Zobrazeno 1 - 10
of 287
pro vyhledávání: '"Cohen, Laurent D."'
Autor:
Zhang, Lin, Lu, Chenggang, Shi, Xin-yang, Shan, Caifeng, Zhang, Jiong, Chen, Da, Cohen, Laurent D.
Atherosclerosis is a chronic, progressive disease that primarily affects the arterial walls. It is one of the major causes of cardiovascular disease. Magnetic Resonance (MR) black-blood vessel wall imaging (BB-VWI) offers crucial insights into vascul
Externí odkaz:
http://arxiv.org/abs/2406.19485
Autor:
Bertrand, Théo, Cohen, Laurent D.
Segmentation of tubular structures in vascular imaging is a well studied task, although it is rare that we try to infuse knowledge of the tree-like structure of the regions to be detected. Our work focuses on detecting the important landmarks in the
Externí odkaz:
http://arxiv.org/abs/2311.07188
Geodesic models are known as an efficient tool for solving various image segmentation problems. Most of existing approaches only exploit local pointwise image features to track geodesic paths for delineating the objective boundaries. However, such a
Externí odkaz:
http://arxiv.org/abs/2309.04169
In this paper, we introduce an efficient method for computing curves minimizing a variant of the Euler-Mumford elastica energy, with fixed endpoints and tangents at these endpoints, where the bending energy is enhanced with a user defined and data-dr
Externí odkaz:
http://arxiv.org/abs/2308.15729
Autor:
Makaroff, Nicolas, Cohen, Laurent D.
When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results. This leads to questioning the possibility of using such an algorithm in applications where th
Externí odkaz:
http://arxiv.org/abs/2306.16098
Leveraging geodesic distances and the geometrical information they convey is key for many data-oriented applications in imaging. Geodesic distance computation has been used for long for image segmentation using Image based metrics. We introduce a new
Externí odkaz:
http://arxiv.org/abs/2306.16109
Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Since the theoretical sample complexity is nearly impossible to evaluate,
Externí odkaz:
http://arxiv.org/abs/2303.10608
Autor:
Groscot, Raphaël, Cohen, Laurent D.
Publikováno v:
14th International Conference on Digital Image Processing (ICDIP 2022), May 2022, Wuhan (Virtual), China
We present Deformable Voxel Grids (DVGs) for 3D shapes comparison and processing. It consists of a voxel grid which is deformed to approximate the silhouette of a shape, via energy-minimization. By interpreting the DVG as a local coordinates system,
Externí odkaz:
http://arxiv.org/abs/2211.11609
Publikováno v:
In Pattern Recognition January 2025 157
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit image features in conjunction with geometric
Externí odkaz:
http://arxiv.org/abs/2111.00794