Bidirectional Image Thresholding Algorithm Using Combined Edge Detection And P-tile Algorithms

Abstract

The main disadvantage of traditional global thresholding techniques is that they do not have an ability to exploit information of the characteristics of target images that they threshold. In this paper, we propose a new approach based on combination of modified p-tile and edge detection algorithms to have more accurate object segmentation. Using our proposed method, it is shown that almost all of our experiments resulted to better object segmentation than using traditional methods.

Single web camera robust interactive eye-gaze tracking method

“…1. Eyes inverted binarization with adaptive thresholding; a) original web camera image; b) image after histogram equalization; c) manual thresholding; d) mean value thresholding; e) Otsu method based thresholding Ref [67]; f) p-tile thresholding, after Ref [68] Introductory step was eye's image histogram equalization (Fig. 1b).…”

“…1c), and several adaptive threshold estimating methods were tested for iris and upper eyelid segmentation (Fig. 1d, 1e, 1f) [66][67][68][69]. Appropriate threshold value should let separate iris and upper eyelid region contour from the eyebrow contour and other eye region elements.…”

“…The most valuable were Otsu [67] (Fig. 1e) and empirically adjusted (19%) percentile thresholding [68] (Fig. 1f) based binarization.…”

Retinal Exudates Detection using Binary Operation and Hard Exudates Classification using Support Vector Machine

“…Where, represents the number of pixels in the region and is the pixels out of the region . The threshold method to get the binary image is deduced from the proven method such as P-tile method [18] and Nilback's method [19] [20] . The largest connected component is assumed to be the optic disc.…”

Attribute Based Level Adaptive Thresholding Algorithm (ablata) For Image Compression And Transmission

“…To achieve the best possible features, it is important for the image under scrutiny should be sharp, clear, and free of noise and artifacts [1][2]. Though, the technologies for acquiring digital images are improving, resulting in images of higher and higher resolution and quality, noise remains as an issue in many images applications whether it be concerned with medical field or of robotics [12]. The presence of noise will degrade the quality of image, and even conceal image details, which consequently influences the subsequent process of image segmentation, object recognition, feature extraction and quantitative analysis [4] [15].…”