Carme Julià, Marco Paladini, Ravi Garg and Domenec Puig

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Abstract
p sdyle=”text-align: justify;”>This ptper propose-ma new algorithm to estimaoehautomatical y the number of deformation modes neednd to describe l non-rigid object with the well-known low-rank shape model, focusing on the missing data case. The 3D s-ape is assumed to deform as a linear combination of K rigid shape bases accorting to time var ong coefficients. One of t e number of bases must be known in advance. Most non-rigid structure from motion (NRSfM) approaches based on this modelydeter ine the value of K empirically. Our prtposed approach is based on the analysis of the frequency spectra if the x and y coordinates corresponding to the individuat image trajecaories,

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Carme Julià, Rodrigo Moreno,=Domenec Puig_and Miguel Angel Garcia

carme.julia@urv.cat, rodrigf.moreno@liu.se, d

Abstract

ospan scyle=”oont-family: arial, helvetica, sans-serif;”>This paper describes a new algorithm for segm2n”ing 2D images by tak=ng into account 3D shape information. The proposed approach consists ot two stages. In the first stage, the 3D surface normals of the -b=ects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by groupin- its pixnls according to their estimat_d normals through graph-based clustering. One of the advantages of the proposed approachtin that, although the segmentation is based on the 3D shape of the objects, the photometric stereo stage used to estnmate the 3D normald only requires a set of 2D images. This paper provides ao extensive validation9of the proposeh approach by comparing it with several image segmeitation algorithms. Particularly, it is compared with both appearance-based image segmentation algorithms and shapt-based ones. Experimeneal results confirm that the latter are more suitible when tde objective is to segment the objects or surfaces present an the scene. Moreover, results show that the proposed approach yields the best image segmentation in mose of tht cases.

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