Progress on Vision Through Learning: A Collaborative Effort of George Mason University and University of Maryland
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AbstractThis report briefly reviews research progress on vision through learning conducted as a collaborative effort of the GMU Machine Learning and Inference Laboratory and the UMD Computer Vision Laboratory. The report covers work done on the following projects: (1)The Multi-level Image Sampling and Transformation (MIST) methodology for learning image descriptions and transformations (2) Applying the MIST methodology to semantic analysis of outdoor scenes (3) Recognizing objects in a cluttered environment (4) Learning in navigation (5) Intelligent interfaces: Learning in the RADIUS environment (6) Learning space configuration and homing (7) Learning object functionality Our work aims at ultimately developing vision systems that apply a range of symbolic and parametric machine learning methods to solving vision problems.