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In this paper, two new methods to segment infrared images of finger in order to perform the finger vein pattern extraction task are presented. In the first, the widespread known and used K nearest neighbor (KNN) classifier, which is a very effective supervised method for clustering data sets, is used. In the second, a novel clustering algorithm named(More)
In this paper an algorithm for vessel segmentation and network extraction in retinal images is proposed. A new multi-scale line-tracking procedure is starting from a small group of pixels, derived from a brightness selection rule, and terminates when a cross-sectional profile condition becomes invalid. The multi-scale image map is derived after combining(More)
A novel method for finger vein pattern extraction from infrared images is presented. This method involves four steps: preprocessing which performs local normalization of the image intensity, image enhancement, image segmentation, and finally postprocessing for image cleaning. In the image enhancement step, an image which will be both smooth and similar to(More)
In this paper, an algorithm for robust finger vessel pattern extraction from infrared images is presented based on image processing, edge suppression, fuzzy enhancement, and fuzzy clustering. Initially, the brightness variations of the images are eliminated using histogram normalization and the vessel patterns are enhanced, to facilitate the separation(More)
In this paper, a fully automatic blind retrospective shading correction method based mainly on a minimization of a multi-objective criterion is presented. The proposed method assumes that the acquired image has distorted from a multiplicative and an additive shading component and thus can be adequately described by the linear image formation model. The(More)
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