Automatic Thresholding Techniques for Optical Images

Image segmentation is one of the important tasks in computer vision and image processing. Thresholding is a simple but most effective technique in segmentation. It based on classify image pixels into object and background depended on the relation between the gray level value of the pixels and the threshold. Otsu technique is a robust and fast thresholding techniques for most real world images with regard to uniformity and shape measures. Otsu technique splits the object from the background by increasing the separability factor between the classes. Our aim form this work is (1) making a comparison among five thresholding techniques (Otsu technique, valley emphasis technique, neighborhood valley emphasis technique, variance and intensity contrast technique, and variance discrepancy technique)on different applications. (2) determining the best thresholding technique that extracted the object from the background. Our experimental results ensure that every thresholding technique has shown a superior level of performance on specific type of bimodal images.