19(7):839-852

This paper presents a model of 3D object recognition
motivated from the robust properties of human
vision system (HVS). The HVS shows the best efficiency
and robustness for an object identification task. The
robust properties of the HVS are visual attention, contrast
mechanism, feature binding, multi-resolution, size
tuning, and part-based representation. In addition,
bottom-up and top-down information are combined
cooperatively. Based on these facts, a plausible computational
model integrating these facts under the Monte
Carlo optimization technique was proposed. In this
scheme, object recognition is regarded as a parameter
optimization problem. The bottom-up process is used to
initialize parameters in a discriminative way; the topdown
process is used to optimize them in a generative
way. Experimental results show that the proposed recognition
model is feasible for 3D object identification
and pose estimation in visible and infrared band images.