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A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL) / Anchal Kumawat in The Visual Computer, vol 38 n° 11 (November 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 11 (November 2022) . - pp 3681 - 3702 Titre : A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL) Type de document : Article/Communication Auteurs : Anchal Kumawat, Auteur ; Sucheta Panda, Auteur Année de publication : 2022 Article en page(s) : pp 3681 - 3702 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] base de données d'images [Termes IGN] détection de contours [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtre de Wiener [Termes IGN] Inférence floue [Termes IGN] logique floue [Termes IGN] méthode robuste [Termes IGN] restauration d'image [Termes IGN] seuillage [Termes IGN] superposition d'images Résumé : (auteur) The problem of edge detection plays a crucial role in almost all research areas of image processing. If edges are detected accurately, one can detect the location of objects and the parameters such as shape and area can be measured more precisely. In order to overcome the above problem, a feature-based image registration (FBIR) method in combination with an improved version of canny with fuzzy logic is proposed for accurate detection of edges. The major contributions of the present work are summarized in three steps. In the first step, a restoration-based enhancement algorithm is proposed to get a fine image from a distorted noisy image. In the second step, two versions of input images are registered using a modified FBIR approach. In the third step, to overcome the drawback of canny edge detection algorithm, each step of the algorithm is modified. The output is then fed to a “fuzzy inference system”. The “fuzzy rule-based technique”, when applied to the problem of “edge detection”, is very “efficient” because the thickness of the edges can be controlled by simply changing “rules and output parameters”. The domain of the images under consideration is various well-known image databases such as Berkeley and USC-SIPI databases, whereas the proposed method is also suitable for other types of both indoor and outdoor images. The robustness of the proposed method is analysed, compared and evaluated with seven image assessment quality (IAQ) parameters. The performance of the proposed method is compared with some of the state-of-the-art edge detection methods in terms of the seven IAQ parameters. Numéro de notice : A2022-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02196-1 Date de publication en ligne : 14/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02196-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102041 [article]