Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
Generating and generalizing models of visual objects
Artificial Intelligence
A logical framework for depiction and image interpretation
Artificial Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Shape matching using polygon approximation and dynamic alignment
Pattern Recognition Letters
Supervised Learning of Descriptions for Image Recognition Purposes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning and image interpretation
Machine learning and image interpretation
Conceptual Spaces for Computer Vision Representations
Artificial Intelligence Review
An Algebra of Geometric Shapes
IEEE Computer Graphics and Applications
Generic Object Recognition: Building and Matching Coarse Descriptions from Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the Special Section on Learning in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-understanding system: A system of experts
International Journal of Intelligent Systems
Understanding the curve-polygon object
Computers and Graphics
Understanding of the concave polygon object in the shape understanding system
Computers and Graphics
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This paper presents a new method of interpretation of the 2D visual objects in terms of 3D geometrical or real world objects. The 3D interpretation of the visual objects depends on the class where a given object is assigned. Each class has its own 3D interpretation method. The interpretation methods for selected classes are described and the results of testing of these methods are presented. It is shown that a visual object can be interpreted as a 3D object by assigning it to one of shape classes. The main novelty of the presented method is that the process of interpretation is related to a visual concept represented as a set of symbolic names of the shape classes. The visual concept, which is one of the components of the category of a visual object, makes it possible to represent knowledge about the visual object in the form of a categorical structure. The presented results are part of research aimed at developing a shape understanding method that will be able to perform complex visual tasks connected with visual thinking.