Software for categorizing a cloud of more than 300,000 three-dimensional (3-D) surface data points, captured from a human subject, is presented. The software is part of an incremental approach that progressively refines the identification of human anthropometric landmarks. The first phase of identification is to orient and segment the human body data points. A step-by-step method for these tasks is presented. One of the algorithms, a discrete point cusp detector, plays a fundamental role in separating the data cloud. The theory and operation of the algorithm is explained. The discrete cusp detector can be used to separate any two cylindrical objects, in (3-D) data, whose boundaries touch. The software has been tested on over a hundred different body scan data sets and shown to be robust.
The Cyberware WB4 whole body scanner generates a high-resolution data set ofthe outer surface ofthe human body. The acquisition of anthropometric data from this data set is important for the development of custom sizing for the apparel industry. Software for locating anthropometric landmarks from a cloud ofmore than 200,000 three-dimensional data points, captured from a human subject, is presented. The first phase of identification is to locate externally placed fudicials on the human body using luminance information captured at scan time. The fudicials are then autonomously labeled and categorized according to their general position and anthropometric significance in the scan. Once registration of the landmarks is complete, body measurements may be extracted for apparel sizing.
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