Pattern Recognition
Extraction of binary character/graphics images from grayscale document images
CVGIP: Graphical Models and Image Processing
A new dichotomization technique to multilevel thresholding devoted to inspection applications
Pattern Recognition Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Rough ν-support vector regression
Expert Systems with Applications: An International Journal
Reducing samples for accelerating multikernel semiparametric support vector regression
Expert Systems with Applications: An International Journal
A novel multi-threshold segmentation approach based on differential evolution optimization
Expert Systems with Applications: An International Journal
Multikernel semiparametric linear programming support vector regression
Expert Systems with Applications: An International Journal
Modified local entropy-based transition region extraction and thresholding
Applied Soft Computing
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
A comparison of nature inspired algorithms for multi-threshold image segmentation
Expert Systems with Applications: An International Journal
Finite Newton method for implicit Lagrangian support vector regression
International Journal of Knowledge-based and Intelligent Engineering Systems
Hi-index | 0.02 |
Threshold selection is an important topic and also a critical preprocessing step for image analysis, pattern recognition and computer vision. In this letter, a novel automatic image thresholding approach only from the support vectors is proposed. It first fits the 1D histogram of a given image by support vector regression (SVR) to obtain all boundary support vectors and then sifts automatically so-needed (multi-) threshold values directly from the support vectors rather than the optimized extrema of the fitted histogram in which finding the extrema is, in general, difficult. The proposed approach is not only computationally efficient but also does not require prior assumptions whatsoever to be made about the image (type, features, contents, stochastic model, etc.). Such an algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively, and the resulting images can preserve the main features of the components of the original images very well.