DocumentCode :
293611
Title :
A model generation method for object recognition task by pictorial examples
Author :
Arita, Daisaku ; Tsuruta, Naoyuki ; Taniguchi, Rin-ichiro ; Amamiya, Makoto
Author_Institution :
Dept. of Inf. Syst., Kyushu Univ., Fukuoka, Japan
Volume :
1
fYear :
1994
fDate :
13-16 Nov 1994
Firstpage :
233
Abstract :
In this paper, we describe a method to construct automatically, from a series of images of objects, a model of an object class. The model is described by a set of 2 dimensional features of segmented regions and the relations among them. To make models of objects, we introduce the concept of the segmentation tree-which represents image segmentation at various levels of abstraction-and also a model generation method based on a series of segmentation trees. Using this segmentation tree, we can cope with the problem of diversity of segmentation patterns and can easily make stable object-models
Keywords :
image segmentation; object recognition; probability; 2 dimensional features; abstraction; diversity; image segmentation; model generation method; object class model; object images; object recognition task; pictorial examples; segmentation tree; segmented regions; stable object-models; Humans; Image segmentation; Information systems; Noise generators; Noise shaping; Object recognition; Pixel; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference
Conference_Location :
Austin, TX
Print_ISBN :
0-8186-6952-7
Type :
conf
DOI :
10.1109/ICIP.1994.413310
Filename :
413310
Link To Document :
بازگشت