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A system is proposed that considers minimal features using subpattern analysis which leads to less response time in a real time scenario. Using training samples, with a high degree of certainty, the minimum variance quadtree components [MVQC] of a signature for a person are listed to be applied on a testing sample. Initially the experiment was conducted on wavelet decomposed information for a signature. The non-MVQCs and core components were analyzed. To characterize the local details Gaussian-Hermite moment was applied. Later Hu moments were applied on the selected subsections. The summation values of the subsections are provided as feature to radial basis function [RBF] and feed forward neural network classifiers. Results indicate that the RBF classifier yielded 7% false rejection rate and feed forward neural network classification technique produced 9% false rejection rate. Promising results were achieved, by experimenting on the list of most prominent minimum variance components which are core components using RBF.

This paper describes the design and implementation of autonomous real-time motion recognition on a Personal Digital Assistant. All previous such applications have been non real-time and required user interaction. The motivation to use a PDA is to test the viability of performing complex video processing on an embedded platform. The application was constructed using a representation and recognition technique for identifying patterns using Hu Moments. The approach is based upon temporal templates (Motion Energy and History Images) and their matching in time. The implementation was done using Intel Integrated Performance Primitives functions in order to reduce the complexity of the application. Tests were conducted using 5 different motion actions like arm waving, walking from left and right of the camera, head tilting and bending forward. Suggestions were also made on how to improve the performance of the system and possible applications.

In intelligent vehicle system, it is significant to detect and identify road markings for vehicles to follow traffic regulation. This paper proposes a method to recognize direction markings on road surface, which is on the basis of detected lanes and uses Hu moments. First of all, the detection of lanes is based on horizontal luminance difference, which converts the RGB color image to the luminance image, calculates the horizontal luminance difference, obtains the candidate points of lanes' edge and uses least square method to fit the lanes. Secondly, with the detected lines as guide for the search of candidate marking, the paper extracts Hu moments of candidate marking, calculates its Mahalanobis distance to every marking type and classifies it to the type which has the minimal distance with the candidate marking. From the simulation results, the method to detect lanes is more effective and time-efficient than canny or sobel edge detection methods; the method to recognize direction marking is effective and has a high accuracy.