Pattern Recognition
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
A new approach for multilevel threshold selection
CVGIP: Graphical Models and Image Processing
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Image thresholding by maximizing the index of nonfuzziness of the 2-D grayscale histogram
Computer Vision and Image Understanding
Thresholding technique with adaptive window selection for uneven lighting image
Pattern Recognition Letters
An Efficient Gray-level Clustering Algorithm for Image Segmentation
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
Image segmentation by automatic histogram thresholding
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization
Expert Systems with Applications: An International Journal
Modeling and Estimation of the Dynamics of Planar Algebraic Curves via Riccati Equations
Journal of Mathematical Imaging and Vision
Unsupervised range-constrained thresholding
Pattern Recognition Letters
Two-dimensional clustering algorithms for image segmentation
WSEAS Transactions on Computers
Adaptive binarization method for enhancing ancient malay manuscript images
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
Color texture segmentation based on image pixel classification
Engineering Applications of Artificial Intelligence
A comparison of nature inspired algorithms for multi-threshold image segmentation
Expert Systems with Applications: An International Journal
Image contrast enhancement for preserving mean brightness without losing image features
Engineering Applications of Artificial Intelligence
An Adaptive Thresholding algorithm of field leaf image
Computers and Electronics in Agriculture
Maximum similarity thresholding
Digital Signal Processing
Hi-index | 0.10 |
A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time.