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Visual Saccades for Object Recognition

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Artificial Intelligence and Soft Computing (ICAISC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

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Abstract

This paper describes a method for rapid location and characterization of objects in 2D images. It derives optimizing parameters of a normalized Gaussian that best approximates the observed object, simultaneously finding the object location in the observed scene. A similarity measure to this optimized Gaussian is used to characterize the object. Optimization process has global and exponentially fast convergence, thus it can be used to implement saccadic motion for object recognition and scene analysis. This method was inspired by Perlovsky’s work on neural dynamic logic used for fast location, characterization, and identification of objects. Developed method was tested and illustrated with an example of an object location and characterization.

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Author information

Authors and Affiliations

  1. Ohio University, Athens, OH, USA

    Janusz A. Starzyk

  2. University of Information Technology and Management, Rzeszow, Poland

    Janusz A. Starzyk

Authors
  1. Janusz A. Starzyk
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Corresponding author

Correspondence to Janusz A. Starzyk .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafal Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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© 2015 Springer International Publishing Switzerland

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Starzyk, J.A. (2015). Visual Saccades for Object Recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_70

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_70

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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