Sample Space Dimensionality Refinement for Symmetrical Object Detection

Abstract

Formerly, dimensionality reduction techniques are effective ways for extracting statistical significance of features from their original dimensions. However, the dimensionality reduction also induces an additional complexity burden which may encumber the real efficiency. In this paper, a technique is proposed for the reduction of the dimension of samples rather than the features in the former schemes, and it is able to additionally reduce the computational complexity of the applied systems during the reduction process. This method effectively reduces the redundancies of a sample, in particular for those objects which possess partially symmetric property, such as human face, pedestrian, and license plate. As demonstrated in the experiments, based upon the premises of faster speeds in training and detection by a factor of 4.06 and 1.24, respectively, similar accuracies to the ones without considering the proposed method are achieved. The performance verifies that the proposed technique can offer competitive practical values in pattern recognition related fields.

Embedded Vein Recognition System with Wavelet Domain

“…(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) Novel recognition techniques developed in recent years mostly utilize biological imaging. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) Given that there are many types of biometrics, a technique's ability to replace others and be accepted by the market is determined by three major criteria, namely, high accuracy rate, low equipment cost, and fast response time. For vein recognition to be widely accepted, the equipment cost must be lowered, although low-resolution cameras will result in poor image quality.…”