Compression, Classification and Image Segmentation

数据压缩旨在产生用于存储或传输的有效表示，以便在保持重构图像的高质量的同时传送或存储尽可能少的比特数。 统计分类和分割都涉及将图像的部分标记为特定类型或类别，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，或者它们可用于在智能浏览大型数据库时自动提取特征。 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，并将图像分解为有用的组件，有效地将图像作为一个整体来表示。 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通用算法。 该项目致力于开发理论和算法，用于联合执行这些操作和相关的信号处理，例如从观测数据估计概率分布或模型。 该方法涉及优化单个组合系统，可能会发生冲突的组合目标，如最大限度地提高信噪比和最小化比特率和贝叶斯风险。 理论将通过模拟两个人工源来加强，其中理论性能界限可用于比较，以及从医疗和文档应用中提取的真实世界的例子。

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.