Towards an automatic image co-registration technique using edge dominant corners primitives

Abstract

This paper presents a new algorithm for image co-registration using dominant corners located on the image's edges under the assumption that the deformation between the successive images is modeled by an affine transformation. This assumption is guaranteed when the time interval between acquired images is small like in a video sequence. Therefore, it is mainly dedicated for motion analysis. The method detects first straight edges of an object contour then classifies their intersection points as contour's corners characterized by their angles and adjacent segments lengths. The suppressor starts then to eliminate iteratively the weak corners until reaching a set of dominant ones called "Dominant Corners". These dominant corners are shown to be very repeatable under affinity transformation. A Primitive is constructed by four consecutive dominant corners located on the same contour. The invariant measure that characterizes each primitive is the ratio of areas of two triangles constructed by two triplets selected from these four corners and the corners directions difference. The primitives are formed in the two studied images from an image sequence. All primitives are used to vote for the best affine model that relates the two images. The method used in primitive construction will lead to an important enhancement over existing methods in the voting process in time and accuracy. This method is tested on real images and good results are reported.

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

“…Besides, a user has to have a certain expertise in visual interpretation, among other needs. In this regard, image registration [199][200], segmentation [201], analysis [202][203], ltering [204], and recognition research [205][206][207] can be applied to ood prediction. Kussul et al [208] presented an NN approach to automatic image segmentation.…”