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Automated Techniques in Anthropometry Using A AUTOMATED TECHNIQUES IN ANTHROPOMETRY USING A THREE DIMENSIONAL LASER SCANNER A Thesis Presented to The Faculty of the Fritz J. and Dolores H. Russ College of Engineering and Technology Ohio University In Partial Fulfillment of the Requirement for the Degree Master of Science by Erick A. Lewark August, 1998 ACKNOWLEDGEMENTS: The author would like to thank all persons involved in the writing of this thesis. Special thanks must go to Dr. Joseph Nurre, my academic and professional advisor, Amy E. Lewark, my wife (the source of much of my inner strength and ambition), and the Defense Logistics Agency's Apparel Research Network for funding research efforts in this emerging field. TABLE OF CONTENTS: 1. INTRODUCTION ..........................................................................................................1 1.1. History of Anthropometry .........................................................................................2 1.2. Present Applications .................................................................................................4 1.3. Review of Scanning Technology ..............................................................................6 1.4. Review of Anthropometric Software ........................................................................9 1.5. Problem Statement .................................................................................................10 2 .METHODS ...................................................................................................................11 2.1. Recognition of Surface Landmarks .........................................................................11 2.2. Shape Analysis .......................................................................................................20 3 . RESULTS .....................................................................................................................29 3.1 . Landscape Marker Recognition ..............................................................................29 3.2. Wrist Identification .................................................................................................33 4 . DISCUSSION AND CONCUSIONS ..........................................................................36 4.1 . Fudicial Landmark Location ...................................................................................36 4.2. Wrist Identification .................................................................................................37 4.3. Summary .................................................................................................................38 6. BIBLIOGRAPHY ........................................................................................................40 7 . APPENDIX ...................................................................................................................43 LIST OF FIGURES: Figure 1. The Cybenvare WB4 Whole-Body Scanner, with subject. (Photo courtesy of Cybenvare, Inc.) ...........................................................................................................7 Figure 2. Flow diagram of algorithms used to locate fudicials in 3-D scan data. ............13 Figure 3. Four two-dimensional texture maps generated in a typical Cybenvare WB4 scan. (Participant's identifying features were removed to assure anonymity.)......... .14 Figure 4. Matrix used in filtering texture maps. A = 1/13, B = -1136. This matrix is convolved with the original texture maps to enhance the appearance of fudicials in a scan... ..........................................................................................................................16 Figure 5. The four two-dimensional texture maps after filtering. Bright points indicate located fudicials. .......................................................................................................,17 Figure 6. Two dimensional representation of neigbor finding routine used to group marker candidates. Distance to every point remaining in scan is computed for each point, points within a distance r are classified as neighbors. The union of the intersecting sets yields marker candidates. ...............................................................-18 Figure 7. Flow chart of procedures used to find location of the wrist in human body scan data. ............................................................................................................................2 1 Figure 8. Front and side views of data captured from a 3-D scan file. The body shown is in a position ideally suited for successful wrist location ............................................22 Figure 9. Scan data segmented into the six major anatomical sections: right and left arms and legs, torso and head, as performed by the segmentation software developed by Nurre (1 997). .........................................................................................................23 Figure 10. Side-by-side comparison of an original limb cross-section (A) with the cross- section after processing by an outer-hull algorithm (B). ............................................24 Figure 11. Gaussian PDF (left) and its first-order derivative (right). These functions serve as the basis for the discrete filter used in computing Gaussian derivatives ......26 Figure 12. Plot of hull circumferences versus cross-section level in arm. Notice the peak around level 50 (thumb), and the trough at about level 100 (wrist) ...........................27 Figure 13. Unprocessed scan with luminance data collected by the Cybenvare WB4 scanner (left). On the right, the same scan is shown after processing, with fudicials labeled as the white spheres. ......................................................................................30 Figure 14. Processed scan demonstrating problems in recognizing fudicials. Note how the nose is interpreted as a fudicial. .................................... .... ...................................3 1 Figure 15. Processed scan showing unrecognized markers. Arrows indicate missed markers on shoulder (top) and thigh (bottom). ..........................................................32 Figure 16. Histogram of the difference between user- and software-determined wrist height (z-axis). ........................................................................................................ .33 Figure 17. Magnified view of a segmented scan with wrist located (white line). ............34 Figure 18. Wrist misidentified at a position superior to the anatomical wrist. ................35 1. INTRODUCTION Most people have been sized for an article of clothing at some point in their lives and are thus familiar with the measurement techniques used by tailors. Similar measurements of the human body are made in anthropometry, but they are performed with much more precision. While the goal of tailoring is to size a person for a garment, anthropometry serves to broaden our knowledge about the human form. One may ask why quantification and identification of human morphology is necessary when these differences are readily visible. As explained by Richtsmeier et al. (1 992), "First, the precision gained through quantification is important. Second, although differences between forms may appear obvious, the significance of the difference cannot be ascertained by the naked eye.. Third, some of our most interesting questions entail comparison of comparisons in the form of ontogenetic and phylogenetic sequences. A comparison of comparisons is not possible without morphometric analysis." Until recently, all measurements of the human body were collected by a human observer in a manual fashion. Advances in laser scanner technology, however, have initiated the development of automated systems which acquire measurement data about the human body directly from surface scans. Such systems incorporate both hardware and software solutions to many of the challenges faced when attempting to quantify the features of an irregular object like the human form. Nevertheless, one of the most complex issues remains to be solved: the automated identification, registration, and measurement of the three dimensional human body scan data collected by these systems. In this thesis, two practical automated methods are presented. The first takes advantage of classical image processing technique to detect and identify externally placed reference markers. The second uses 3-D shape analysis methods to locate the wrist of a human subject in scan data. 1.1. History of Anthropometry In the past, measurements have been gathered using mechanical devices, such as the segmometer devised by Carr et al. (1993) to measure distances on the body. Other methods of anthropometry have been employed with regards to the quantification of body surfaces. First, body surface measurements have been estimated from physical dimensions such as body length and mass. Clearly, there is room for significant error with this approach. In the late 1800's, anthropologists devised instrumentation to "integrate" the surface of the body from directly measured points. Before this method was invented, anthropologists had drawn triangles on the surface of the body and had calculated the surface area of the body as the sum of the areas of the triangles. An even more interesting method employed the use of a removable material which was placed over the surface of the body and then measured. Finally, the skinning of cadavers has also been used to quantify body surface area (Brozek et al. 1987). The major drawback
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