Authors:
Falk Schmidsberger
and
Frieder Stolzenburg
Affiliation:
Hochschule Harz, Germany
Keyword(s):
Vision and perception, Data mining, Clustering, Decision trees, Object recognition, Image understanding, Autonomous robots.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Autonomous Systems
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Vision and Perception
Abstract:
Each object in a digital image is composed of many patches (segments) with different shapes and colors. In order to recognize an object, e.g. a table or a book, it is necessary to find out which segments are typical for which object and in which segment neighborhood they occur. If a typical segment in a characteristic neighborhood is found, this segment will be part of the object to be recognized. Typical adjacent segments for a certain object define the whole object in the image. Following this idea, we introduce a procedure that learns typical segment configurations for a given object class by training with example images of the desired object, which can be found in and downloaded from the Internet. The procedure employs methods from machine learning, namely k-means clustering and decision trees, and from computer vision, e.g. contour signatures.