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Appearance-based indoor localization: A comparison of patch descriptor performance

Jose Rivera-Rubio, Ioannis Alexiou, Anil A. Bharath
2015 Pattern Recognition Letters  
We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry.  ...  Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs).  ...  We are also grateful to members of the Imperial College's Biologically Inspired Computer Vision group for their help and collaboration, and to Dr Riccardo Secoli and Dr Luke Dickens for their valuable  ... 
doi:10.1016/j.patrec.2015.03.003 fatcat:3st4sbxafnbs7h6dohpwqvwqnq

Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition

Zhe Wang, Limin Wang, Yali Wang, Bowen Zhang, Yu Qiao
2017 IEEE Transactions on Image Processing  
First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called PatchNet.  ...  Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%)  ...  First, we propose a patchlevel architecture to model the appearance of local patches, called as PatchNet.  ... 
doi:10.1109/tip.2017.2666739 pmid:28207394 fatcat:3igegqypazcdlircqjyt6t7m4i

Localizing Facial Keypoints with Global Descriptor Search, Neighbour Alignment and Locally Linear Models

Md. Kamrul Hasan, Christopher Pal, Sharon Moalem
2013 2013 IEEE International Conference on Computer Vision Workshops  
We present our technique for facial keypoint localization in the wild submitted to the 300-W challenge. Our approach begins with a nearest neighbour search using global descriptors.  ...  We then employ an alignment of local neighbours and dynamically fit a locally linear model to the global keypoint configurations of the returned neighbours.  ...  (HoG) based descriptors.  ... 
doi:10.1109/iccvw.2013.55 dblp:conf/iccvw/HasanPM13 fatcat:4g56ibkklfa5jad7dgl3fo4acq

OTC: A Novel Local Descriptor for Scene Classification [chapter]

Ran Margolin, Lihi Zelnik-Manor, Ayellet Tal
2014 Lecture Notes in Computer Science  
Our descriptor captures the texture of a patch along multiple orientations, while maintaining robustness to illumination changes, geometric distortions and local contrast differences.  ...  In this paper we present a novel local descriptor suited for such a task: Oriented Texture Curves (OTC).  ...  We propose a novel local descriptor: Oriented Texture Curves (OTC). The descriptor is based on three key ideas.  ... 
doi:10.1007/978-3-319-10584-0_25 fatcat:f64qvx66rrc3hg74nqjwtyk4ci

High-Precision Localization Using Ground Texture [article]

Linguang Zhang, Adam Finkelstein, Szymon Rusinkiewicz
2019 arXiv   pre-print
We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside.  ...  Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed  ...  Right: The precise voting leads to a more defined local maximum. Fig. 4 : 4 The average performance of different detector + descriptor combinations on both indoor and outdoor datasets.  ... 
arXiv:1710.10687v3 fatcat:wkghxndtenhrzlped5zmd7e2p4

LF-Net: Learning Local Features from Images [article]

Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi
2018 arXiv   pre-print
We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision.  ...  To do so we exploit depth and relative camera pose cues to create a virtual target that the network should achieve on one image, provided the outputs of the network for the other image.  ...  Acknowledgments This work was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant "Deep Visual Geometry Machines" (RGPIN-2018-03788), and by systems  ... 
arXiv:1805.09662v2 fatcat:ba2d64my3vdqthsfqdhe6aor6q

Local Color Contrastive Descriptor for Image Classification [article]

Sheng Guo and Weilin Huang and Yu Qiao
2015 arXiv   pre-print
In this paper, we present a simple yet efficient local descriptor for image classification, referred as Local Color Contrastive Descriptor (LCCD), by leveraging the neural mechanisms of color contrast.  ...  Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combinations, and achieves the state-of-the-art performance on three challenging  ...  A. On the MIT Indoor-67 Database We evaluate the performance of the LCCD on the task of indoor scene recognition.  ... 
arXiv:1508.00307v1 fatcat:fkybrhzfyfeldlrs3c5on425ry

Feature description using local neighborhoods

Man Hee Lee, Minsu Cho, In Kyu Park
2015 Pattern Recognition Letters  
To remedy the problem, we propose a novel feature description and similarity measure based on local neighborhoods.  ...  The proposed descriptor and similarity is useful for a wide range of matching methods including nearest neighbor matching methods and popular graph matching algorithms.  ...  It means that an appearance-based matching technique is unlikely to find correct correspondences based on the patch-based descriptors.  ... 
doi:10.1016/j.patrec.2015.08.016 fatcat:utofonxgxzhelkhtoujtyfkogu

