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International Symposium on Visual Computing

ISVC 2015: Advances in Visual Computing pp 45–53Cite as

A Computer Vision System for Automatic Classification of Most Consumed Brazilian Beans

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

Abstract

In this work we propose a computer vision system (CVS) for automatic classification of beans. It is able to classify the beans most consumed in Brazil, according to their skin colors and is composed by three main steps: (i) image acquisition and pre-processing, (ii) segmentation of grains and (iii) classification of grains. In the conducted experiments, we used an apparatus controlled by a PC that includes a conveyor belt, an image acquisition chamber and a camera, to simulate an industrial line of production. The results obtained in the experiments indicate that proposed system could be used to support the visual quality inspection of Brazilian beans.

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Notes

  1. 1.

    Available at http://www.lps.usp.br/~hae/software/.

  2. 2.

    Available at http://opencv.org/

References

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Acknowledgments

The authors would like to thank UNINOVE and FAPESP São Paulo Research Foundation (Process 2014/09194-5) by financial support.

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Authors and Affiliations

  1. Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil

    S. A. Araújo, W. A. L. Alves, P. A. Belan & K. P. Anselmo

Authors
  1. S. A. Araújo
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  2. W. A. L. Alves
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  3. P. A. Belan
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  4. K. P. Anselmo
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Corresponding author

Correspondence to S. A. Araújo .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Araújo, S.A., Alves, W.A.L., Belan, P.A., Anselmo, K.P. (2015). A Computer Vision System for Automatic Classification of Most Consumed Brazilian Beans. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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