Possibilistic BRISK method for an efficient registration (PBRISK)

This paper aims to present a possibilistic registration method using BRISK. BRISK method is a key point detector and descriptor. It is rotation and scale invariant, but it takes more time to detect the feature points and it suffers from the high number of outliers. The main idea of the proposed method is to apply the theory of possibilities for extracting primitives to obtaining an efficient registration. We explore the suitability of the BRISK method for the task of image registration by limiting the outlier's number. The proposed method uses the semantic aspect of images for features detection as well as matching. This “semantic focussing process” allows reducing the quantity of information, as well as the noise effects during the matching process by the creation of a new space called “Semantic knowledge space” which contains a set of projections of images each presenting a single content called a “possibilistic maps The experiments as well as the comparative study carried out, using medical images, show the efficiency of the proposed method in terms of outliers' reduction, noise robustness, time complexity and precision improved.

I. Introduction

Image registration is one of the most important method of image processing. It is the process of superimposing two or more images of the same scene or it can be said like this, it is the process of transforming different sets of data into one coordinate system. The method of image registration can be alienated into two types. First one is known as Area Based Image Registration and second one is Feature Based Image Registration. Area Based Image Registration is useful for providing the information about pixel intensity, where as Feature based Image Registration is beneficial for extracting the useful features from an image like Regions, Points and Corners etc. [4]. It Consist four steps [1]. These are Feature Detection, Feature Matching, Transform Model Estimation, Image Resampling and Transformation. In the process of Feature Detection, features can be detected from an image like closed-boundary regions, edges, contours, line intersections, corners, etc. from both targeted and sensed images.