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We present a novel approach, clustering on local image profiles, for statistically characterizing image intensity in object boundary regions.In deformable model segmentation, a driving consideration is the geometry to image match, the degree to which the target image conforms to some template within the object boundary regions. The template should account for variation over a training set and yet be specific enough to drive an optimization to a desirable result. Using clustering, a template can be built that is optimal over the training data in the metric used, such as normalized correlation. We present a method that first determines local crossboundary image profile types in the space of training data and then builds a template of optimal types.Also presented are the results of a study using this approach on the human kidney in the context of medial representation deformable model segmentation. The results show an improvement in the automatic segmentations using the cluster template, over a previously built template.

Abstract-This paper presents an efficient human recognition system based on vein pattern from the palma dorsa. A new absorption based technique has been proposed to collect good quality images with the help of a low cost camera and light source. The system automatically detects the region of interest from the image and does the necessary preprocessing to extract features. A Euclidean Distance based matching technique has been used for making the decision. It has been tested on a data set of 1750 image samples collected from 341 individuals. The accuracy of the verification system is found to be 99.26% with false rejection rate (FRR) of 0.03%.

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