Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Computer
Introduction to the Special PAMI Issues on Industrial Machine Vision and Computer Vision Technology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grasping of Static and Moving Objects Using a Vision-Based Control Approach
Journal of Intelligent and Robotic Systems
The framework of controlled active vision
Mathematical and Computer Modelling: An International Journal
| Hi-index | 4.10 |
A model-based approach has been proposed to make object recognition computationally tractable. In this approach, models associated with objects expected to appear in the scene are recorded in the system's knowledge base. The system extracts various features from the input images using robust, low-level, general-purpose operators. Finally, matching is performed between the image-derived features and the scene domain models to recognize objects. Factors affecting the successful design and implementation of model-based vision systems include the ability to derive suitable object models, the nature of image features extracted by the operators, a computationally effective matching approach, knowledge representation schemes, and effective control mechanisms for guiding the systems's overall operation. The vision system they describe uses gray-scale images, which can successfully handle complex scenes with multiple object types.