The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional signal and image processing
Two-dimensional signal and image processing
International Journal of Computer Vision
On Photometric Issues in 3D Visual Recognition from aSingle 2D Image
International Journal of Computer Vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two- and three-dimensional patterns of the face
Two- and three-dimensional patterns of the face
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Design of steerable filters for feature detection using canny-like criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICSR'10 Proceedings of the Second international conference on Social robotics
Robust camera pose and scene structure analysis for service robotics
Robotics and Autonomous Systems
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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.