Combinatorial Optimization for Computer Vision and Biomedical Image Analysis

自动计算机/机器人视觉用于制造、医疗保健、安全和多媒体是一个非常活跃的研究领域，但真实的数据性能在质量、速度或两者方面仍远未达到完美。由于各种数字媒体和计算资源的可用性激增，潜在应用程序的数量显着增加，但数据的大小和复杂性也随之增加。尽管在过去的10-15年中取得了重大进展，但计算机视觉和生物医学图像分析社区正在寻找新的算法和数学模型，即使是分割，重建，检测等基本问题。算法的鲁棒性、计算效率和可扩展性是至关重要的因素。在具有高维特征的大真实的数据背景下，高阶正则化约束的有效优化方法仍然是一个挑战。

Automatic computer/robot vision for manufacturing, health care, security, and multi-media is a very active area of research, but real data performance is still far from perfect in quality, speed, or both. Due to explosion in availability of ever diverse digital media and computational resources, the number of potential applications grew significantly, but so did the size and complexities of the data. Despite significant progress in the last 10-15 years, the computer vision and biomedical image analysis communities are looking for new algorithms and mathematical models even for basic problems like segmentation, reconstruction, detection. Robustness, computational efficiency, and scalability of algorithms are crucial factors. Efficient optimization methods for high-order regularization constraints in the context large real data with high-dimensional features remain a challenge.