Literature DB >> 18244823

Comparison of edge detection algorithms using a structure from motion task.

M C Shin1, D B Goldgof, K W Bowyer, S Nikiforou.   

Abstract

This paper presents an evaluation of edge detector performance. We use the task of structure from motion (SFM) as a "black box" through which to evaluate the performance of edge detection algorithms. Edge detector goodness is measured by how accurately the SFM could recover the known structure and motion from the edge detection of the image sequences. We use a variety of real image sequences with ground truth to evaluate eight different edge detectors from the literature. Our results suggest that ratings of edge detector performance based on pixel-level metrics and on the SFM are well correlated and that detectors such as the Canny detector and Heitger detector offer the best performance.

Year:  2001        PMID: 18244823     DOI: 10.1109/3477.938262

Source DB:  PubMed          Journal:  IEEE Trans Syst Man Cybern B Cybern        ISSN: 1083-4419


  1 in total

1.  ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation.

Authors:  Yeganeh Jalali; Mansoor Fateh; Mohsen Rezvani; Vahid Abolghasemi; Mohammad Hossein Anisi
Journal:  Sensors (Basel)       Date:  2021-01-03       Impact factor: 3.576

  1 in total

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