In this paper, we propose a new method for automatic extraction of breast mass spicules in 2-D mammography. Spicules are abnormal curvilinear structures which characterize most of malignant breast masses. They are important features for discrimination between benign and malignant masses. In our method, the curvilinear structures are first approximated by line segments derived from localized Rad...

We investigate the Bunimovich stadium dynamics and nd that in the limit of innnitely long stadium the symbolic dynamics is a subshift of nite type. For a stadium of nite length the Markov partitions are innnite, but the inadmissible symbol sequences can be determined exactly by means of the appropriate pruning front. We outline a construction of a sequence of nite Markov graph approximations by...

COMBINATORIAL MARKOV RANDOM FIELDS AND THEIR APPLICATIONS TO INFORMATION ORGANIZATION

Abstract How to establish the matching (or corresponding) between two different 3D shapes is a classical problem. This paper focused on the research on shape mapping of 3D mesh models, and proposed a shape mapping algorithm based on Hidden Markov Random Field and EM algorithm, as introducing a hidden state random variable associated with the adjacent blocks of shape matching when establishing H...

We present a discriminative method to classify data that have interdependencies in 2-D lattice. Although both Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) are well-known methods for modeling such dependencies, they are often ineffective and inefficient, respectively. This is because many of the simplifying assumptions that underlie the MRF’s efficiency compromise its accurac...

COMBINATORIAL MARKOV RANDOM FIELDS AND THEIR APPLICATIONS TO INFORMATION ORGANIZATION

We show that the definition of neighbor in Markov random fields as defined by Besag (1974) when the joint distribution of the sites is not positive is not well-defined. In a random field with finite number of sites we study the conditions under which giving the value at extra sites will change the belief of an agent about one site. Also the conditions under which the information from some sites...

Word clustering is a conventional and important NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distributional similarity and the other relies on the co-occurrence of two words in lexicosyntactic patterns. Although the two methods have been discussed separately, it is promising to combine them since they are complementary with each other. ...

This paper presents a novel tissue proliferation model of extraneuronal filaceous material consisting of accumulation of the amyloid beta-proteins. Proposed model is constructed using a 3-D Gaussian Hidden Markov Random Field obtained from fluorescent microscopy measurements.

The problem we address in this paper is to label datapoints when the information about them is provided primarily in terms of their subsets or groups. The knowledge we have for a group is a numerical weight or likelihood value for each group member to belong to same class. These likelihood values are computed given a class specific model, either explicit or implicit, of the pattern we wish to l...