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A New Algorithm for Local Blur-Scale Computation and Edge Detection

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Advances in Visual Computing (ISVC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

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Abstract

Precise and efficient object boundary detection is the key for successful accomplishment of many imaging applications involving object segmentation or recognition. Blur-scale at a given image location represents the transition-width of the local object interface. Hence, the knowledge of blur-scale is crucial for accurate edge detection and object segmentation. In this paper, we present new theory and algorithms for computing local blur-scales and apply it for scale-based gradient computation and edge detection. The new blur-scale computation method is based on our observation that gradients inside a blur-scale region follow a Gaussian distribution with non-zero mean. New statistical criteria using maximal likelihood functions are established and applied for local blur-scale computation. Gradient vectors over a blur-scale region are summed to enhance gradients at blurred object interfaces while leaving gradients at sharp transitions unaffected. Finally, a blur-scale based non-maxima suppression method is developed for edge detection. The method has been applied to both natural and phantom images. Experimental results show that computed blur-scales capture true blur extents at individual image locations. Also, the new scale-based gradient computation and edge detection algorithms successfully detect gradients and edges, especially at the blurred object interfaces.

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Authors and Affiliations

  1. Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242, USA

    Indranil Guha & Punam K. Saha

Authors
  1. Indranil Guha
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  2. Punam K. Saha
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Corresponding author

Correspondence to Indranil Guha .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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Guha, I., Saha, P.K. (2018). A New Algorithm for Local Blur-Scale Computation and Edge Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_52

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  • DOI: https://doi.org/10.1007/978-3-030-03801-4_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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