2 results for Al-Sarraf, Ali

  • An Approach for Evaluating Robustness of Edge Operators using Real-World Driving Scenes

    Al-Sarraf, Ali; Vaudrey, Tobi; Klette, Reinhard; Woo, Young Woon (2008)

    Conference poster
    The University of Auckland Library

    Conference Details: 2008 23rd International Conference Image and Vision Computing New Zealand Lincoln University, Christchurch, 26-28 November 2008. http://www.lvl.co.nz/ivcnz2008/ Over the past 20 years there have been many papers that compare and evaluate di erent edge operators. Most of them focus on accuracy and also do comparisons against synthetic data. This paper focuses on real-world driver assistance scenes and does a comparison based on robustness. The three edge operators compared are Sobel, Canny and the under-publicized phase-based Kovesi-Owens operator. The Kovesi- Owens operator has the distinct advantage that it uses one pre-selected set of parameters and can work across almost any type of scene, where as other operators require parameter tuning. The results from our comparison show that the Kovesi-Owens operator is the most robust of the three, and can get decent results, even under weak illumination and varying gradients in the images. Keywords: edge operators, edge robustness evaluation, Kovesi-Owens, phase operators

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  • An Approach for Evaluating Robustness of Edge Operators on Real-World Driving Scenes

    Al-Sarraf, Ali; Vaudrey, Tobi; Klette, Reinhard; Woo, Young Woon (2008)

    Report
    The University of Auckland Library

    You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the original MI_tech website http://www.mi.auckland.ac.nz/index.php?option=com_content&view=article&id=91&Itemid=76 . All other rights are reserved by the author(s). Over the past 20 years there have been many papers that compare and evaluate di erent edge operators. Most of them focus on accuracy and also do comparisons against synthetic data. This paper focuses on real-world driver assistance scenes and does a comparison based on robustness. The three edge operators compared are Sobel, Canny and the under-publicized phase-based Kovesi-Owens operator. The Kovesi- Owens operator has the distinct advantage that it uses one pre-selected set of parameters and can work across almost any type of scene, where as other operators require parameter tuning. The results from our comparison show that the Kovesi-Owens operator is the most robust of the three, and can get decent results, even under weak illumination and varying gradients in the images.

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