Geometric Analysis in Cultural Heritage

We present a review of recent techniques for performing geometric analysis in cultural heritage applications, targeting the broad community of researchers and practitioners in cultural heritage computing. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation, and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives

The value of visualization

The field of visualization is getting mature. Many problems have been solved, and new directions are sought for. In order to make good choices, an understanding of the purpose and meaning of visualization is needed. Especially, it would be nice if we could assess what a good visualization is. In this paper an attempt is made to determine the value of visualization. A technological viewpoint is adopted, where the value of visualization is measured based on effectiveness and efficiency. An economic model of visualization is presented, and benefits and costs are established. Next, consequences (brand limitations of visualization are discussed (including the use of alternative methods, high initial costs, subjective/less, and the role of interaction), as well as examples of the use of the model for the judgement of existing classes of methods and understanding why they are or are not used in practice. Furthermore, two alternative views on visualization are presented and discussed: viewing visualization as an art or as a scientific discipline. Implications and future directions are identified

Evaluation of fiber clustering methods for diffusion tensor imaging

Fiber tracking is a standard approach for the visualization of the results of diffusion tensor imaging (DTI). If fibers are reconstructed and visualized individually through the complete white matter, the display gets easily cluttered making it difficult to get insight in the data. Various clustering techniques have been proposed to automatically obtain bundles that should represent anatomical structures, but it is unclear which clustering methods and parameter settings give the best results. We propose a framework to validate clustering methods for white-matter fibers. Clusters are compared with a manual classification which is used as a ground truth. For the quantitative evaluation of the methods, we developed a new measure to assess the difference between the ground truth and the clusterings. The measure was validated and calibrated by presenting different clusterings to physicians and asking them for their judgement. We found that the values of our new measure for different clusterings match well with the opinions of physicians. Using this framework, we have evaluated different clustering algorithms, including shared nearest neighbor clustering, which has not been used before for this purpose. We found that the use of hierarchical clustering using single-link and a fiber similarity measure based on the mean distance between fibers gave the best results