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Block classification based edge detector and object localizer

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Abstract

In this paper, we proposed a new edge detector based on a statistical modelization of the image surface. We used two classical and widely used metrics to constitute our detector properties, which are the mean and standard deviation. Our approach was to take advantage from the intensities fluctuation for a better understanding of the image surface. Moreover, our detector is able to highlight pertinent regions in the image. This property has been exploited in the present work, to identify important image contours. Besides its novelty and efficiency, the main advantage of our detector is its simplicity, which makes it easy to implement in terminals with low processing capacity, it’s also little memory consumer and doesn’t need a training phase which makes it independent from the availability of labeled datasets. Experiments shows that our detector out-performs some state-of-the-art detector.

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Authors and Affiliations

  1. Department of electronic, University of Constantine 1, 325 Route de Ain El Bey, Constantine, Algeria

    Manel Benaissa & Abdelhak Bennia

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  1. Manel Benaissa
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  2. Abdelhak Bennia
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Correspondence to Manel Benaissa.

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Benaissa, M., Bennia, A. Block classification based edge detector and object localizer. Multimed Tools Appl 78, 14573–14589 (2019). https://doi.org/10.1007/s11042-018-6821-8

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