Deep Dense Local Feature Matching and Vehicle Removal for Indoor Visual Localization [article]

Kyung Ho Park
2022 arXiv   pre-print
In this paper, we propose a visual localization framework that robustly finds the match for a query among the images collected from indoor parking lots.  ...  It is a challenging problem when the vehicles in the images share similar appearances and are frequently replaced such as parking lots.  ...  We highlight that this manuscript aims to share an empirical cutting-edge use case of visual localization in indoor environments rather than suggesting academic contribution.  ... 
arXiv:2205.12544v1 fatcat:zptg3idxwrcmvhcyn45qsqkv4a

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis [article]

Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, Akihiko Torii
2018 arXiv   pre-print
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold.  ...  First, we develop a new large-scale visual localization method targeted for indoor environments.  ...  At the same time, binarization reduces the memory requirements by a factor of 32, compressing 428GB of original descriptors to just 13.4GB. Comparison with learning based localization methods.  ... 
arXiv:1803.10368v2 fatcat:rwbxbgtwcnfz7hqmk3xur5mmoi

PATTERN RECOGNITION WITH LOCAL INVARIANT FEATURES [chapter]

C. Schmid, G. Dorkó, S. Lazebnik, K. Mikolajczyk, J. Ponce
2005 Handbook of Pattern Recognition and Computer Vision  
It is then demonstrated that combining local features with pattern classification techniques allows for texture and category-level object recognition in the presence of varying viewpoints and background  ...  It is explained how to extract scale and affine-invariant regions and how to obtain discriminant descriptors for these regions.  ...  Schaffalitzky and A. Zisserman for providing the code for their detectors and descriptors. Bike and people images have been provided by A.  ... 
doi:10.1142/9789812775320_0005 fatcat:b2is7srf6nc5paquutc7gugwyq

Features for Ground Texture Based Localization – A Survey [article]

Jan Fabian Schmid, Stephan F. Simon, Rudolf Mester
2020 arXiv   pre-print
Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization.  ...  We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental  ...  Ground texture based localization can be performed with appearance-based approaches [5, 15, 27, 38] , e.g. using normalized cross-correlation to find reoccurring image patches, and with feature-based  ... 
arXiv:2002.11948v2 fatcat:7fwoqfboongwlcrhlt2tmrhevu

RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching [article]

Udit Singh Parihar, Aniket Gujarathi, Kinal Mehta, Satyajit Tourani, Sourav Garg, Michael Milford, K. Madhava Krishna
2022 arXiv   pre-print
The use of local detectors and descriptors in typical computer vision pipelines work well until variations in viewpoint and appearance change become extreme.  ...  We propose rotation-robust local descriptors, learnt through training data augmentation based on rotation homographies, and a correspondence ensemble technique that combines vanilla feature correspondences  ...  Traditional local descriptors [7] , [8] typically employ a detector-descriptor pipeline; the detector comprises sparse or dense keypoints, while the descriptor characterizes a patch around the detector  ... 
arXiv:2103.08573v4 fatcat:d4xfnslvxvayvhr4wux2m4vx6e

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, Akihiko Torii
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Thus, purely RGB-based localization approaches are also relevant in indoor scenes. Obviously, indoor scenes are GPSdenied environments.  ...  We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold.  ...  At the same time, binarization reduces the memory requirements by a factor of 32, compressing 428GB of original descriptors to just 13.4GB. Comparison with learning based localization methods.  ... 
doi:10.1109/cvpr.2018.00752 dblp:conf/cvpr/TairaOSCPSPT18 fatcat:2jmh7a5izvghro3ve62heyn32q

Image-based localization using LSTMs for structured feature correlation [article]

Florian Walch, Caner Hazirbas, Laura Leal-Taixé, Torsten Sattler, Sebastian Hilsenbeck, Daniel Cremers
2017 arXiv   pre-print
We provide extensive quantitative comparison of CNN-based and SIFT-based localization methods, showing the weaknesses and strengths of each.  ...  We make use of LSTM units on the CNN output, which play the role of a structured dimensionality reduction on the feature vector, leading to drastic improvements in localization performance.  ...  This work was partially funded by the ERC Consolidator grant 3D Reloaded and a Sofja Kovalevskaja Award from the Alexander von Humboldt Foundation, endowed by the Federal Ministry of Education and Research  ... 
arXiv:1611.07890v4 fatcat:p5nnydiuzfhonjzjlp5fmmsp5e
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