Multi-region level set image segmentation based on image cartoon-texture decomposition model

Xiao, Yin; Guo, Jianping; Zhong, Chong-Fa; Yao, Y. H.; Zhang, Zhe; Wei, Yi

doi:10.1109/isbb.2011.6107653

Cited by 3 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can not accurately locate the characteristic boundary of image with fuzzy edge, so the image segmentation is not accurate enough. In other words, accurately determine image characteristic boundary is the key step of image segmentation [7]. Usually the gradient length size ( I ∇ ) of each image point is used to determine image edge ( I ∇ is smaller in smooth area, bigger on the edges ).…”

Section: Edge Detection Operatormentioning

confidence: 99%

A New Image Segmentation Model

Liu

Zhang

2014

AMM

View full text Add to dashboard Cite

To build a new image segmentation model based on level set theory : Add edge detection operator to edgeless active contour model to detect local information; introduce adaptive coefficient of area item to let the model autonomously adjust and evolve curve position according to image information; adopt weighted average gray value to replace traditional absolute mean value to reduce error and improve segmentation result. Experimental result comparison shows that the new model can detect global information and local information at the same time, adaptively adjust curve evolution direction, and has a fast segmentation speed. Compared to edgeless active contour model, the new model is a more effective image segmentation method as it has greater advantages in image segmentation accuracy and computational complexity.

show abstract

Section: Edge Detection Operatormentioning

confidence: 99%