Model-based image segmentation

基于模型的图像分割

• 批准号: 227766-2011
• 负责人: ElSakka, Mahmoud
• 金额: $ 1.75万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=478601
• 关键词: Model; based; image; segmentation

Image de-noising and image segmentation represent a crucial part in many image processing and computer vision applications. The goal of de-noising is to smooth homogenous regions of an image while preserving the region boundaries (i.e., edges). Meanwhile, segmentation techniques aim to extract the boundaries of homogenous regions. In traditional pipelined de-noising/segmentation schemes, the segmentation step may suffer from a loss of vital information due to the de-noising step, or due to the excess of noise. Combining these two steps should preserve much of this lost information compared to the traditional methods.

图像去噪和图像分割是许多图像处理和计算机视觉应用中的关键部分。去噪的目标是在保持区域边界（即，边缘）。同时，分割技术的目标是提取同质区域的边界。在传统的流水线去噪/分割方案中，分割步骤可能由于去噪步骤或由于噪声过量而遭受重要信息的损失。与传统方法相比，结合这两个步骤应该保留大部分丢失的信息。