by Shin, B-S, Cha, E-Y, Woo, YN and Klette, R
Abstract:
In a process of insect footprint recognition, footprint segments need to be extracted from scanned insect footprints in order to find out appropriate features for classification. In this paper, we use a clustering method in a preprocessing stage for extraction of insect footprint segments. In general, sizes and strides of footprints may be different according to type and size of an insect for recognition. Therefore we propose a method for insect footprint segment extraction using an improved ART2 algorithm regardless of size and stride of footprint pattern. In the improved ART2 algorithm, an initial threshold value for clustering is determined automatically using the contour shape of the graph created by accumulating distances between all the spots within a binarized footprint pattern image. In the experimental results, applying the proposed method to two kinds of insect footprint patterns, we illustrate that clustering is accomplished correctly. © Springer-Verlag Berlin Heidelberg 2007.
Reference:
Segmentation of scanned insect footprints using ART2 for threshold selection (Shin, B-S, Cha, E-Y, Woo, YN and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 4872 LNCS, 2007.
Bibtex Entry:
@inproceedings{shin2007segmentationselection,
author = "Shin, B-S and Cha, E-Y and Woo, YN and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "311--320",
title = "Segmentation of scanned insect footprints using ART2 for threshold selection",
volume = "4872 LNCS",
year = "2007",
abstract = "In a process of insect footprint recognition, footprint segments need to be extracted from scanned insect footprints in order to find out appropriate features for classification. In this paper, we use a clustering method in a preprocessing stage for extraction of insect footprint segments. In general, sizes and strides of footprints may be different according to type and size of an insect for recognition. Therefore we propose a method for insect footprint segment extraction using an improved ART2 algorithm regardless of size and stride of footprint pattern. In the improved ART2 algorithm, an initial threshold value for clustering is determined automatically using the contour shape of the graph created by accumulating distances between all the spots within a binarized footprint pattern image. In the experimental results, applying the proposed method to two kinds of insect footprint patterns, we illustrate that clustering is accomplished correctly. © Springer-Verlag Berlin Heidelberg 2007.",
isbn = "9783540771289",
issn = "0302-9743",
eissn = "1611-3349",
keyword = "ART2 algorithm",
keyword = "Clustering",
keyword = "Insect footprint segmentation",
language = "eng",
}