图像分割是由图像处理向图像分析与理解过渡的重要桥梁，是计算机视觉等相关研究领域的基础科学问题。近年来各种新理论和方法不断涌现，其中水平集分割方法具有更为统一的理论与框架，能够通过能量泛函整合图像的低层特征和外部约束条件实现分割，成为目前图像分割方法的一个重要分支。然而，目前的水平集图像分割方法并不完善，仍存在（1）水平集函数簇构成的空间非凸，导致能量泛函存在局部极值等优化问题；（2）水平集函数演化中函数初始化、演化停止条件设计不尽完善，对目标边缘和形状语义刻画能力有限，且不能实现自动分割；（3）水平集函数演化缺少并行的快速实现方法，限制了分割方法的实际应用。本项目基于人类视觉感知模型、稀疏表示以及格子波尔兹曼方法，研究符合人眼视觉感知特性且具有形状语义选择性的快速水平集图像分割方法。以上研究有助于完善和扩展现有水平集图像分割的理论与方法，也为图像分析与理解等高层应用提供实用工具。

Being a fundamental problem in computer vision and the related research fields, image segmentation is an important bridge from image processing to image analysis and understanding. In recent years, many new theories and methods rose up, of which level set based image segmentation methods have became a popular and important branch for providing a general framework for image segmentation. This framework integrates the low level image features and the different constraints into an energy functional to realize image segmentation, but it is not perfect and still has many defect issues as follows, (1) the space spanned by level set functions is not convex, which causes some optimal problems, e.g., the local optimums; (2) in the evolution of level set function, the designs of initialization and stopping criterion are not qualified enough to depict the object boundaries and shape semantic, meanwhile, the segmentation methods cannot be executed automatically; (3) the level set based image segmentation methods are lack of the fast and parallel computing realizations, which limits their extension and application. Based on human visual perception model, sparse representation and lattice Boltzmann method, our applicant attempts to realize the level set based image segmentation methods conforming visual perception and driven by shape semantics. These all research contents contribute to accomplishing the theorem and the application of active contour model for image segmentation and providing more practical segmentation tools to the high-level applications of image techniques.