Topic
Otsu's method
About: Otsu's method is a research topic. Over the lifetime, 1439 publications have been published within this topic receiving 20654 citations.
Papers published on a yearly basis
Papers
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
5,205 citations
TL;DR: A faster version of Otsu's method for improving the efficiency of computation for the optimal thresholds of an image by determining the modified between-class variance by accessing a look-up table is quicker than that by performing mathematical arithmetic operations.
Abstract: Otsu reference proposed a criterion for maximizing the between-class variance of pixel intensity to perform picture thresholding. However, Otsu's method for image segmentation is very time-consuming because of the inefficient formulation of the be- tween-class variance. In this paper, a faster version of Otsu's method is proposed for improving the efficiency of computation for the optimal thresholds of an image. First, a criterion for maximizing a modified between-class variance that is equivalent to the criterion of maximizing the usual between-class variance is proposed for image segmen- tation. Next, in accordance with the new criterion, a recursive algorithm is designed to efficiently find the optimal threshold. This procedure yields the same set of thresholds as the original method. In addition, the modified between-class variance can be pre-computed and stored in a look-up table. Our analysis of the new criterion clearly shows that it takes less computation to compute both the cumulative probability (zeroth order moment) and the mean (first order moment) of a class, and that determining the modified between-class variance by accessing a look-up table is quicker than that by performing mathematical arithmetic operations. For example, the experimental results of a five-level threshold selection show that our proposed method can reduce down the processing time from more than one hour by the conventional Otsu's method to less than 107 seconds.
1,077 citations
TL;DR: The Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions is revised and the performance of the revised method, the valley-emphasis method, on common defect detection applications is tested.
563 citations
TL;DR: This paper proves that Otsu threshold is equal to the average of the mean levels of two classes partitioned by this threshold, and proposes an improved Otsi algorithm that constrains the search range of gray levels.
529 citations
18 Dec 2004
TL;DR: The Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions is revised and improved and tested the performance of the revised method on common defect detection applications.
Abstract: Automatic thresholding has been widely used in the machine vision industry for automated visual inspection of defects. A commonly used thresholding technique, the Otsu method, provides satisfactory results for thresholding an image with histogram of bimodal distribution. This method, however, fails if the histogram is unimodal or close to unimodal. For defect detection applications, defects range from no defect, small defect, to large defect, which means the gray-level distributions range from unimodal to bimodal. In this paper, we revised and improved the Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions. We also tested the performance of the revised method on common defect detection applications.
439 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 16 |
| 2024 | 32 |
| 2023 | 53 |
| 2022 | 83 |
| 2021 | 51 |
| 2020 | 59 |