Ambiguity distance: an edge evaluation measure using fuzziness of edges
Fuzzy Sets and Systems - Information processing
Segmentation of black and white cartoons
SCCG '03 Proceedings of the 19th spring conference on Computer graphics
Feature Extraction of Edge by Directional Computation of Gray-Scale Variation
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Edge landmarks in monocular SLAM
Image and Vision Computing
Colour image segmentation using homogeneity method and data fusion techniques
EURASIP Journal on Advances in Signal Processing - Image processing and analysis in biomechanics
Gradient histogram: Thresholding in a region of interest for edge detection
Image and Vision Computing
Robust line detection using two-orthogonal direction image scanning
Computer Vision and Image Understanding
Coding Images with Local Features
International Journal of Computer Vision
Real-time visual odometry estimation based on principal direction detection on ceiling vision
International Journal of Automation and Computing
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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? 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?