Multi-scale edge detection with local noise estimate

Bo Jiang; Zia-ur Rahman

doi:10.1117/12.859931

20 August 2010 Multi-scale edge detection with local noise estimate

Bo Jiang, Zia-ur Rahman

Proceedings Volume 7798, Applications of Digital Image Processing XXXIII; 779805 (2010) https://doi.org/10.1117/12.859931
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

The (unrealistic) assumption that noise can be modeled as independent, additive and uniform can lead to problems when edge detection methods are applied to real or natural images. The main reason for this is because filter scale and threshold for the gradient are difficult to determine at a regional or local scale when the noise estimate is on a global scale. A filter with one global scale might under-smooth areas of high noise, but over-smooth less noisy area. Similarly, a static, global threshold may not be appropriate for the entire image because different regions have different degrees of detail. Thus, some methods use more than one filter for detecting edges and discard the thresholding method in edge discrimination. Multi-scale description of the image mimics the receptive fields of neurons in the early visual cortex of animals. At the small scale, details can be reliably detected. At the larger scale, the contours or the frame get more attention. So, the image features can be fully represented by combining a range of scales. The proposed multi-scale edge detection algorithm utilizes this hierarchical organization to detect and localize edges. Furthermore, instead of using one default global threshold, local dynamic threshold is introduced to discriminate edge or non-edge. Based on a critical value function, the local dynamic threshold for each scale is determined using a novel local noise estimation (LNE) method. Additionally, the proposed algorithm performs connectivity analysis on edge map to ensure that small, disconnected edges are removed. Experiments where this method is applied to a sequence of images of the same scene but with different signal-noise-ratio (SNR), show the method to be robust to noise.

Citation Download Citation

Bo Jiang and Zia-ur Rahman "Multi-scale edge detection with local noise estimate", Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 779805 (20 August 2010); https://doi.org/10.1117/12.859931

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