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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]

Investigation of recognition and classification of forest fires based on fusion color and textural features of images / Cong Li in Forests, vol 13 n° 10 (October 2022)  

Public [article]  inForests > vol 13 n° 10 (October 2022) . - n° 1719 Titre : Investigation of recognition and classification of forest fires based on fusion color and textural features of images Type de document : Article/Communication Auteurs : Cong Li, Auteur ; Qiang Liu, Auteur ; Binrui Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] base de données d'images [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction de traits caractéristiques [Termes IGN] image RVB [Termes IGN] incendie de forêt [Termes IGN] matrice de co-occurrence [Termes IGN] motif binaire local [Termes IGN] niveau de gris (image) Résumé : (auteur) An image recognition and classification method based on fusion color and textural features was studied. Firstly, the suspected forest fire region was segmented via the fusion RGB-YCbCr color spaces. Then, 10 kinds of textural features were extracted by a local binary pattern (LBP) algorithm and 4 kinds of textural features were extracted by a gray-level co-occurrence matrix (GLCM) algorithm from the suspected fire region. In terms of its application, a database of the forest fire textural feature vector of three scenes was constructed, including forest images without fire, forest images with fire, and forest images with fire-like interference. The existence of forest fires can be recognized based on the database via a support vector machine (SVM). The results showed that the method’s recognition rate for forest fires reached 93.15% and that it had a strong robustness with respect to distinguishing fire-like interference, which provides a more effective scheme for forest fire recognition. Numéro de notice : A2022-834 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13101719 Date de publication en ligne : 18/10/2022 En ligne : https://doi.org/10.3390/f13101719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102030 [article]

Investigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 7 (July 2022) . - 1811 - 1836 Titre : Investigating the role of image retrieval for visual localization Type de document : Article/Communication Auteurs : Martin Humenberger, Auteur ; Yohann Cabon, Auteur ; Noé Pion, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 1811 - 1836 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] base de données d'images [Termes IGN] estimation de pose [Termes IGN] flou [Termes IGN] localisation basée image [Termes IGN] localisation basée vision [Termes IGN] point de repère [Termes IGN] précision de localisation [Termes IGN] Ransac (algorithme) [Termes IGN] réalité de terrain [Termes IGN] structure-from-motion [Termes IGN] vision par ordinateur Résumé : (auteur) Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of “ground truth” for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization. Numéro de notice : A2022-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01615-7 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1007/s11263-022-01615-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101070 [article]

Effective CBIR based on hybrid image features and multilevel approach / D. Latha in Multimedia tools and applications, vol inconnu (March 2022)  

Public [article]  inMultimedia tools and applications > vol inconnu (March 2022) . - pp Titre : Effective CBIR based on hybrid image features and multilevel approach Type de document : Article/Communication Auteurs : D. Latha, Auteur ; A. Geetha, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] base de données d'images [Termes IGN] écart type [Termes IGN] espace colorimétrique [Termes IGN] image en couleur [Termes IGN] image RVB [Termes IGN] matrice de co-occurrence [Termes IGN] motif binaire local [Termes IGN] niveau de gris (image) [Termes IGN] observation multiniveaux [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] saturation de la couleur [Termes IGN] texture d'image [Termes IGN] transformation intensité-teinte-saturation Résumé : (auteur) Content based image retrieval (CBIR) process can retrieve images by matching its feature set values. The proposed novel CBIR methodology called Effective CBIR based on hybrid image features and multilevel approach (CBIR_LTP_GLCM) integrates the hybrid features such as color features and texture features, along with multilevel approach. The color features such as mean and standard deviation are adopted in the proposed method to represent the global color properties of an image. This method manipulates the color input-image by processing the Hue, Saturation and Value channels of the HSV color space. This novel work is enriched with the image feature derived from Local Ternary Pattern (LTP) in addition with GLCM. So, the proposed method CBIR_LTP_GLCM is potentially charged with meaningful modifications travelling with color image manipulation and extended image retrieval accuracy with the aid of multilevel approach. The proposed methodology is experimentally compared with the existing recent CBIR versions by using the standard database such as Corel-1 k, and a user contributed database named DB_VEG. Numéro de notice : A2022-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11042-022-12588-7 Date de publication en ligne : 30/03/2022 En ligne : https://doi.org/10.1007/s11042-022-12588-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100337 [article]

Detection of pictorial map objects with convolutional neural networks / Raimund Schnürer in Cartographic journal (the), vol 58 n° 1 (August 2021)  

Public [article]  inCartographic journal (the) > vol 58 n° 1 (August 2021) . - pp 50 - 68 Titre : Detection of pictorial map objects with convolutional neural networks Type de document : Article/Communication Auteurs : Raimund Schnürer, Auteur ; René Sieber, Auteur ; Jost Schmid-Lanter, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 50 - 68 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] base de données d'images [Termes IGN] bibliothèque numérique [Termes IGN] carte ancienne [Termes IGN] carte numérique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données issues des réseaux sociaux [Termes IGN] objet cartographique [Termes IGN] pictogramme Résumé : (auteur) In this work, realistically drawn objects are identified on digital maps by convolutional neural networks. For the first two experiments, 6200 images were retrieved from Pinterest. While alternating image input options, two binary classifiers based on Xception and InceptionResNetV2 were trained to separate maps and pictorial maps. Results showed that the accuracy is 95–97% to distinguish maps from other images, whereas maps with pictorial objects are correctly classified at rates of 87–92%. For a third experiment, bounding boxes of 3200 sailing ships were annotated in historic maps from different digital libraries. Faster R-CNN and RetinaNet were compared to determine the box coordinates, while adjusting anchor scales and examining configurations for small objects. A resulting average precision of 32% was obtained for Faster R-CNN and of 36% for RetinaNet. Research outcomes are relevant for trawling map images on the Internet and for enhancing the advanced search of digital map catalogues. Numéro de notice : A2021-651 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2020.1738112 Date de publication en ligne : 11/09/2020 En ligne : https://doi.org/10.1080/00087041.2020.1738112 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98381 [article]

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