VeggieVision: a produce recognition system

The authors present an automatic product 1D system ("VeggieVision"), intended to ease the produce checkout process. The system consists of an integrated scale and imaging system with a user-friendly interface. When a produce item is placed on the scale, an image is taken. A variety of features, color, texture (shape, density), are then extracted. These features are compared to stored "signatures" which were obtained by prior system training (either on-line or off-line). Depending on the certainty of the classification, the final decision is made either by the system or by a human from a number of choices selected by the system. Over 95% of the time, the correct produce classification is in the top four choices.

[1]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  K. Laws.  Textured Image Segmentation , 1980 .

[3]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[4]  Luc Van Gool,et al.  Texture analysis Anno 1983 , 1985, Comput. Vis. Graph. Image Process..

[5]  Makoto Miyahara,et al.  Mathematical Transform Of (R, G, B) Color Data To Munsell (H, V, C) Color Data , 1988, Other Conferences.

[6]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[7]  마텐 볼레 루돌프,et al.  Produce recognition system , 1995 .

[8]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .