Adaptive object detection and recognition based on a feedback strategy

Detecting and recognizing objects in environments with unpredictable illumination changes remains a challenging task. Existing algorithms employ a passive methodology to deal with these environments, where learning is performed from many samples taken under various lighting conditions or with some pre-designed color constancy models. In this paper, the challenges of unpredictable illumination changes are addressed through a feedback strategy. With the use of feedback, self-adaptation in object detection and recognition is possible in response to variable illumination. Self-adaptation is achieved through feedback from the recognition phase to the detection phase. A multilevel Markov random field (MRF) is adopted to model both the detection and recognition processes. The original MRF approach is extended to a model that encodes simultaneous object detection and recognition. Experimental results show the feasibility of the proposed framework.

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