DocumentCode :
2714358
Title :
Level-set segmentation with contour based object representation
Author :
Weiler, Daniel ; Roehrbein, Florian ; Eggert, Julian
Author_Institution :
Control Theor. & Robot. Lab., Darmstadt Univ. of Technol., Darmstadt, Germany
fYear :
2009
fDate :
14-19 June 2009
Firstpage :
3327
Lastpage :
3334
Abstract :
In this paper we present an approach for contour based object representation. To this end we use a curvature signal gained by a level-set segmentation method. The advantage of that curvature signal is that it generates no computational overhead as it is a byproduct of standard level-set segmentation methods. Different methods for the description of the segmented objects, so called object descriptors are presented. The object descriptors are all invariant against translation, rotation and scale of the object. Furthermore we show a sparse and memory efficient representation of the descriptors for a series of objects. Finally an approach for classification of unknown objects based on ldquomemorizedrdquo objects is proposed.
Keywords :
image classification; image representation; image segmentation; object detection; contour based object representation; curvature signal; level-set segmentation method; object classifcation; object descriptor; Automatic control; Biological system modeling; Control theory; Image segmentation; Labeling; Neural networks; Neurons; Pixel; Shape; Signal generators;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
ISSN :
1098-7576
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2009.5179047
Filename :
5179047
Link To Document :
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