A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Histogram thresholding by minimizing graylevel fuzziness
Information Sciences: an International Journal
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Image segmentation using fuzzy homogeneity criterion
Information Sciences: an International Journal
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation by histogram thresholding using hierarchical cluster analysis
Pattern Recognition Letters
Automatic thresholding for defect detection
Pattern Recognition Letters
On minimum variance thresholding
Pattern Recognition Letters
Information Sciences: an International Journal
Detecting and tracking regional outliers in meteorological data
Information Sciences: an International Journal
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Information Sciences: an International Journal
Information Sciences: an International Journal
Associating visual textures with human perceptions using genetic algorithms
Information Sciences: an International Journal
Optimal edge detection in two-dimensional images
IEEE Transactions on Image Processing
Improving feature space based image segmentation via density modification
Information Sciences: an International Journal
Performance evaluation of moment-based watermarking methods: A review
Journal of Systems and Software
A new approach to estimate lacunarity of texture images
Pattern Recognition Letters
Hi-index | 0.07 |
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.