Random walks for feature-preserving mesh denoising
Computer Aided Geometric Design
Semi-Supervised learning using random walk limiting probabilities
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
| Hi-index | 0.14 |
We consider the problem of texture discrimination. Random walks are performed in a plain domain D bounded by an absorbing boundary 驴 and the absorption distribution is calculated. Measurements derived from such distributions are the features used for discrimination. Both problems of texture discrimination and edge segment detection can be solved using the same random walk approach. The border distributions and their differences with respect to a homogeneous image can classify two different images as having similar or dissimilar textures. The existence of an edge segment is concluded if the boundary distribution for a given window (subimage) differs significantly from the boundary distribution for a homogeneous (uniform grey level) window. The random walk procedure has been implemented and results of texture discrimination are shown. A comparison is made between results obtained using the random walk approach and the first-or second-order statistics, respectively. The random walk procedure is intended mainly for the texture discrimination problem, and its possible application to the edge detection problem (as shown in this paper) is just a by-product.