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Application of Multichannel Vision Concepts and Mechanisms in an Artificial Industrial Vision System

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3643))

Abstract

The design of visual processing systems in very demanding industrial environments is a technical field in which bioinspiration has not been explored as a developing tool. The need of extremely quick, accurate and real time responses needed in industrial applications is not usually seen as compatible with the “messy”, “slow” or “inaccurate” methods and algorithms inspired in the information processing mechanisms underlying neural activity in the visual pathway. We are trying, thus, to explore the practical possibilities of interaction among concepts from both worlds: the “real” vision system designed for a real time quality control of a production line, and the “inspiration” taken from multi-channel biological vision. In previous papers [1,2] a biologically plausible parallel system for visual detection of form, movement, shape and size has been developed. The system, working off-line and skipping real time restrictions, was tested for a variety of situations, yielding very good results in estimating the mentioned visual characteristics of moving objects. Furthermore, a second parallel-computing version was designed introducing the concept of parallel channel processing, e.g., the discrimination of different visual characteristics by mean of multiprocessors and multithread computing. The architecture we present here, which includes certain concepts developed in the previously explained results [3,4], is intended to work in the production line of a beverage canning industry where cans with faulty imprinted use date and lot number have to be immediately discharged from the line.

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References

  1. Alemán-Flores, M., Leibovic, K.N., Moreno Díaz Jr., R.: A computacional Model for Visual Size, Location and Movement. LNCS, vol. 1333, pp. 406–419. Springer, Heidelberg (1997)

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  2. Quesada-Arencibia, A., Moreno-Díaz Jr., R., Alemán-Flores, M., Leibovic, K.N.: Two Parallel Channel CAST Vision System for Motion Analysis. LNCS, vol. 2178, pp. 316–327. Springer, Heidelberg (2001)

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  3. Quesada-Arencibia, A.: Un Sistema Bioinspirado de Análisis y Seguimiento Visual de Movimiento. Doctoral Dissertation. PhD Thesys. Universidad de Las Palmas de Gran Canaria (2001)

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  4. Rodríguez Rodríguez, J.C., Quesada-Arencibia, A., Moreno-Díaz Jr., R., Leibovic, K.N.: On parallel channel modeling of retinal processes. In: Moreno-Díaz Jr., R., Pichler, F. (eds.) EUROCAST 2003. LNCS, vol. 2809, pp. 471–481. Springer, Heidelberg (2003)

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  5. Leibovic, K.N.: Science of Vision. Springer, New York (1990)

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© 2005 Springer-Verlag Berlin Heidelberg

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Quesada-Arencibia, A., Rodríguez-Rodríguez, J.C., Moreno-Díaz, R. (2005). Application of Multichannel Vision Concepts and Mechanisms in an Artificial Industrial Vision System. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2005. EUROCAST 2005. Lecture Notes in Computer Science, vol 3643. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556985_63

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  • DOI: https://doi.org/10.1007/11556985_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29002-5

  • Online ISBN: 978-3-540-31829-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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