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Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery
[article]
2021
arXiv
pre-print
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To overcome this problem, we propose a simple real-time unsupervised training augmentation technique, termed randomized histogram matching (RHM). ...
RHM also offers substantially better performance than other comparably simple approaches that are widely-used in overhead imagery. ...
Randomized Histogram Matching (RHM) To overcome this problem we propose a simple augmentation technique, termed randomized histogram matching (RHM), that matches the histogram of each training image to ...
arXiv:2104.14032v2
fatcat:fvw5slajenfwjam5cxij2t4xn4
GisGCN: A Visual Graph-Based Framework to Match Geographical Areas through Time
2022
ISPRS International Journal of Geo-Information
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Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. ...
Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. ...
IOU histograms for matching graph geographic areas obtained for Meurthe-en-Moselle department. If all the nodes attributes match exactly (injective matching) across graphs, the IOU value will be 1. ...
doi:10.3390/ijgi11020097
fatcat:ki42hxfyovd7lkzob7xkduknzy
A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation
2020
Remote Sensing
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To address this problem, we propose an unsupervised domain adaptation (DA) method that does not require labeled training data, and thus can maintain detection performance in the target domain at a low ...
Recently, object detectors based on deep learning have become widely used for vehicle detection and contributed to drastic improvement in performance measures. ...
Acknowledgments: The aerial images used in this paper were provided by NTT Geospace (http://www.nttgeospace.co.jp/).
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs12030575
fatcat:vtommlr5xzdvvfhdc4smyh6hky
Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance
2014
Sensors
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VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection ...
VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video ...
The authors would like to thank Adnan Bubalo (AFRL), Robert Biehl, Brad Galego, Helen Webb and Michael Schneider (BAE Systems) for their support. ...
doi:10.3390/s141019843
pmid:25340453
pmcid:PMC4239870
fatcat:ony3ylej4nhzxbnap2zide3kwi
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
[article]
2022
arXiv
pre-print
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It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. ...
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. ...
texts. perform single-shot domain adaptation by matching the reference image in the target domain with a corresponding synthesized image from the source domain, obtained through latent space optimization ...
arXiv:2202.14020v1
fatcat:qu3plbdnszdujcwxwq3zizysje
Integrating language models into classifiers for BCI communication: a review
2016
Journal of Neural Engineering
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While these methods have been used for years in traditional augmentative and assistive communication (AAC) devices, information about the output domain has largely been ignored in BCI communication systems ...
In the RSVP speller, however, the user focuses on the center of the screen where characters appear in a random sequence. ...
Acknowledgments This work was supported by the National Institute of Biomedical Imaging and Bioengineering Award Number K23EB014326 (NP) and the UCLA Scholars in Translational Medicine Program (NP). ...
doi:10.1088/1741-2560/13/3/031002
pmid:27153565
pmcid:PMC5495144
fatcat:ojnc2ywspzafhdjli3e4xc7gei
Structural Prior Driven Regularized Deep Learning for Sonar Image Classification
[article]
2020
arXiv
pre-print
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Deep learning has been recently shown to improve performance in the domain of synthetic aperture sonar (SAS) image classification. ...
Despite deep learning's recent success, there are still compelling open challenges in reducing the high false alarm rate and enabling success when training imagery is limited, which is a practical challenge ...
ACKNOWLEDGMENTS The authors would like to thank the NATO Centre for Maritime Research & Experimentation (CMRE) for providing the data used in this work. ...
arXiv:2010.13317v1
fatcat:zsaqtwpthbhivpio6jqcg7g5tq
ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
[article]
2019
arXiv
pre-print
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both strong practicality and generalization ability in geometric matching applications. ...
In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local feature descriptors. ...
instead of a random
coordinate pairs) for outlier rejection in image matching. ...
arXiv:1904.04084v1
fatcat:gd2pe42w75bbjazhw33z4pwiwe
Applied Imagery Pattern Recognition 2011
2011
2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
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Again and again, we see that for some problems these computer methods are more sensitive, more perceptive, than even a trained artist or art historian, at least for a handful of problems. ...
For instance, visual psychologists have shown that most of us trained art scholars and artists included-are not particularly good at judging perspective or the location of illumination in a photograph, ...
These augmented random decision trees enable fa st construction of reliable, mission-specific training data. ...
doi:10.1109/aipr.2011.6176381
fatcat:7bbefbxrnnfjvdtun4zixrckjy
A review of EO image information mining
[article]
2012
arXiv
pre-print
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We analyze the state of the art of content-based retrieval in Earth observation image archives focusing on complete systems showing promise for operational implementation. ...
The approaches taken are analyzed, focusing in particular on the phases after primitive feature extraction. ...
representative example for the proposed workflow from remote sensing imagery to GIS. ...
arXiv:1203.0747v2
fatcat:nwiylcsdrnhthi753xcxwxgo7e
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
2017
IEEE Geoscience and Remote Sensing Magazine
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simple to start with. ...
In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? ...
SARptical is a unique data set for SAR and optical image matching in dense urban areas. ...
doi:10.1109/mgrs.2017.2762307
fatcat:ec7b32lpdnhvzbdz2uoayw6anq
Image retrieval
2008
ACM Computing Surveys
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We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real-world. ...
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. ...
As a result, the overhead for a user in specifying what she is looking for at the beginning of a search is much reduced. ...
doi:10.1145/1348246.1348248
fatcat:5jbcrsxkkbac5cya3zb7eb22ea
Unfolding the Restrained Encountered in Hyperspectral Images
2019
International journal of recent technology and engineering
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We reiterate our main focus in this article on providing the various challenges existing relating to HSI and a case study of the current solutions provided for each. ...
For a single scene, the hyperspectral images (HSI) are composed of hundreds of channels of spectral data. ...
Small Number of Labelled Samples For classification purposes, to augment and increase the size of training samples, semi-supervised [24] and domain adaptation/transductive learning [56] , approaches ...
doi:10.35940/ijrte.b1763.078219
fatcat:cgdtvtrzbzaylm4eowtog52x3m
Image Enhancement Driven by Object Characteristics and Dense Feature Reuse Network for Ship Target Detection in Remote Sensing Imagery
2021
Remote Sensing
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As the application scenarios of remote sensing imagery (RSI) become richer, the task of ship detection from an overhead perspective is of great significance. ...
performance for target detection tasks; on the other hand, upsampling or pooling operations result in the loss of detailed information in the features, and the CNN with outstanding results are often accompanied ...
CoGAN [40] is a model that is also suitable for unpaired images, using two shared weight generators to generate images in two domains with random noise. ...
doi:10.3390/rs13071327
fatcat:phazoirsb5cfbajmwnbldx25lu
A Review of Environmental Context Detection for Navigation Based on Multiple Sensors
2020
Sensors
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quality (GNSS in urban canyons for instance or camera-based navigation in a non-textured environment). ...
Thus, it is important firstly to define this concept of context for navigation and to find a way to extract it from available information. ...
Thanks for the feedback of the different reviewers and the help of MDPI concerning the paper formatting.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20164532
pmid:32823560
fatcat:zqewbmjyd5c55mycnj7qg3by6e
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