Compression, Classification and Image Segmentation

压缩、分类和图像分割

• 批准号: 9706284
• 负责人: Robert Gray
• 金额: $ 37.57万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 2000-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9706284&HistoricalAwards=false
• 关键词: Compression; Classification; Image; Segmentation

Data compression aims at producing an efficient representation for storage or transmission in order to communicate or store the smallest possible number of bits while retaining high quality in the reconstructed image. Statistical classification and segmentation are both concerned with labeling parts of an image as being of a certain type or class, such as tumor or normal tissue in a medical image or text, graphics, or photographs in a document page. Classification and segmentation can be used to assist human users of images, as in highlighting suspicious tissue in a medical image or defects in an imaged circuit, or they can be used for the automatic extraction of features in intelligent browsing of large databases. All three operations have in common the goal of efficiently representing an image as a whole, by optimally trading off quality and cost or distortion, an din decomposing the image into useful components. Most of the systems involving these operations, however, focus on each separate operation rather than on combining them into common algorithms with multiple goals. This project is devoted to the development of theory and algorithms for jointly performing these operations and related signal processing such as the estimation of probability distributions or models from observed data. The approach involves optimizing single combined systems subject to possibly conflicting combined goals, such as maximizing signal-to-noise ratios and minimizing bit rates and Bayes risk. Theory will be reinforced by simulations for both artificial sources, where theoretical performance bounds can be used for comparison, and real-world examples drawn from medical and document applications.

数据压缩的目的是产生用于存储或传输的有效表示，以便在保持重建图像的高质量的同时传递或存储尽可能小的比特数。统计分类和分割都涉及将图像的某些部分标记为某一类型或类别，例如医学图像中的肿瘤或正常组织或文档页面中的文本、图形或照片。分类和分割可用于帮助图像的人类用户，如突出显示医学图像中的可疑组织或成像电路中的缺陷，或者它们可用于在大型数据库的智能浏览中自动提取特征。所有这三种操作都有一个共同的目标，即通过最佳地权衡质量和成本或失真，将图像分解成有用的分量，从而有效地表示图像作为一个整体。然而，大多数涉及这些操作的系统都专注于每个单独的操作，而不是将它们组合成具有多个目标的共同算法。该项目致力于开发联合执行这些运算和相关信号处理的理论和算法，例如根据观测数据估计概率分布或模型。该方法涉及到在满足可能冲突的组合目标的情况下优化单个组合系统，例如最大化信噪比和最小化比特率和贝叶斯风险。理论将通过对人工信号源的模拟来加强，其中理论性能界限可以用于比较，以及来自医学和文档应用的真实世界的例子。