Bibliographies thématiques / Visual place recognition

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Littérature scientifique sur le sujet « Visual place recognition »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 28 janvier 2023

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Articles de revues sur le sujet "Visual place recognition"

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Lowry, Stephanie, Niko Sunderhauf, Paul Newman, et al. "Visual Place Recognition: A Survey." IEEE Transactions on Robotics 32, no. 1 (2016): 1–19. http://dx.doi.org/10.1109/tro.2015.2496823.

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Torii, Akihiko, Josef Sivic, Masatoshi Okutomi, and Tomas Pajdla. "Visual Place Recognition with Repetitive Structures." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 11 (2015): 2346–59. http://dx.doi.org/10.1109/tpami.2015.2409868.

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Grill-Spector, Kalanit, and Nancy Kanwisher. "Visual Recognition." Psychological Science 16, no. 2 (2005): 152–60. http://dx.doi.org/10.1111/j.0956-7976.2005.00796.x.

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What is the sequence of processing steps involved in visual object recognition? We varied the exposure duration of natural images and measured subjects' performance on three different tasks, each designed to tap a different candidate component process of object recognition. For each exposure duration, accuracy was lower and reaction time longer on a within-category identification task (e.g., distinguishing pigeons from other birds) than on a perceptual categorization task (e.g., birds vs. cars). However, strikingly, at each exposure duration, subjects performed just as quickly and accurately o
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Zeng, Zhiqiang, Jian Zhang, Xiaodong Wang, Yuming Chen, and Chaoyang Zhu. "Place Recognition: An Overview of Vision Perspective." Applied Sciences 8, no. 11 (2018): 2257. http://dx.doi.org/10.3390/app8112257.

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Place recognition is one of the most fundamental topics in the computer-vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of knowledge accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place-recognition literature. Since condition-invariant and viewpoint-invariant features are essential factors to long-term robust visual place-recognition systems, we start with
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Masone, Carlo, and Barbara Caputo. "A Survey on Deep Visual Place Recognition." IEEE Access 9 (2021): 19516–47. http://dx.doi.org/10.1109/access.2021.3054937.

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Stumm, Elena S., Christopher Mei, and Simon Lacroix. "Building Location Models for Visual Place Recognition." International Journal of Robotics Research 35, no. 4 (2015): 334–56. http://dx.doi.org/10.1177/0278364915570140.

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Wang, Bo, Xin-sheng Wu, An Chen, Chun-yu Chen, and Hai-ming Liu. "The Research Status of Visual Place Recognition." Journal of Physics: Conference Series 1518 (April 2020): 012039. http://dx.doi.org/10.1088/1742-6596/1518/1/012039.

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Horst, Michael, and Ralf Möller. "Visual Place Recognition for Autonomous Mobile Robots." Robotics 6, no. 2 (2017): 9. http://dx.doi.org/10.3390/robotics6020009.

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Oertel, Amadeus, Titus Cieslewski, and Davide Scaramuzza. "Augmenting Visual Place Recognition With Structural Cues." IEEE Robotics and Automation Letters 5, no. 4 (2020): 5534–41. http://dx.doi.org/10.1109/lra.2020.3009077.

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Chen, Baifan, Xiaoting Song, Hongyu Shen, and Tao Lu. "Hierarchical Visual Place Recognition Based on Semantic-Aggregation." Applied Sciences 11, no. 20 (2021): 9540. http://dx.doi.org/10.3390/app11209540.

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A major challenge in place recognition is to be robust against viewpoint changes and appearance changes caused by self and environmental variations. Humans achieve this by recognizing objects and their relationships in the scene under different conditions. Inspired by this, we propose a hierarchical visual place recognition pipeline based on semantic-aggregation and scene understanding for the images. The pipeline contains coarse matching and fine matching. Semantic-aggregation happens in residual aggregation of visual information and semantic information in coarse matching, and semantic assoc
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Thèses sur le sujet "Visual place recognition"

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Stumm, Elena. "Location models for visual place recognition." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30341/document.

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Cette thèse traite de la cartographie et de la reconnaissance de lieux par vision en robotique mobile. Les recherches menées visent à identifier comment les modèles de localisation peuvent être améliorés en enrichissant les représentations existantes afin de mieux exploiter l'information visuelle disponible. Les problèmes de la cartographie et de la reconnaissance visuelle de lieux présentent un certain nombre de défis : les solutions doivent notamment être robustes vis-à-vis des scènes similaires, des changements de points de vue de d'éclairage, de la dynamique de l'environnement, du bruit de
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Vysotska, Olga [Verfasser]. "Visual Place Recognition in Changing Environments / Olga Vysotska." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/119900538X/34.

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Vysotska, Olga [Verfasser]. "Visual Place Recognition in Changing Environments / Olga Vysotska." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/1217404473/34.

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Qiao, Yongliang. "Place recognition based visual localization in changing environments." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCA004/document.

