Person re-identification is a well-known technique for recognizing a person, who is continuously observed by nonoverlapping cameras in a wide area surveillance. Body part-based re-identification gives a higher level of importance to certain parts of body instead of giving equal importance to whole body. This is mainly due to the fact that certain body parts can be distinguishable better than the others in presence of variable viewing positions, occlusions, illuminations and resolutions of the scenes. This paper focuses on the evaluation of the discrimination power of three body parts, viz., head, torso, and leg for their potential use in fusion-based re-identification technique. In particular, the transformation of features between two body parts is conducted in a warp function space that consists of positive cost for same pair of targets and negative cost for different pair of targets. The support vector machine-based classifier is used to discriminate these two types of cost and the results are evaluated in terms of correct recognition for a given false alarm rate. Experiments are conducted on two publicly available databases, namely, CAVIAR4ReID and CUHK01. The results conclude that torso is the most important body part in the problem of person re-identification.

Noise reduction is an essential step of cDNA microarray image analysis for obtaining better-quality gene expression measurements. Wavelet-based denoising methods have shown significant success in traditional image processing. The complex wavelet transform (CWT) is preferred to the classical discrete wavelet transform for denoising of microarray images due to its improved directional selectivity for better representation of the circular edges of spots and near shift-invariance property. Existing CWT-based denoising methods are not efficient for microarray image processing because they fail to take into account the signal as well as noise correlations that exist between red and green channel images. In this paper, two bivariate estimators are developed for the CWT-based denoising of microarray images using the standard maximum a posteriori and linear minimum mean squared error estimation criteria. The proposed denoising methods are capable of taking into account both the interchannel signal and noise correlations. Significance of the proposed denoising methods is assessed by examining the effect of noise reduction on the estimation of the log-intensity ratio. Extensive experimentations are carried out to show that the proposed methods provide better noise reduction of microarray images leading to more accurate estimation of the log-intensity ratios as compared to the other CWT-based denoising methods.