Contextual and non-contextual performance evaluation of edge detectors T.B. Nguyen, D. Ziou * Dept. de Math. & d'Informatique, Faculte de Sciences, Universit e de Sherbrooke, Sherbrooke, Que., Canada J1K 2RI Received 23 April 1999; received in revised form 29 May 2000 Abstract This paper presents two new evaluation methods for edge detectors. First is non-contextual and concerns the evaluation of edge detector performance in terms of detection errors. The second contextual method evaluates the performance of edge detectors in the context of image reconstruction. Both methods study the in¯uence of image characteristics and edge detector properties on detector performance. Five detectors are evaluated and the performance is compared. Ó 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Edge detection; Performance evaluation; Detector properties; Image characteristics 1. Introduction Several edge detectors have been proposed, with dierent goals and mathematical and algorithmic properties (Ziou and Tabbone, 1998). Conse- quently, one problem encountered by vision sys- tems developers is the selection of an edge detector to be used in a given application. This selection is primarily based on the de®nition of the in¯uence of image characteristics and the properties of the detectors on their performance, a process we call edge detector performance evaluation (Ziou and Koukam, 1998). Several performance evaluation methods have already been proposed. Certain authors (Heath et al., 1997; Cho et al., 1997; Bowyer and Phillips, 1998; Dougherty et al., 1998) group the existing methods according to the presence or absence of ground truth. Such grouping is based on the complexity of the images (e.g., real images, synthetic images) and the per- formance criteria used. For example, without ground truth, it is dicult to measure the dis- placement of an edge from its true location. Ex- isting methods that rely on ground truth use either synthetic images or simple real images for which it is easy to specify the ground truth. This grouping of existing evaluation methods takes into account neither the subsequent use of edges nor the inter- vention of humans (i.e., subjectivity vs objectivity) during the evaluation process. Thus, we propose to group the existing work in two classes according to whether it considers subsequent use of edges in a given application (contextual and non-contextual) and the type of performance criteria used (sub- jective, objective; with or without ground truth). Both contextual and non-contextual evaluation methods can be either objective or subjective. The www.elsevier.nl/locate/patrec Pattern Recognition Letters 21 (2000) 805±816 * Corresponding author. Tel.: +1-819-821-3031; fax: +1-819- 821-8200. E-mail address: ziou@dmi.usherb.ca (D. Ziou). 0167-8655/00/$ - see front matter Ó 2000 Published by Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 6 5 5 ( 0 0 ) 0 0 0 4 5 - 3