Texture-driven parametric snakes for semi-automatic image segmentation
Autor: | Adrien Depeursinge, Anais Badoual, Michael Unser |
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Rok vydání: | 2019 |
Předmět: |
active contour
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology supervised learning wavelet 0202 electrical engineering electronic engineering information engineering interactive Segmentation active contour model Parametric statistics parametric snake Active contour model fisher's linear discriminant analysis Pixel business.industry circular harmonic wavelets segmentation Supervised learning 020207 software engineering Pattern recognition Filter (signal processing) Image segmentation Linear discriminant analysis Computer Science::Graphics Computer Science::Computer Vision and Pattern Recognition Signal Processing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business texture level set Software |
Zdroj: | Computer Vision and Image Understanding. 188:102793 |
ISSN: | 1077-3142 |
Popis: | We present a texture-driven parametric snake for semi-automatic segmentation of a single and closed structure in an image. We propose a new energy functional that combines intensity and texture information. The two types of image information are balanced using Fisher’s linear discriminant analysis. The framework can be used with any filter-based texture features. The parametric representation of the snake allows for easy and friendly user interaction while the framework can be trained on-the-fly from pixel collections provided by the user. We demonstrate the efficiency of the snake through an extensive validation on synthetic as well as on real data. Additionally, we show that the proposed snake is robust to noise and that it improves the segmentation performance when compared to an intensity-only scheme. |
Databáze: | OpenAIRE |
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