Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices

In this paper, an automatic method is proposed to perform image registration in visible and infrared pair of video sequences for multiple targets. In multimodal image analysis like image fusion systems, color and IR sensors are placed close to each other and capture a same scene simultaneously, but the videos are not properly aligned by default because of different fields of view, image capturing information, working principle and other camera specifications. Because the scenes are usually not planar, alignment needs to be performed continuously by extracting relevant common information. In this paper, we approximate the shape of the targets by polygons and use affine transformation for aligning the two video sequences. After background subtraction, keypoints on the contour of the foreground blobs are detected using DCE (Discrete Curve Evolution)technique. These keypoints are then described by the local shape at each point of the obtained polygon. The keypoints are matched based on the convexity of polygon's vertices and Euclidean distance between them. Only good matches for each local shape polygon in a frame, are kept. To achieve a global affine transformation that maximises the overlapping of infrared and visible foreground pixels, the matched keypoints of each local shape polygon are stored temporally in a buffer for a few number of frames. The matrix is evaluated at each frame using the temporal buffer and the best matrix is selected, based on an overlapping ratio criterion. Our experimental results demonstrate that this method can provide highly accurate registered images and that we outperform a previous related method

Automated Top View Registration of Broadcast Football Videos

In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static model. Automatic registration using existing approaches has been difficult due to the lack of sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduce this problem to a minimal per-frame edge map matching procedure. We show that the per-frame results can be improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of football World Cup 2014

Cage Active Contours for image warping and morphing

Cage Active Contours (CACs) have shown to be a framework for segmenting connected objects using a new class of parametric region-based active contours. The CAC approach deforms the contour locally by moving cage's points through affine transformations. The method has shown good performance for image segmentation, but other applications have not been studied. In this paper, we extend the method with new energy functions based on Gaussian mixture models to capture multiple color components per region and extend their applicability to RGB color space. In addition, we provide an extended mathematical formalization of the CAC framework with the purpose of showing its good properties for segmentation, warping, and morphing. Thus, we propose a multiple-step combined method for segmenting images, warping the correspondences of the object cage points, and morphing the objects to create new images. For validation, both quantitative and qualitative tests are used on different datasets. The results show that the new energies produce improvements over the previously developed energies for the CAC. Moreover, we provide examples of the application of the CAC in image segmentation, warping, and morphing supported by our theoretical conclusions