An adaptable threshold detector

A data set often comprises some data classes. For example, a gray-scale image may consist of some objects, each of which has similar pixels' gray-scales. The threshold obtained by Otsu's thresholding method (OTM) is biased towards certain data class with larger variance or larger number of data when the variances or the numbers of data among classes are quite different. In this paper, Adaptable Threshold Detector (ATD) is proposed to improve the effectiveness of OTM in determining proper thresholds by dividing class variance by class interval. ATD is more versatile at selecting application-dependent thresholds by changing two parameter values which describe the relative importance among data size, standard deviation, and class interval of a class. In this paper, ATD is applied to crop the expected objects from images to verify its effect upon thresholding. Experimental results demonstrate that ATD is able to perform better than OTM in segmenting objects from images, besides excelling over the Valley-Emphasis Method (VEM) and the Minimum Class Variance Thresholding Method (MCVTM). ATD is also suitable for separating objects from serialized video images, i.e. computerized tomography. Crown Copyright (c) 2010 Published by Elsevier Inc. All rights reserved

An Adaptable Threshold Detector

A data set often comprises some data classes. For example, a gray-scale image may consist of some objects, each of which has similar pixels' gray-scales. The threshold obtained by Otsu's thresholding method (OTM) is biased towards certain data class with larger variance or larger number of data when the variances or the numbers of data among classes are quite different. In this paper, AdaptableThresholdDetector (ATD) is proposed to improve the effectiveness of OTM in determining proper thresholds by dividing class variance by class interval. ATD is more versatile at selecting application-dependent thresholds by changing two parameter values which describe the relative importance among data size, standard deviation, and class interval of a class. In this paper, ATD is applied to crop the expected objects from images to verify its effect upon thresholding. Experimental results demonstrate that ATD is able to perform better than OTM in segmenting objects from images, besides excelling over the Valley-Emphasis Method (VEM) and the Minimum Class Variance Thresholding Method (MCVTM). ATD is also suitable for separating objects from serialized video images, i.e. computerized tomography