Change in viewpoint is one of the major factors for variation in object appearance across different images. Thus, view-invariant object recognition is a challenging and important image understanding task. In this paper, we propose a method that can match objects in images taken under different viewpoints. Unlike most methods in the literature, no restriction on camera orientations or internal camera parameters are imposed and no prior knowledge of 3D structure of the object is required. We prove that when two cameras take pictures of the same object from two different viewing angels, the relationship between every quadruple of points reduces to the special case of homography with two equal eigenvalues. Based on this property, we formulate the problem as an error function that indicates how likely two sets of 2D points are projections of the same set of 3D points under two different cameras. Comprehensive set of experiments were conducted to prove the robustness of the method to noise, and evaluate its performance on real-world applications, such as face and object recognition.
@cite_38: We propose a new template-based approach for view invariant recognition of body poses, based on geometric constraints derived from the motion of body point triplets. In addition to spatial information our templates encode temporal information of body pose transitions. Unlike existing methods that study a body pose as a whole, we decompose it into a number of body point triplets, and compare their motions to our templates. Using the fact that the homography induced by the motion of a triplet of body points in two identical body pose transitions reduces to the special case of a homology, we exploit the equality of two of its eigenvalues to impose constraints on the similarity of the pose transitions between two subjects, observed by different perspective cameras and from different viewpoints. Extensive experimental results show that our method can accurately identify human poses from video sequences when they are observed from totally different viewpoints with different camera parameters.
In this paper, we take a geometric approach to the problem of viewpoint variation. Our work is inspired by @cite_38 , who used homographgy constraints to recognize body pose transitions between two successive frames of two video cameras, observing human actions. Although, we are not dealing with video frames in this work, we show that the concept can be extended also to a pair of still images of a rigid object (i.e. instead of dealing with moving points in space viewed by two pairs of frames (4 images), we can extend the idea to recognizing a rigid object from two images. The key to achieve this extension is to consider quadruple of points in each camera image, instead of triplets of points in two frames of each camera The result is a rigid object recognition method that can handle unknown viewpoints and internal camera parameters.