报告题目：Active Contour Models Driven by Hyperbolic Mean Curvature Flow for Image Segmentation

报告人：黄守军 教授（浙江师范大学）

报告时间：12月7日 14：30-15：30

报告地点：腾讯会议：553-739-914

主办单位：5278论坛

摘要： Parabolic mean curvature flow-driven active contour models (PMCF-ACMs) are widely used for image segmentation, yet they suffer severe degradation under high-intensity noise because gradient-descent evolutions exhibit the well-known zig-zag phenomenon. To overcome this drawback, we propose hyperbolic mean curvature flow-driven ACMs (HMCF-ACMs). This novel framework incorporates an adjustable acceleration field to autonomously regulate curve evolution smoothness, providing dual degrees of freedom for adaptive selection of both initial contours and velocity fields. We rigorously prove that HMCF-ACMs are normal flows and establish their numerical equivalence to wave equations through a level set formulation with signed distance functions. An efficient numerical scheme combining spectral discretization and optimized temporal integration is developed to solve the governing equations, and its stability condition is derived through Fourier analysis. Extensive experiments on natural and medical images validate that HMCF-ACMs achieve superior performance under high-noise conditions, demonstrating reduced parameter sensitivity, enhanced noise robustness, and improved segmentation accuracy compared to PMCF-ACMs.

报告人简介： 黄守军，浙江师范大学数理医学院副院长、党委委员‌，双龙特聘教授，研究方向为医学图像处理和偏微分方程。在国内外重要期刊上发表学术论文20余篇。主持国家自然科学基金项目2项，安徽省自然科学基金面上项目2项以及安徽省高校优秀青年人才基金项目等基金。曾访问香港大学、香港城市大学、美国理海大学、美国宾夕法尼亚州立大学等。作为参与人获浙江省高校优秀科研成果奖一等奖、多次指导学生参加全国大学生数学建模大赛和数学竞赛并获奖。曾主持和参与多项省级教研课题。