Abstract: Matching of digital images problem is a crucial step in many image analysis applications. This paper investigates this problem and proposes a method to solve it. The proposed method is implemented and applied on real complex aerial images. The proposed method is based on two main stages. In the first stage, the edge elements are determined locally and then aggregated globally into better defined lines called straight-line segments. Based on the descriptions of these segments, matching is performed between them to estimate the transformation coefficients between the images. In the second stage, points of interests which have high variance are selected automatically and then their corresponding points are determined in the other image. The proposed algorithm shows excellent result when applied and tested on real urban complex images.

Abstract: Through this paper we mention a new cogent method for 3D fitting ellipsoid and conic from scattered data, damaged by noise and outliers. This 3D statistical approach fitting builds on 3D Zernike moments. We prefer to estimate the probability density function p.d.f with the Zernike moment theory overseen by maximum entropy principle MEP. The purposed algorithm was successfully used in simulated noisy 3D conic forms images and 3D fitting a tumor for magnetic resonance imaging MRI. A deeper analysis of 3D fitting process is demonstrated to show its rapidness as well as its superior performance related noise immunity .

Abstract: This paper deals with the analytical-statistical method for the face recognition. Although the bases of the face recognition are known by researchers worldwide, the statistical tests of data obtained by measuring chosen anthropometrical points can be found in several articles. Our aim is to show how the data act during the various emotions of one face, which will be helpful for deeper knowledge of how the face behaves. The focus is also aimed at study of aging. The data were tested in the STATISTICA software using e.g. the ANOVA, the Shapiro-Wilk test, the k-Means clustering and the t-test. Acquired results reflect the difficulty of describing the face and the applicability of combination of different recognition methods (e.g. methods based on neural networks, recognition of facial contours, distribution of the gray scale in the image, deformation models) to get the best results in the verification/identification of a human.

Abstract: Automatic object detection is one of the challenging tasks in the areas of image processing. We have proposed an automatic object detection approach for human hands. Our proposed approach is more efficient in compare to other existing approaches and faster as well.