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.

数据压缩旨在产生用于存储或传输的有效表示形式，以便在重建图像中保留高质量的同时传达或存储最小数量的位。 统计分类和分割都与将图像的部分标记为某种类型或类别的部分，例如在文档页面中的医学图像或文本，图形或照片中的肿瘤或正常组织。 分类和分割可用于帮助人类的图像用户，例如在成像电路中突出显示医疗图像或缺陷中的可疑组织，也可以用于自动提取大型数据库的智能浏览中的特征。 这三个操作的共同点是通过最佳地交易质量，成本或失真，将图像分解为有用的组件来有效地表示整个图像的目标。 但是，涉及这些操作的大多数系统都集中在每个单独的操作上，而不是将它们合并为具有多个目标的常见算法。 该项目致力于共同执行这些操作和相关信号处理的理论和算法的发展，例如估算概率分布或从观察到的数据中进行模型。 该方法涉及优化可能具有冲突的组合目标的单一组合系统，例如最大化信噪比，最小化比特率和贝叶斯风险。 对于两个人工来源的模拟，理论绩效界限都可以用于比较，并从医学和文档应用中绘制的现实示例来加强理论。