期刊論文
學年 | 101 |
---|---|
學期 | 1 |
出版(發表)日期 | 2012-11-30 |
作品名稱 | A Novel Histogram-Based Multi-Threshold Searching Algorithm for Multilevel Color Thresholding |
作品名稱(其他語言) | |
著者 | Tsai, Chi-Yi; Liu, Tsung-Yen; Chen, Wei-Chieh |
單位 | 淡江大學電機工程學系 |
出版者 | Rijeka: InTech Open Access Publisher |
著錄名稱、卷期、頁數 | International Journal of Advanced Robotic Systems 9(262), pp.1-13 |
摘要 | Image segmentation is an important preliminary process required in object tracking applications. This paper addresses the issue of unsupervised multi‐colour thresholding design for colour‐based multiple objects segmentation. Most of the current unsupervised colour thresholding techniques require adopting a supervised training algorithm or a cluster‐number decision algorithm to obtain optimal threshold values of each colour channel for a colour‐of‐interest. In this paper, a novel unsupervised multi‐threshold searching algorithm is proposed to automatically search the optimal threshold values for segmenting multiple colour objects. To achieve this, a novel ratio‐map image computation method is proposed to efficiently enhance the contrast between colour and non¬colour pixels. The Otsu’s method is then applied to the ratio‐map image to extract all colour objects from the image. Finally, a new histogram‐based multi‐threshold searching algorithm is developed to search the optimal upper‐bound and lower‐bound threshold values of hue, saturation and brightness components for each colour object. Experimental results show that the proposed method not only succeeds in separating all colour objects-of-interest in colour images, but also provides satisfactory colour thresholding results compared with an existing multilevel thresholding method. |
關鍵字 | Multi-object; Segmentation; Multilevel Thresholding; Colour Thresholding; Multi-threshold Searching; Ratio-map Image |
語言 | en_US |
ISSN | 1729-8806 |
期刊性質 | |
收錄於 | SCI EI |
產學合作 | |
通訊作者 | Tsai, Chi-Yi |
審稿制度 | |
國別 | HRV |
公開徵稿 | |
出版型式 | 電子版 紙本 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/79668 ) |