Image Understanding Research at Columbia University

摘要

This is an overview of image understanding research at Columbia's Center for Research in Intelligent Systems since the 1993 IU workshop. It reviews our work on the following topics: 1. Physical Models and Sensors (a) Seeing Beyond Lambert's Law (b) Impossible Shape from Shading (c) Specular Reflection and Stereo Vision (d) Color and Polarization (e) Active Shape from Focus 2. Visual Learning and Recognition (a) Learning Object Appearance Models (b) Real-Time Object Recognition System (c) Software Library for Appearance Matching 3. Sensor and Illumination Planning (a) Illumination Planning for Recognition (b) Model Based Sensor Planning 4. Shape Modeling (a) Extruded Generalized Cylinders (b) Periodic Generalized Cylinders (c) Deformable Object Models 5. Vision for Robotics (a) Positioning and Tracking Appearance (b) Visual Control for Grasping (c) Automatic CAD Model Acquisition (d) Uncertainity Models for Robotic Tasks 6. Qualitative Vision (a) Uncalibrated Visual Servoing (b) Qualitative Navigation (c) Visual Interpretation of Gestures (d) Language Description of Visual Images As is evident from the above list our research spans all key areas of computational vision, ranging from sensors and low-level processing to high-level symbolic descriptions and system integration. Here, we provide brief summaries of our results, accompanied by experimental results when appropriate. For technical details we direct the reader to the references cited in this report.