The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Christophe Odet - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Unifying Variational Approach and region growing segmentation
2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
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Unifying Variational Approach and region growing segmentation
2010 18th European Signal Processing Conference, 2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
Jean-loïc Rose - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Unifying Variational Approach and region growing segmentation
2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
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Unifying Variational Approach and region growing segmentation
2010 18th European Signal Processing Conference, 2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
Thomas Grenier - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Unifying Variational Approach and region growing segmentation
2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
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Unifying Variational Approach and region growing segmentation
2010 18th European Signal Processing Conference, 2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
Chantal Revol-muller - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Unifying Variational Approach and region growing segmentation
2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
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Unifying Variational Approach and region growing segmentation
2010 18th European Signal Processing Conference, 2010Co-Authors: Jean-loïc Rose, Thomas Grenier, Chantal Revol-muller, Christophe OdetAbstract:Region growing is one of the most popular image segmentation methods. The algorithm for region growing is easily understandable but criticized for its lack of theoretical background. In order to overcome this weakness, we propose to describe region growing in a new framework using a Variational Approach that we called Variational Region Growing (VRG). Variational Approach is commonly used in image segmentation methods such as active contours or level sets, but is rather original in the context of region growing. It relies on an evolution equation derived from an energy minimization, that drives the evolving region towards the targeted solution. Here, the energy minimization and the VRG robustness to the initial seeds location are performed on gray-level and color images.
Amal A. Farag - One of the best experts on this subject based on the ideXlab platform.
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Variational Approach for small-size lung nodule segmentation
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Amal A. FaragAbstract:This paper describes a novel Variational Approach for segmentation of small-size lung nodules which may be detected in low dose CT (LDCT) scans. These nodules do not possess distinct shape or appearance characteristics; hence, their segmentation is enormously difficult, especially at small size (≤ 1 cm). Variational methods hold promise in these scenarios despite the difficulties in estimation of the energy function parameters and the convergence. The proposed method is analytic and has a clear implementation strategy for LDCT scans. We show the effectiveness of the algorithm for segmenting various types of nodules regardless of their location in the lung tissue.
