Recognizing 3-D objects by using a Hopfield-style optimization algorithm for matching patch-based descriptions
A new method is proposed for recognizing 3-D objects by using a Hopfield-style optimization algorithm based on matching patch-based image and model descriptions. To obtain the image descriptions, range images are employed to extract reliable high-level patch features. In the optimization process, the objective function is a Liapunov function which encodes a set of geometric constraints on the descriptions. The optimization is implemented in a Hopfield network with its interconnections encoding the imposed unary, binary and bounding edge constraints. At first, the paper makes an explanation on a new pre-processing method for deriving the required image description. It then presents the structure of the used Hopfield network that is able to recognize multiple model objects all at the same time. Experimental results based on synthetic or real range images are also reported.
patch-based descriptions hopfield networks 3-d object recognition model-based recognition optimal matching algorithms constraint satisfication