-Gait can be identified by observing static and dynamic parts of human body. In this paper a variant of gait energy image called change energy images (CEI) are generated to capture detailed static and dynamic information of human gait. Radon transform is applied to CEI in four different directions (vertical, horizontal and two opposite cross sections) considering four different angles to compute discriminative feature values. The extracted features are represented in the form of interval -valued type symbolic data. The proposed method is capable of recognizing an individual when he/she have variations in their gait due to different clothes they wear, in different normal conditions and carrying a bag. A similarity measure suitable for the proposed gait representation is explored for the purpose of establishing similarity match for gait recognition. Experiments are conducted on CASIA database B and the results have shown better recognition performance compared to some of the existing methods.

The cultivation classification is one of the main steps in crop management. Classification may be used for various grades. The texture-based grading of the areca nut is suggested in this paper. Applications of Wavelet, Gabor, Local binary (LBP), Gray Level Difference Matrix (GLDM) and Gray Level Co-Occurrence Matrix (GLCM) are used to extract various texture features from areca nut. The Nearest Neighbor (NN) system is used. Experimentation with a dataset of 700 images from 7 classes to illustrate the efficiency of the proposed model. The Gabor wavelet features reflect 91.43 per cent of the classification limit.