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Informations générales |
| Auteur |
Tsai, Chi-Yi; Liu, Tsung-Yen; Chen, Wei-Chieh |
| Publié |
InTech Open Access Publisher, 2012
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| Abstract |
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 noncolour
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 objectsof‐
interest in colour images, but also provides satisfactory
colour thresholding results compared with an existing
multilevel thresholding method. |
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International Journal of Advanced Robotic Systems
Auteur: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publié: 2004
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