Comparison of parametric and nonparametric edge-detection algorithms

Afshin Amini; Ahmed H. Tewfik

doi:10.1117/12.19531

1 June 1990 Comparison of parametric and nonparametric edge-detection algorithms

Afshin Amini, Ahmed H. Tewfik

Proceedings Volume 1244, Image Processing Algorithms and Techniques; (1990) https://doi.org/10.1117/12.19531
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States

Abstract

Edge detection algorithms play an important role in automated vision analysis. In this sequel we propose a model based edge detection algorithm based on the second directional derjvative of each pixel in the image. The new method models the picture as the output of a two dimensional all pole causal sequence with a quarter plane region and a nonsymmetric half plane region of support. To estimate the parameters of the model we used an overdetermined system of normal equations and utilized the least squares and total least squares approach to solve for the unknown parameters. The estimated parameters were subsequently used in a closed form to approximate the second directional derivative for detecting edges. We compared our method along with Haralick's [5] and Thou et a! [10] with that of Canny's [2]. The first three algorithms are parametric algorithms: they are based on parametrizing the local behavior of the image. By contrast the last algorithm is non-parametric since it does not assume any particular model for the image. We take mto account the quantitative measures introduced in [9] to study the performance of various algorithms for different synthetic images. keywords- QP: quarter plane, NSHP: non symmetric halfplane, LS: least squares, TLS: total least squares

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Afshin Amini and Ahmed H. Tewfik "Comparison of parametric and nonparametric edge-detection algorithms", Proc. SPIE 1244, Image Processing Algorithms and Techniques, (1 June 1990); https://doi.org/10.1117/12.19531

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KEYWORDS

Sensors

Autoregressive models

Detection and tracking algorithms

Edge detection

Image processing

Image segmentation

Data modeling

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