Fingerprint mosaicing entails the reconciliation of information presented by two or more impressions of a finger in order to generate composite information. It can be accomplished by blending these impressions into a single mosaic, or by integrating the feature sets (viz., minutiae information) pertaining to these impressions. In this work, we use Thin-plate Splines (TPS) to model the relative transformation between two impressions of a finger
thereby accounting for the non-linear distortion present between them. The estimated deformation is used (a) to register the two images and blend them into a single entity before extracting minutiae from the resulting mosaic (image mosaicing); and (b) to register the minutiae point sets corresponding to the two images and
integrate them into a single master minutiae set (feature mosaicing). Experiments conducted on the FVC 2002 DB1 database indicate that both mosaicing schemes result in improved matching performance although feature mosaicing is observed to outperform image mosaicing.
KEYWORDS: Retina, Cones, Data modeling, Spatial frequencies, Visual process modeling, Data processing, Systems modeling, Visualization, Visual information processing, Computer simulations
At the retinal level, the strategies utilized by biological visual systems allow them to outperform machine vision systems, serving to motivate the design of electronic or `smart' sensors based on similar principles. Design of such sensors in silicon first requires a model of retinal information processing which captures the essential features exhibited by biological retinas. In this paper, a simple retinal model is presented, which qualitatively accounts for the achromatic information processing in the primate cone system. The model exhibits many of the properties found in biological retina such as data reduction through nonuniform sampling, adaptation to a large dynamic range of illumination levels, variation of visual acuity with illumination level, and enhancement of spatio temporal contrast information. The model is validated by replicating experiments commonly performed by electrophysiologists on biological retinas and comparing the response of the computer retina to data from experiments in monkeys. In addition, the response of the model to synthetic images is shown. The experiments demonstrate that the model behaves in a manner qualitatively similar to biological retinas and thus may serve as a basis for the development of an `artificial retina.'
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