Comparison of Edge Detectors: A Methodology and Initial study
Document Type
Article
Publication Date
1-1996
Digital Object Identifier (DOI)
https://doi.org/10.1006/cviu.1997.0587
Abstract
Because of the difficulty of obtaining ground truth for real images, the traditional technique for comparing low-level vision algorithms is to present image results, side by side, and to let the reader subjectively judge the quality. This is not a scientifically satisfactory strategy. However, human rating experiments can be done in a more rigorous manner to provide useful quantitative conclusions. We present a paradigm based on experimental psychology and statistics, in which humans rate the output of low level vision algorithms. We demonstrate the proposed experimental strategy by comparing four well-known edge detectors: Canny, Nalwa–Binford, Sarkar–Boyer, and Sobel. We answer the following questions: Is there a statistically significant difference in edge detector outputs as perceived by humans when considering an object recognition task? Do the edge detection results of an operator vary significantly with the choice of its parameters? For each detector, is it possible to choose a single set of optimal parameters for all the images without significantly affecting the edge output quality? Does an edge detector produce edges of the same quality for all images, or does the edge quality vary with the image?
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Computer Vision and Pattern Recognition, v. 69, issue 1, p. 38-54
Scholar Commons Citation
Heath, Mike; Sarkar, Sudeep; Sanocki, Thomas; and Bowyer, Kevin, "Comparison of Edge Detectors: A Methodology and Initial study" (1996). Psychology Faculty Publications. 501.
https://digitalcommons.usf.edu/psy_facpub/501