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Dans de nombreuses applications, il est crucial qu'un robot ou un véhicule se localise, notamment pour la navigation ou la conduite autonome. Cette thèse traite de la localisation visuelle par des méthodes de reconnaissance de lieux. Le principe est le suivant: lors d'une phase hors-ligne, des images géo-référencées de l'environnement d'évolution du véhicule sont acquises, des caractéristiques en sont extraites et sauvegardées. Puis lors de la phase en ligne, il s'agit de retrouver l'image (ou la séquence d'images) de la base d'apprentissage qui correspond le mieux à l'image (ou la séquence d'
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Lowry, Stephanie Margaret. "Visual place recognition for persistent robot navigation in changing environments." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/79404/1/Stephanie_Lowry_Thesis.pdf.

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This thesis demonstrates that robots can learn about how the world changes, and can use this information to recognise where they are, even when the appearance of the environment has changed a great deal. The ability to localise in highly dynamic environments using vision only is a key tool for achieving long-term, autonomous navigation in unstructured outdoor environments. The proposed learning algorithms are designed to be unsupervised, and can be generated by the robot online in response to its observations of the world, without requiring information from a human operator or other external s
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Neubert, Peer. "Superpixels and their Application for Visual Place Recognition in Changing Environments." Doctoral thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-190241.

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Superpixels are the results of an image oversegmentation. They are an established intermediate level image representation and used for various applications including object detection, 3d reconstruction and semantic segmentation. While there are various approaches to create such segmentations, there is a lack of knowledge about their properties. In particular, there are contradicting results published in the literature. This thesis identifies segmentation quality, stability, compactness and runtime to be important properties of superpixel segmentation algorithms. While for some of these propert
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Pepperell, Edward. "Visual sequence-based place recognition for changing conditions and varied viewpoints." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93741/1/Edward_Pepperell_Thesis.pdf.

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Correctly identifying previously-visited locations is essential for robotic place recognition and localisation. This thesis presents training-free solutions to vision-based place recognition under changing environmental conditions and camera viewpoints. Using vision as a primary sensor, the proposed approaches combine image segmentation and rescaling techniques over sequences of visual imagery to enable successful place recognition over a range of challenging environments where prior techniques have failed.
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Garg, Sourav. "Robust visual place recognition under simultaneous variations in viewpoint and appearance." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134410/1/Sourav%20Garg%20Thesis.pdf.

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This thesis explores the problem of visual place recognition and localization for a mobile robot, particularly dealing with the challenges of simultaneous variations in scene appearance and camera viewpoint. The proposed methods draw inspiration from humans and make use of semantic cues to represent places. This approach enables effective place recognition from similar or opposing viewpoints, despite variations in scene appearance caused by different times of day or seasons. The research contributions presented in the thesis advance visual place recognition techniques, making them more useful
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Stone, Thomas Jonathan. "Mechanisms of place recognition and path integration based on the insect visual system." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28909.

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Animals are often able to solve complex navigational tasks in very challenging terrain, despite using low resolution sensors and minimal computational power, providing inspiration for robots. In particular, many species of insect are known to solve complex navigation problems, often combining an array of different behaviours (Wehner et al., 1996; Collett, 1996). Their nervous system is also comparatively simple, relative to that of mammals and other vertebrates. In the first part of this thesis, the visual input of a navigating desert ant, Cataglyphis velox, was mimicked by capturing images in
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Hausler, Stephen D. "Appearance and viewpoint invariant visual place recognition using multi-scale and multi-modality systems." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226953/1/Stephen_Hausler_Thesis.pdf.

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This thesis provides solutions to the problem of visual place recognition, that is, the ability for an autonomous system to recognise where it is in the world using images. The thesis shows how novel ensemble and multi-scale methods can be combined with modern artificial intelligence techniques to provide robust and reliable place recognition capabilities. It provides autonomous systems with enhanced positioning capabilities that can facilitate navigation even in challenging environmental conditions, such as at night or during adverse weather.
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Livres sur le sujet "Visual place recognition"

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Solebo, Ameenat Lola. Identification of visual impairments. Edited by Alan Emond. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198788850.003.0021.

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The development of optimal visual function is important for the future quality of life. Early recognition of morphological abnormalities, such as cataracts, allow for early intervention and a reduction in long-term impairment. There is a period of sensitivity, during which it is important that a clear image is presented to the retina. If treatment is not undertaken in a timely fashion, it can lead to permanent amblyopia. Apart from the newborn and 6–8-week examinations, the only recommended routine examination of the eyes should take place at 4–5 years of age. This should only be undertaken by
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Bouzas, Antia Mato, and Lorenzo Casini, eds. Migration in the Making of the Gulf Space: Social, Political, and Cultural Dimensions. Berghahn Books, 2022. http://dx.doi.org/10.3167/9781800733503.

