Texture-driven parametric snakes for semi-automatic image segmentation

Autor: Adrien Depeursinge, Anais Badoual, Michael Unser
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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