<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.</p></abstract>

Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Robust Active Contour Model Guided by Local Binary Pattern Stopping Function"}

Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with...

Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

Online veröffentlicht: 30. Nov. 2017

Seitenbereich: 165 - 182

DOI: https://doi.org/10.1515/cait-2017-0047

Schlüsselwörter
Active contour models, edge stopping function, image segmentation, local binary pattern

© by Abdallah Azizi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

Online veröffentlicht: 30. Nov. 2017

Seitenbereich: 165 - 182

DOI: https://doi.org/10.1515/cait-2017-0047

SchlüsselwörterActive contour models, edge stopping function, image segmentation, local binary pattern

© by Abdallah Azizi

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Active contour models, edge stopping function, image segmentation, local binary pattern