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Combining visual and literary analyses and original ethnographic studies as part of a more general political reflection, Migration in the Making of Gulf Space examines the role of migrants and non-citizens in the processes of settling in the Arab States of the Gulf region. The contributions underscore the aspirational character of the Gulf as a place where migrant recognition can be attained while also reflecting on practices of exclusion. The book is the result of an interdisciplinary dialogue among scholars and includes an original contribution by the acclaimed author of the novel Temporary
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Kleege, Georgina. More than Meets the Eye. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190604356.001.0001.

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More Than Meets the Eye: What Blindness Brings to Art explores the ways blindness and visual art are linked in many facets of the culture. The author writes from her position as the blind daughter of two visual artists. Due to this background, she claims to know something about art, but recognizes that this claim challenges cultural notions that conflate seeing with knowing. The book examines the ways blindness has been represented in philosophy, visual culture, and cognitive science, showing how these traditional understandings of blindness rely on an over-determined, one-to-one correspondenc
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Chapitres de livres sur le sujet "Visual place recognition"

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Qi, Junkun, Rui Wang, Chuan Wang, and Xiaochun Cao. "Coarse-to-Fine Visual Place Recognition." In Neural Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92273-3_3.

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Tsintotas, Konstantinos A., Loukas Bampis, and Antonios Gasteratos. "Dynamic Places’ Definition for Sequence-Based Visual Place Recognition." In Online Appearance-Based Place Recognition and Mapping. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09396-8_4.

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Panphattarasap, Pilailuck, and Andrew Calway. "Visual Place Recognition Using Landmark Distribution Descriptors." In Computer Vision – ACCV 2016. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54190-7_30.

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dos Santos, Filipe Neves, Paulo Cerqueira Costa, and António Paulo Moreira. "Visual Signature for Place Recognition in Indoor Scenarios." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10380-8_62.

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Gong, Mingying, Lifeng Sun, Shiqiang Yang, and Yun Yang. "Robust Place Recognition by Avoiding Confusing Features and Fast Geometric Re-ranking." In Computational Visual Media. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34263-9_27.

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Xu, Zhenyu, Qieshi Zhang, Fusheng Hao, Ziliang Ren, Yuhang Kang, and Jun Cheng. "VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder." In Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88007-1_8.

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Ali, Abbas M., and Tarik A. Rashid. "Kernel Visual Keyword Description for Object and Place Recognition." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28658-7_3.

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Jafar, Fairul Azni, Nurul Azma Zakaria, Ahamad Zaki Mohamed Noor, and Kazutaka Yokota. "Environmental Visual Features Based Place Recognition in Manufacturing Environment." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8954-3_6.

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Du, Dapeng, Na Liu, Xiangyang Xu, and Gangshan Wu. "Don’t Be Confused: Region Mapping Based Visual Place Recognition." In Advances in Multimedia Information Processing – PCM 2017. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77383-4_46.

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Mihankhah, Ehsan, and Danwei Wang. "Avoiding to Face the Challenges of Visual Place Recognition." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01054-6_52.

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Actes de conférences sur le sujet "Visual place recognition"

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Garg, Sourav, Tobias Fischer, and Michael Milford. "Where Is Your Place, Visual Place Recognition?" In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/603.

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Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling robotic platforms and intelligent augmentation platforms such as augmented reality devices to perceive and understand the physical world. In this paper, we observe that there are three "drivers" that impose requirements on spatially intelligent agents and thus VPR systems: 1) the particular agent including its sensors and computational resources, 2) the operating environment o
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Alijani, Farid, Jukka Peltomaki, Jussi Puura, Heikki Huttunen, Joni-Kristian Kamarainen, and Esa Rahtu. "Long-term Visual Place Recognition." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956392.

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Torii, Akihiko, Josef Sivic, Toma Pajdla, and Masatoshi Okutomi. "Visual Place Recognition with Repetitive Structures." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.119.

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Stumm, Elena, Christopher Mei, Simon Lacroix, and Margarita Chli. "Location graphs for visual place recognition." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139964.

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Gehrig, Mathias, Elena Stumm, Timo Hinzmann, and Roland Siegwart. "Visual place recognition with probabilistic voting." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989362.

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Kim, Yong Nyeon, Dong Wook Ko, and Il Hong Suh. "Visual navigation using place recognition with visual line words." In 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2014. http://dx.doi.org/10.1109/urai.2014.7057494.

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Hansen, Peter, and Brett Browning. "Visual place recognition using HMM sequence matching." In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6943207.

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Stumm, Elena, Christopher Mei, Simon Lacroix, Juan Nieto, Marco Hutter, and Roland Siegwart. "Robust Visual Place Recognition with Graph Kernels." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.491.

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Camara, Luis G., and Libor Preucil. "Spatio-Semantic ConvNet-Based Visual Place Recognition." In 2019 European Conference on Mobile Robots (ECMR). IEEE, 2019. http://dx.doi.org/10.1109/ecmr.2019.8870948.

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Hafez, A. H. Abdul, Saed Alqaraleh, and Ammar Tello. "Encoded Deep Features for Visual Place Recognition." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302266.

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