Texture analysis and classification using deterministic tourist walk

In this paper, we present a study on a deterministic partially self-avoiding walk (tourist walk), which provides a novel method for texture feature extraction. The method is able to explore an image on all scales simultaneously. Experiments were conducted using different dynamics concerning the tourist walk. A new strategy, based on histograms. to extract information from its joint probability distribution is presented. The promising results are discussed and compared to the best-known methods for texture description reported in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Occupational exposure to radon and natural gamma radiation in the La Carolina, a former gold mine in San Luis Province, Argentina

Radon and gamma radiation level measurements were carried out inside the La Carolina mine, one of the oldest gold mining camps of southern South America, which is open for touristic visits nowadays. CR-39 track-etch detectors and thermoluminescent dosimeters of natural CaF(2) and LiF TLD-100 were exposed at 14 points along the mine tunnels in order to estimate the mean (222)Rn concentration and the ambient dose equivalent during the summer season (November 2008 to February 2009). The values for the (222)Rn concentration at each monitoring site ranged from 1.8 +/- 0.1 kBq m(-3) to 6.0 +/- 0.5 kBq m(-3), with a mean value of 4.8 kBq m(-3), indicating that these measurements exceed in about three times the upper action level recommended by ICRP for workplaces. The correlations between radon and gamma radiation levels inside the mine were also investigated. Effective doses due to (222)Rn and gamma rays inside the mine were determined, resulting in negligible values to tourists. Considering the effective dose to the mine tourist guides, values exceeding 20 mSv of internal contribution to the effective doses can be reached, depending on the number of working hours inside the mine. (C) 2009 Elsevier Ltd. All rights reserved.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

Texture analysis based on maximum contrast walker

Recently, the deterministic tourist walk has emerged as a novel approach for texture analysis. This method employs a traveler visiting image pixels using a deterministic walk rule. Resulting trajectories provide clues about pixel interaction in the image that can be used for image classification and identification tasks. This paper proposes a new walk rule for the tourist which is based on contrast direction of a neighborhood. The yielded results using this approach are comparable with those from traditional texture analysis methods in the classification of a set of Brodatz textures and their rotated versions, thus confirming the potential of the method as a feasible texture analysis methodology. (C) 2010 Elsevier B.V. All rights reserved.