Face detection is one of the most difficult image processing issues. In many stage face recognition, important parts detection. recent years, there has been much progress in Many recognition system adopt or develop .We have proposed automatic algorithm an individual person through group by matching extracted from based on Normalized Cross Correlation (NCC) functions and skin variable size detec...

This paper describes a class of algorithms enabling efficient and exhaustive matching of a template into an image based on the Zero mean Normalized Cross-Correlation function (ZNCC). The approach consists in checking at each image position two sufficient conditions obtained at a reduced computational cost. This allows to skip rapidly most of the expensive calculations required to evaluate the Z...

Autocorrelation of a double-exposed image, unlike cross-correlation between two images, produces a correlation function that is symmetric about the origin. Thus, while it is possible to calculate the speed and direction of tracer particles in a particle image velocimetry (PIV) image using autocorrelation, it is impossible to tell whether the velocity is in the positive or negative direction. Th...

We propose two novel distance measures, normalized between 0 and 1, and based on normalized cross-correlation for image matching. These distance measures explicitly utilize the fact that for natural images there is a high correlation between spatially close pixels. Image matching is used in various computer vision tasks, and the requirements to the distance measure are application dependent. Im...

Preface We introduce parameters called normalized mean square covariance (NMSV), normalized mean square correlation (NMSR), re-centered normalized mean square correlation (RC-NMSR), and stochastic degree of freedom (SDF) to efficiently characterize the correlation properties of Ä ¾ stochastic processes. We show that these parameters are very useful to characterize wireless communication channel...