Combined Model-Based 3d Object Recognition

Abstract

This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

Flexible 3d Object Recognition Framework Using 2d Views via a Similarity-Based Aspect-Graph Approach

“…Both viewer-centered and view-based frameworks conform to the intuition of human perception, during which a person memorizes an object using several primary views without requiring an exhaustive 3D object model. Moreover, Kim et al 18 proposed a combined model-based method to recognize 3D objects using a combination of a bottom-up process (model parameter initialization) and a top-down process (model parameter optimization).…”