Detecting multiple planes in images is a challenging problem, but one with many applications. Recent work such as J-Linkage and Ordered Residual Kernels have focussed on developing a domain independent approach to detect multiple structures. These multiple structure detection methods are then used for estimating multiple homographies given feature matches between two images. Features participating in the multiple homographies detected, provide us the multiple scene planes. We show that these methods provide locally optimal results and fail to merge detected planar patches to the true scene planes. These methods use only residues obtained on applying homography of one plane to another as cue for merging. In this paper, we develop additional cues such as local consistency of planes, local normals, texture etc. to perform better classification and merging . We formulate the classification as an MRF problem and use TRWS message passing algorithm to solve non metric energy terms and complex sparse graph structure. We show results on challenging dataset common in robotics navigation scenarios where our method shows accuracy of more than 85 percent on average while being close or same as the actual number of scene planes.
@cite_9 observe that J-linkage uses only nearby feature matches to generate initial homographies. This is done to ensure that the computed homographies correspond to real scene planes. Though, the downside of such an approach is that the homographies output by J-linkage are also locally optimal and do not fit a large scene plane. The scene plane is output as multiple small planar patches by J-linkage. @cite_9 solve this by continuing to cluster the matches (after J-linkage) using the distance measure:
@cite_9
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We present a new method for the robust detection and matching of multiple planes in pairs of images. Such planes can serve as stable landmarks for vision-based urban navigation. Our approach starts from SIFT matches and generates multiple local homography hypotheses using the recent J-linkage technique by Toldo and Fusiello, a robust randomized multi-model estimation algorithm. These hypotheses are then globally merged, spatially analyzed, robustly fitted, and checked for stability. When tested on more than 30,000 image pairs taken from panoramic views of a college campus, our method yields no false positives and recovers 72 of the matchable building walls identified by a human, despite significant occlusions and viewpoint changes. This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual data representation. Each point is represented with the characteristic function of the set of random models that fit the point. A tailored agglomerative clustering, called J-linkage, is used to group points belonging to the same model. The method does not require prior specification of the number of models, nor it necessitate parameters tuning. Experimental results demonstrate the superior performances of the algorithm. We wish to match sets of images to sets of images where both sets are undergoing various distortions such as viewpoint and lighting changes. To this end we have developed a joint manifold distance (JMD) which measures the distance between two subspaces, where each subspace is invariant to a desired group of transformations, for example affine warping of the image plane. The JMD may be seen as generalizing invariant distance metrics such as tangent distance in two important ways. First, formally representing priors on the image distribution avoids certain difficulties, which in previous work have required ad-hoc correction. The second contribution is the observation that previous distances have been computed using what amounted to "home-grown" nonlinear optimizers, and that more reliable results can be obtained by using generic optimizers which have been developed in the numerical analysis community, and which automatically set the parameters which home-grown methods must set by art. The JMD is used in this work to cluster faces in video. Sets of faces detected in contiguous frames define the subspaces, and distance between the subspaces is computed using JMD. In this way the principal cast of a movie can be 'discovered' as the principal clusters. We demonstrate the method on a feature-length movie. The robust alignment of images and scenes seen from widely different viewpoints is an important challenge for camera and scene reconstruction. This paper introduces a novel class of viewpoint independent local features for robust registration and novel algorithms to use the rich information of the new features for 3D scene alignment and large scale scene reconstruction. The key point of our approach consists of leveraging local shape information for the extraction of an invariant feature descriptor. The advantages of the novel viewpoint invariant patch (VIP) are: that the novel features are invariant to 3D camera motion and that a single VIP correspondence uniquely defines the 3D similarity transformation between two scenes. In the paper we demonstrate how to use the properties of the VIPs in an efficient matching scheme for 3D scene alignment. The algorithm is based on a hierarchical matching method which tests the components of the similarity transformation sequentially to allow efficient matching and 3D scene alignment. We evaluate the novel features on real data with known ground truth information and show that the features can be used to reconstruct large scale urban scenes.