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Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model
January 1989 (vol. 11 no. 1)
pp. 84-95

The edge-detection problem is posed as one of detecting step discontinuities in the observed correlated image, using directional derivatives estimated with a random field model. Specifically, the method consists of representing the pixels in a local window by a 2-D causal autoregressive (AR) model, whose parameters are adaptively estimated using a recursive least-squares algorithm. The directional derivatives are functions of parameter estimates. An edge is detected if the second derivative in the direction of the estimated maximum gradient is negatively sloped and the first directional derivative and a local estimate of variance satisfy some conditions. Because the ordered edge detector may not detect edges of all orientations well, the image scanned in four different directions, and the union of the four edge images is taken as the final output. The performance of the edge detector is illustrated using synthetic and real images. Comparisons to other edge detectors are given. A linear feature extractor that operates on the edges produced by the AR model is presented.

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Index Terms:
2D random field model; edge detection; 2D causal autoregressive model; parameter estimation; picture processing; pattern recognition; linear feature extraction; recursive least-squares algorithm; directional derivatives; edge detector; parameter estimation; pattern recognition; picture processing; statistical analysis
Citation:
Y.T. Zhou, V. Venkateswar, R. Chellappa, "Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 1, pp. 84-95, Jan. 1989, doi:10.1109/34.23115
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