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Multi-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 3 (March 2021) . - pp 529 - 540 Titre : Multi-level progressive parallel attention guided salient object detection for RGB-D images Type de document : Article/Communication Auteurs : Zhengyi Liu, Auteur ; Quntao Duan, Auteur ; Song Shi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 529 - 540 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] approche hiérarchique [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] image RVB [Termes IGN] optimisation spatiale [Termes IGN] profondeur [Termes IGN] réseau neuronal récurrent [Termes IGN] saillance Résumé : (auteur) Detecting salient objects in RGB-D images attracts more and more attention in recent years. It benefits from the widespread use of depth sensors and can be applied in the comprehensive understanding of RGB-D images. Existing models focus on double-stream networks which transfer from color stream to depth stream, but depth stream with one channel information cannot learn the same feature as color stream with three channels information even if HHA representation is adopted. In our works, RGB-D four-channels input is chosen, and meanwhile, progressive parallel spatial and channel attention mechanisms are performed to improve feature representation. Spatial and channel attention can pay more attention on partial positions and channels in the image which show higher response to salient objects. Both attentive features are optimized by attentive feature from higher layer, respectively, and parallel fed into recurrent convolutional layer to generate side-output saliency maps guided by saliency map from higher layer. Last multi-level saliency maps are fused together from multi-scale perspective. Experiments on benchmark datasets demonstrate that parallel attention mechanism and progressive optimization operation play an important role in improving the accuracy of salient object detection, and our model outperforms state-of-the-art models in evaluation matrices. Numéro de notice : A2021-340 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01821-9 date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01821-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97578 [article]

Visualization of 3D property data and assessment of the impact of rendering attributes / Stefan Seipel in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 4 n° 2 (December 2020) . - n° 23 Titre : Visualization of 3D property data and assessment of the impact of rendering attributes Type de document : Article/Communication Auteurs : Stefan Seipel, Auteur ; Martin Andrée, Auteur ; Karolina Larsson, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] attribut non spatial [Termes IGN] cadastre 3D [Termes IGN] cadastre étranger [Termes IGN] classification barycentrique [Termes IGN] couleur (rédaction cartographique) [Termes IGN] mesure de similitude [Termes IGN] propriété foncière [Termes IGN] rédaction cartographique [Termes IGN] rendu (géovisualisation) [Termes IGN] saillance [Termes IGN] scène 3D [Termes IGN] Stockholm (Suède) [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Visualizations of 3D cadastral information incorporating both intrinsically spatial and non-spatial information are examined here. The design of a visualization prototype is linked to real-case 3D property information. In an interview with domain experts, the functional and visual features of the prototype are assessed. The choice of rendering attributes was identified as an important aspect for further analysis. A computational approach to systematic assessment of the consequences of different graphical design choices is proposed. This approach incorporates a colour similarity metric, visual saliency maps, and k-nearest-neighbour (kNN) classification to estimate risks of confusing or overlooking relevant elements in a visualization. The results indicate that transparency is not an independent visual variable, as it affects the apparent colour of 3D objects and makes them inherently more difficult to distinguish. Transparency also influences visual saliency of objects in a scene. The proposed analytic approach was useful for visualization design and revealed that the conscious use of graphical attributes, like combinations of colour, transparency, and line styles, can improve saliency of objects in a 3D scene. Numéro de notice : A2020-796 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-020-00063-6 date de publication en ligne : 26/10/2020 En ligne : https://doi.org/10.1007/s41651-020-00063-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96612 [article]

A novel deep network and aggregation model for saliency detection / Ye Liang in The Visual Computer, vol 36 n° 9 (September 2020)  

Public [article]  inThe Visual Computer > vol 36 n° 9 (September 2020) . - pp 1883 - 1895 Titre : A novel deep network and aggregation model for saliency detection Type de document : Article/Communication Auteurs : Ye Liang, Auteur ; Hongzhe Liu, Auteur ; Nan Ma, Auteur Année de publication : 2020 Article en page(s) : pp 1883 - 1895 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] architecture de réseau [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déconvolution [Termes IGN] extraction de traits caractéristiques [Termes IGN] saillance Résumé : (auteur) Recent deep learning-based methods for saliency detection have proved the effectiveness of integrating features with different scales. They usually design various complex architectures of network, e.g., multiple networks, to explore the multi-scale information of images, which is expensive in computation and memory. Feature maps produced with different subsampling convolutional layers have different spatial resolutions; therefore, they can be used as the multi-scale features to reduce the costs. In this paper, by exploiting the in-network feature hierarchy of convolutional networks, we propose a novel multi-scale network for saliency detection (MSNSD) consisting of three modules, i.e., bottom-up feature extraction, top-down feature connection and multi-scale saliency prediction. Moreover, to further boost the performance of MSNSD, an input image-aware saliency aggregation method is proposed based on the ridge regression, which combines MSNSD with some well-performed handcrafted shallow models. Extensive experiments on several benchmarks show that the proposed MSNSD outperforms the state-of-the-art saliency methods with less computational and memory complexity. Meanwhile, our aggregation method for saliency detection is effective and efficient to combine deep and shallow models and make them complementary to each other. Numéro de notice : A2020-601 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-019-01781-9 date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.1007/s00371-019-01781-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95952 [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 (May 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 3 (May 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 IGN] analyse visuelle [Termes IGN] interprétation (psychologie) [Termes IGN] oculométrie [Termes IGN] point de repère [Termes IGN] questionnaire [Termes IGN] saillance [Termes IGN] scène intérieure [Termes IGN] segmentation sémantique [Termes IGN] test statistique [Termes IGN] vision [Termes 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 Affiliation des auteurs : non IGN 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]

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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 IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de cible [Termes IGN] fusion de données [Termes IGN] image radar moirée [Termes 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 Affiliation des auteurs : non IGN 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]

Saliency and Burstiness for Feature Selection in CBIR / Kamel Guissous (2020)  

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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)  

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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)  

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Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs / Abraham Montoya Obeso (2018)  

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

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

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