the pose (position & orientation) of a camera based on a set of correspondences between world space and camera space by decomposing the problem into orientation estimation and triangulation. Both of these steps can be proven to be globally convergent which allows fast and accurate estimation. Furthermore, the pose estimation works in full projective space, p...

The Duluth-WSI systems in SemEval-2 built word co–occurrence matrices from the task test data to create a second order co–occurrence representation of those test instances. The senses of words were induced by clustering these instances, where the number of clusters was automatically predicted. The Duluth-Mix system was a variation of WSI that used the combination of training and test data to cr...