A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Optimal thresholding—a new approach
Pattern Recognition Letters
Extraction of binary character/graphics images from grayscale document images
CVGIP: Graphical Models and Image Processing
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
A new dichotomization technique to multilevel thresholding devoted to inspection applications
Pattern Recognition Letters
Journal of Global Optimization
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Performance Evaluation of Thresholding Algorithms for Optical character Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Novel Image Segmentation Approach Based on Particle Swarm Optimization
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Expert Systems with Applications: An International Journal
A hybrid particle swarm optimisation with differential evolution approach to image segmentation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Expert Systems with Applications: An International Journal
A comparison of nature inspired algorithms for multi-threshold image segmentation
Expert Systems with Applications: An International Journal
Block-matching algorithm based on differential evolution for motion estimation
Engineering Applications of Artificial Intelligence
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
Hi-index | 12.05 |
Threshold selection is a critical preprocessing step for image analysis, pattern recognition and computer vision. On the other hand differential evolution (DE) is a heuristic method for solving complex optimization problems, yielding promising results. DE is easy to use, keeps a simple structure and holds acceptable convergence properties and robustness. In this work, a novel automatic image multi-threshold approach based on differential evolution optimization is proposed. Hereby the segmentation process is considered to be similar to an optimization problem. First, the algorithm fills the 1-D histogram of the image using a mix of Gaussian functions whose parameters are calculated using the differential evolution method. Each Gaussian function approximating the histogram represents a pixel class and therefore a threshold point. The proposed approach is not only computationally efficient but also does not require prior assumptions whatsoever about the image. The method is likely to be most useful for applications considering different and perhaps initially unknown image classes. Experimental results demonstrate the algorithm's ability to perform automatic threshold selection while preserving main features from the original image.