Generating and generalizing models of visual objects
Artificial Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Supervised Learning of Descriptions for Image Recognition Purposes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Introduction to the Special Section on Learning in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Acquisition of Object Models by Relational Learning
Visual Information Systems
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
| Hi-index | 0.00 |
Feature extraction is an important part of object model acquisition and object recognition systems. Global features describing properties of whole objects,or local features denoting the constituent parts of objects and their relationships may be used. When a model acquisition or object recognition system requires symbolic input,the features should be represented in symbolic form. Global feature extraction is well-known and oft-reported. This paper discusses the issues involved in the extraction of local features, and presents a method to represent them in symbolic form. Some novel features, specifically between two circular arcs, and a line and a circular arc, are also presented.