Recording measurements of produce provides important information, such as surface area, volume, density, and weight, for agriculture and food processing applications, calculating water loss, heat transfer, quantity of pesticide applications, and respiration rates for example. A machine vision system that uses a non-destructive method to
measure volume and surface area of objects with irregular shapes is presented in this paper. The system first takes a series of silhouettes of the object from different directions by rotating the object at a fixed angular interval. The boundary points of each image are then extracted to construct a silhouette. A three-dimensional wire-frame model of the object can be reconstructed by integrating silhouettes obtained from different view angles. Surface area and volume can then be measured by means of surface fitting and approximation on the wire-frame model.

 Project Sponsors:

 Virginia Tech

 Collaborators:

 Dr. Joseph Eifert, Virginia Tech
 Dr. Andy Chang, Youngstown State University

 Graduate Students:

 Ben Westover, Pengcheng Zhan, and Xiaoqian Xu

Publications:

1. J.D. Eifert, G.C. Sanglay, D.J. Lee, S.S. Sumner, and M.D. Pierson, "Prediction of Raw Produce Surface Area from Weight Measurement", Journal of Food Engineering, vol. 7/4, p. 552-556, June 2006.

2. D.J. Lee, X. Xu, J.D. Eifert, and P. Zhan, "Area and Volume Measurements of Objects with Irregular Shapes Using Multiple Silhouettes”, Optical Engineering, vol. 45/2, p. 027202-27212, February 2006.

3. D.J. Lee, J.D. Eifert, and B.P. Westover, "Surface Area and Volume Measurement Using Radial Projections", SPIE Vision Geometry XI, vol. 4794, p. 92-100, Seattle, WA, USA, July, 2002. 

4. J.D. Eifert, G.C. Sanglay, and D.J. Lee, "Prediction of Raw Produce Surface Area from Weight Measurement", Annual Meeting of the International Association for Food Protection, San Diego, CA, USA, July, 2002.

(Click image to view paper poster)