Tham khảo tài liệu 'advances in theory and applications of stereo vision part 7', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 140 Advances in Theory and Applications of Stereo Vision linked to any matching features. Any features that are very similar to existing ones have a distance that is less than a third that of the closest non-matching feature will be removed as they do not add significant new information. The result is that training images that are closely matched by the similarity transform are clustered into model views that combine their features for improved robustness. Otherwise the training images form new views in which features are linked to their neighbors. Although Lowe 2001 shows an examples in which a few objects are successfully identified in a cluttered scene no results are reported on recognizing objects with large viewpoint variations significant occlusions and illumination variations. Patch-based 3D model with affine detector and spatial constraint Generic 3D objects often have non-flat surfaces. To model and recognize a 3D object given a pair of stereo images Rothganger et al. 2006 proposes a method for capturing the non-flat surfaces of the 3D object by a large set of sufficiently small patches their geometric and photometric invariants and their 3D spatial constraints. Different views of the object can be matched by checking whether groups of potential correspondences found by correlation are geometrically consistent. This strategy is used in the object modeling phase where matches found in pairs of successive images of the object are used to create a 3D affine model. Given such a model consisting of a large set of affine patches the object in a test image can be claimed recognized if the matches between the affine regions on the model and those found in the test image are consistent with local appearance models and geometric constraints. Their approach consists of three major modules 1. Appearance-based selection of possible matches Using the Harris affine detector Section 2 and a DoG-based Difference-of-Gaussians interest point detector corner-like and .
