IEEE Trans Pattern Anal Mach Intell - Robustly Aligning a Shape Model and Its Application to Car Alignment of Unknown Pose.

Tópicos

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Resumo

Precisely localizing in an image a set of feature points that form a shape of an object, such as car or face, is called alignment. Previous shape alignment methods attempted to fit a whole shape model to the observed data, based on the assumption of Gaussian observation noise and the associated regularization process. However, such an approach, though able to deal with Gaussian noise in feature detection, turns out not to be robust or precise because it is vulnerable to gross feature detection errors or outliers resulting from partial occlusions or spurious features from the background or neighboring objects. We address this problem by adopting a randomized hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate a shape-and-pose hypothesis of the object from a partial shape or a subset of feature points. For alignment, a large number of hypotheses are generated by randomly sampling subsets of feature points, and then evaluated to find the one that minimizes the shape prediction error. This method of randomized subset-based matching can effectively handle outliers and recover the correct object shape. We apply this approach on a challenging data set of over 5,000 different-posed car images, spanning a wide variety of car types, lighting, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.

Resumo Limpo

precis local imag set featur point form shape object car face call align previous shape align method attempt fit whole shape model observ data base assumpt gaussian observ nois associ regular process howev approach though abl deal gaussian nois featur detect turn robust precis vulner gross featur detect error outlier result partial occlus spurious featur background neighbor object address problem adopt random hypothesisandtest approach first bayesian infer algorithm develop generat shapeandpos hypothesi object partial shape subset featur point align larg number hypothes generat random sampl subset featur point evalu find one minim shape predict error method random subsetbas match can effect handl outlier recov correct object shape appli approach challeng data set differentpos car imag span wide varieti car type light background scene partial occlus experiment result demonstr favor improv previous method accuraci robust

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