Shape Boltzmann machine (a type of Deep Boltzmann machine) is a powerful tool for shape modelling; however. has some drawbacks in representation of local shape parts. Disjunctive Normal Shape Model (DNSM) is a strong shape model that can effectively represent local parts of objects. In this paper. we propose a new shape model based on Shape Boltzmann Machine and Disj unctive Normal Shape Model which we call Disj unctive Normal Shape Boltzmann Machine (DNSBM). DNSBM learns binary distributions of shapes by taking both local and global shape constraints into account using a type of Deep Boltzmann Machine. The samples generated using DNSBM look realistic. Moreover, DNSBM is capable of generating novel samples that differ from training examples by exploiting the local shape representation capability of DNSM. We demonstrate the performance of DNSBM for shape completion on two different data sets in which exploitation of local shape parts is important for capturing the statistical variability of the underlying shape distribution s. Experimental results show that DNSBM is a strong model for representing shapes that are composed of local parts.
In REF , a model based on the SBM was proposed for object shape modeling that represents the physical local part of the objects as a union of convex polytopes.
Disjunctive Normal Shape Boltzmann Machine
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science