Abstract

A new method for evaluating edge detection algorithms is presented and applied to measure the relative performance of algorithms by Canny, Nalwa-Binford, Iverson-Zucker, Bergholm, and Rothwell. The basic measure of performance is a visual rating score which indicates the perceived quality of the edges for identifying an object. The process of evaluating edge detection algorithms with this performance measure requires the collection of a set of gray-scale images, optimizing the input parameters for each algorithm, conducting visual evaluation experiments and applying statistical analysis methods. The novel aspect of this work is the use of a visual task and real images of complex scenes in evaluating edge detectors. The method is appealing because, by definition, the results agree with visual evaluations of the edge images.

Keywords

Artificial intelligenceComputer scienceEdge detectionCanny edge detectorMeasure (data warehouse)Enhanced Data Rates for GSM EvolutionVisual inspectionAlgorithmComputer visionPattern recognition (psychology)Object detectionProcess (computing)Image processingImage (mathematics)Data mining

Affiliated Institutions

Related Publications

Publication Info

Year
1997
Type
article
Volume
19
Issue
12
Pages
1338-1359
Citations
491
Access
Closed

External Links

View on DOI.org

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

491
OpenAlex

Cite This

Michael D. Heath, Sudeep Sarkar, Thomas Sanocki et al. (1997). A robust visual method for assessing the relative performance of edge-detection algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence , 19 (12) , 1338-1359. https://doi.org/10.1109/34.643893

Identifiers

DOI
10.1109/34.643893