Title:
A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications
A Recursive Segmentation and Classification Scheme for Improving Segmentation Accuracy and Detection Rate in Real-time Machine Vision Applications
Author(s)
Ding, Yuhua
Vachtsevanos, George J.
Yezzi, Anthony
Zhang, Yingchuan
Wardi, Yorai Y.
Vachtsevanos, George J.
Yezzi, Anthony
Zhang, Yingchuan
Wardi, Yorai Y.
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Abstract
Segmentation accuracy is shown to be a critical factor in
detection rate improvement. With accurate segmentation,
results are easier to interpret, and classification
performance is better. Therefore, it is required to have a
performance measure for segmentation evaluation.
However, a number of restrictions limit using existing
segmentation performance measures. In this paper a recursive segmentation and classification scheme is proposed to improve segmentation accuracy and
classification performance in real-time machine vision applications. In this scheme, the confidence level of
classification results is used as a new performance
measure to evaluate the accuracy of segmentation
algorithm. Segmentation is repeated until a classification
with desired confidence level is achieved. This scheme can be implemented automatically. Experimental results
show that it is efficient to improve segmentation accuracy
and the overall detection performance, especially for
real-time machine vision applications, where the scene is
complicated and a single segmentation algorithm cannot
produce satisfactory results.
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Date Issued
2002
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Text
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Proceedings