Title :
Statistical model for occluded object recognition
Author :
Ying, Zhengrong ; Castanon, David
Author_Institution :
Dept. of Electr. Comput. Eng., Boston Univ., MA, USA
Abstract :
In this paper we present a model-based statistical algorithm for recognition of partially occluded objects from noisy features. The likelihood ratio of the image features to template features is used for recognition. Two different statistical occlusion models are introduced: an independent prior model and a Markov random field (MRF) prior model. Our experiments show that the MRF model performs more robustly than the independent model in the presence of partial occlusion
Keywords :
Markov processes; object recognition; statistical analysis; Markov random field prior model; image features; independent prior model; likelihood ratio; model-based statistical algorithm; noisy features; partially occluded object recognition; statistical model; template features; Background noise; Bayesian methods; Electrical capacitance tomography; Image analysis; Image recognition; Object recognition; Read only memory; Statistics;
Conference_Titel :
Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on
Conference_Location :
Bethesda, MD
Print_ISBN :
0-7695-0446-9
DOI :
10.1109/ICIIS.1999.810284
