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Saliency-guided single shot multibox detector for target detection in SAR images / Lan Du in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3366 - 3376 Titre : Saliency-guided single shot multibox detector for target detection in SAR images Type de document : Article/Communication Auteurs : Lan Du, Auteur ; Lu Li, Auteur ; Di Wei, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3366 - 3376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] saillance Résumé : (auteur) The single shot multibox detector (SSD), a proposal-free method based on convolutional neural network (CNN), has recently been proposed for target detection and has found applications in synthetic aperture radar (SAR) images. Moreover, the saliency information reflected in the saliency map can highlight the target of interest while suppressing clutter, which is beneficial for better scene understanding. Therefore, in this article, we propose a saliency-guided SSD (S-SSD) for target detection in SAR images, in which we effectively integrate the saliency into the SSD network not only to suggest where to focus on but also to improve the representation capability in complex scenes. The proposed S-SSD contains two separated convolutional backbone subnetwork architectures, one with the original SAR image as input to extract features, and the other with the corresponding saliency map obtained from the modified Itti’s method as input to acquire refined saliency information under supervision. In addition, the dense connection structure, instead of the plain structure used in original SSD, is applied in the two convolutional backbone architectures to utilize multiscale information with fewer parameters. Then, for integrating saliency information to guide the network to emphasize informative regions, multilevel fusion modules are utilized to merge the two streams into a unified framework, thereby making the whole network end-to-end jointly trained. Finally, the convolutional predictors are used to predict targets. The experimental results on the miniSAR real data demonstrate that the proposed S-SSD can achieve better detection performance than state-of-the-art methods. Numéro de notice : A2020-237 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2953936 date de publication en ligne : 11/12/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2953936 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94983 [article]

Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding / Weihua Dong in Cartography and Geographic Information Science, vol 47 n° 3 (March 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 3 (March 2020) . - pp 229 - 243 Titre : Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Tong Qin, Auteur ; Hua Liao, Auteur Année de publication : 2020 Article en page(s) : pp 229 - 243 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] interprétation (psychologie) [Termes descripteurs IGN] oculométrie [Termes descripteurs IGN] point de repère [Termes descripteurs IGN] questionnaire [Termes descripteurs IGN] saillance [Termes descripteurs IGN] scène intérieure [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] test statistique [Termes descripteurs IGN] vision [Termes descripteurs IGN] vision par ordinateur [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Landmark visual salience (characterized by features that contrast with their surroundings and visual peculiarities) and semantic salience (characterized by features with unusual or important meaning and content in the environment) are two important factors that affect an individual’s visual attention during wayfinding. However, empirical evidence regarding which factor dominates visual guidance during indoor wayfinding is rare, especially in real-world environments. In this study, we assumed that semantic salience dominates the guidance of visual attention, which means that semantic salience will correlate with participants’ fixations more significantly than visual salience. Notably, in previous studies, semantic salience was shown to guide visual attention in static images or familiar scenes in a laboratory environment. To validate this assumption, first, we collected the eye movement data of 22 participants as they found their way through a building. We then computed the landmark visual and semantic salience using computer vision models and questionnaires, respectively. Finally, we conducted correlation tests to verify our assumption. The results failed to validate our assumption and show that the role of salience in visual guidance in a real-world wayfinding process is different from the role of salience in perceiving static images or scenes in a laboratory. Visual salience dominates visual attention during indoor wayfinding, but the roles of salience in visual guidance are mixed across different landmark classes and tasks. The results provide new evidence for understanding how pedestrians visually interpret landmark information during real-world indoor wayfinding. Numéro de notice : A2020-169 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1697965 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1697965 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94841 [article]

Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n°12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°12 (December 2019) . - pp 2406 - 2423 Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] point de repère [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] représentation mentale spatiale [Termes descripteurs IGN] saillance [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] ville Résumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559 [article]

Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7365 - 7377 Titre : Saliency-guided deep neural networks for SAR image change detection Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Xiaorui Ma, Auteur ; Xiaojun Zhou, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7365 - 7377 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] saillance [Termes descripteurs IGN] télédétection en hyperfréquence Mots-clés libres : hierarchical fuzzy C-means clustering (HFCM) Résumé : (auteur) Change detection is an important task to identify land-cover changes between the acquisitions at different times. For synthetic aperture radar (SAR) images, inherent speckle noise of the images can lead to false changed points, which affects the change detection performance. Besides, the supervised classifier in change detection framework requires numerous training samples, which are generally obtained by manual labeling. In this paper, a novel unsupervised method named saliency-guided deep neural networks (SGDNNs) is proposed for SAR image change detection. In the proposed method, to weaken the influence of speckle noise, a salient region that probably belongs to the changed object is extracted from the difference image. To obtain pseudotraining samples automatically, hierarchical fuzzy C-means (HFCM) clustering is developed to select samples with higher probabilities to be changed and unchanged. Moreover, to enhance the discrimination of sample features, DNNs based on the nonnegative- and Fisher-constrained autoencoder are applied for final detection. Experimental results on five real SAR data sets demonstrate the effectiveness of the proposed approach. Numéro de notice : A2019-536 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2913095 date de publication en ligne : 19/05/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2913095 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94154 [article]

Image retrieval based on saliency for urban image contents / Kamel Guissous (2017)  

Public Titre : Image retrieval based on saliency for urban image contents Type de document : Article/Communication Auteurs : Kamel Guissous , Auteur ; Valérie Gouet-Brunet , Auteur Congrès : IPTA 2017, 7th International Conference on Image Processing Theory, Tools and Applications (28 novembre - 1er décembre 2017), Commanditaire Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Importance : 6 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] saillance [Termes descripteurs IGN] scène urbaine Résumé : (auteur) With the increase of image datasets size and of descriptors complexity in Content-Based Image Retrieval (CBIR) and Computer Vision, it is essential to find a way to limit the amount of manipulated data, while keeping its quality. Instead of treating the entire image, the selection of regions which hold the essence of information is a relevant option to reach this goal. As the visual saliency aims at highlighting the areas of the image which are the most important for a given task, in this paper we propose to exploit visual saliency maps to prune the most salient image features. A novel visual saliency approach based on the local distribution analysis of the edges orientation, particularly dedicated to structured contents, such as street view images of urban environments, is proposed. It is evaluated for CBIR according to three criteria: quality of retrieval, volume of manipulated features and computation time. The proposal can be exploited into various applications involving large sets of local visual features; here it is experimented within two applications: cross-domain image retrieval and image-based vehicle localisation. Numéro de notice : C2017-040 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IPTA.2017.8310131 date de publication en ligne : 12/03/2018 En ligne : https://doi.org/10.1109/IPTA.2017.8310131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91922

Road extraction based on snakes and sophisticated line extraction / Ivan Laptev (1997)

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