Sparse points matching by combining 3D mesh saliency with statistical descriptors

dc.contributor.authorCastellani, U.en_US
dc.contributor.authorCristani, M.en_US
dc.contributor.authorFantoni, S.en_US
dc.contributor.authorMurino, V.en_US
dc.date.accessioned2015-02-21T16:19:38Z
dc.date.available2015-02-21T16:19:38Z
dc.date.issued2008en_US
dc.description.abstractThis paper proposes new methodology for the detection and matching of salient points over several views of an object. The process is composed by three main phases. In the first step, detection is carried out by adopting a new perceptually-inspired 3D saliency measure. Such measure allows the detection of few sparse salient points that characterize distinctive portions of the surface. In the second step, a statistical learning approach is considered to describe salient points across different views. Each salient point is modelled by a Hidden Markov Model (HMM), which is trained in an unsupervised way by using contextual 3D neighborhood information, thus providing a robust and invariant point signature. Finally, in the third step, matching among points of different views is performed by evaluating a pairwise similarity measure among HMMs. An extensive and comparative experimental session has been carried out, considering real objects acquired by a 3D scanner from different points of view, where objects come from standard 3D databases. Results are promising, as the detection of salient points is reliable, and the matching is robust and accurate.en_US
dc.description.number2en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume27en_US
dc.identifier.doi10.1111/j.1467-8659.2008.01162.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages643-652en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2008.01162.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleSparse points matching by combining 3D mesh saliency with statistical descriptorsen_US
Files