资源分类
》 资源 》论文》Object Recognition based on Visual Grammars and Bayesian Networks

Object Recognition based on Visual Grammars and Bayesian Networks

2019-11-08 | |  57 |   43 |   0
Abstract A novel proposal for object recognition based on relational grammars and Bayesian Networks is presented. Based on a Symbol-Relation grammar an object is represented as a hierarchy of features and spatial relations. This representation is transformed to a Bayesian network structure which parameters are learned from examples. Thus, recognition is based on probabilistic inference in the Bayesian network representation. Preliminary results in modeling natural objects are presented.

上一篇：Parametric Local Multimodal Hashing for Cross-View Similarity Search

下一篇：A Bayesian Factorised Covariance Model for Image Analysis∗ Jun Li and Dacheng Tao

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...