Search Results

Anthropometric databases consisting of both multimedia and relational content are increasingly becoming commonplace. These databases are huge and contain data with diverse formats, representations and models. Data mining provides a powerful mechanism to further explore and explain the data as contained in these heterogeneous repositories, focusing on discovering new relationships which cannot be found using standard information retrieval techniques. In particular, cluster analysis is a data mining technique which is used to group data records into unlabeled classes, e.g. to group individuals with similar body types, income and education levels into a cluster, using unsupervised learning. This paper introduces cluster analysis as a method to explore 3D body scans together with the relational anthropometric and demographic data as contained in an integrated multimedia anthropometric database.

Last year we presented the influence of pose on three-dimensional (3-D) shape classification in the context of a repeatability study. Meaning, that the subjects are repeatedly scanned 10 times and they attempt to assume the same pose each time. It was shown that changes due to a slight pose modification had no detrimental effects for shape classification. This paper discuss a second set of experiments, designed to test the stability of the geometric search engine in more extreme cases. The pose of the subjects between the scans are modified substantially, with an increasing amount of differences compared to the CAESAR protocol. Experimental results will be presented and discussed.

Cleopatra is a 3D database management software to classify the digitized 3D shape of the human bodies contained in the CAESAR database. The classification process is based on the geometric characteristics of the digitized data, along with a filtering process based on text information contained in both, the traditional anthropometric and the extracted measurements files. The information contained in the demographic file is also available for filtering. This paper discusses the experimental results of tests related to the subject's pose while the scan is made. This is a repeatability test where the subjects (a male and a female) are repeatedly scanned ten times. Each scan is made at different times and the subject attempts to assume the same pose each time.

This paper presents a new approach for the classification and retrieval of three-dimensional images and models from databases. A set of retrieval algorithms is introduced. These algorithms are content-based, meaning that the input is not made out of keywords but of three-dimensional models. Tensors of inertia, distribution of normal vectors, distribution of cords and multiresolution analysis are used to describe each model. Scale, shape or color or any combination of these parameters can search the database. A user friendly interface makes the retrieval operation simple and intuitive and make it possible to edit reference models according to the specifications of the user. Experimental results using a database of more than 1000 VRML models are presented.