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2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12
2019
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Deep Learning Ensemble for Hyperspectral Image Classification. ...
., +, JSTARS Sept. 2019 3295-3306 Deep Learning Ensemble for Hyperspectral Image Classification. ...
doi:10.1109/jstars.2020.2973794
fatcat:sncrozq3fjg4bgjf4lnkslbz3u
Signal Classification under structure sparsity constraints
[article]
2018
arXiv
pre-print
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A key emphasis of this work is to formulate novel optimization problems for learning dictionary and structured sparse representations. ...
This dissertation focuses on the theory and application of sparse signal processing and learning algorithms for image processing and computer vision, especially object classification problems. ...
Numerous sparse coding and dictionary learning algorithms in the manuscript are reproducible via a user-friendly toolbox. ...
arXiv:1812.10859v1
fatcat:hl4stotmojagbmnebld7vwnp2e
A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios
2020
Remote Sensing
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Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. ...
Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. ...
Acknowledgments: Thanks to the guidance of Intelligent Perception and Computing Teaching and Research Office in BUPT, and thanks to the editors for their suggestions for this article. ...
doi:10.3390/rs12152460
fatcat:itc5ixwgrffuzgzyj2yrycaree
Assessing Effectiveness of Exercised Variants of Machine Learning Techniques
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Thus, the machine learning scheme is incorporated with deep learning and artificial intelligence technology. ...
However, there are different schemes (perception based, instance-based and logic based) to provide an effective classification, prediction, and data recognition in terms of characterizing the features ...
for image classification. ...
doi:10.35940/ijitee.d1781.029420
fatcat:3dig3j6ja5hovmazntsm3ldt3m
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
[article]
2014
arXiv
pre-print
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; Sparse machine learning and inference. ...
; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry ...
Relation to Previous Works This result extends the work of [6] for 1 -regularization, [12] for analysis-1 , [1] for non-overlapping group Lasso, [2] for the trace norm, and [13] for general polyhedral ...
arXiv:1410.0719v2
fatcat:4y3drgk3ujh5hopfn2p2runlzu
Image splicing detection with local illumination estimation
2015
2015 IEEE International Conference on Image Processing (ICIP)
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the land of deep unsupervised learning . ...
Sparse
Recovery
Hyperspectral
Imaging
16:00
Break (2000 BCD)
16:30
Detection &
Classification
Indexing &
Retrieval
Biometric
Applications
Optical Flow
& Motion
Estimation
Face ...
Concluding the Show & Tell session will be a section concentrating on why the sensory gap is important to consider for practical applications, such as annotation tools, and image learning and mining systems ...
doi:10.1109/icip.2015.7351341
dblp:conf/icip/FanCF15
fatcat:7ja5gjnp5rafvedc2nman7xcru
Implementation of Fog computing for reliable E-health applications
2015
2015 49th Asilomar Conference on Signals, Systems and Computers
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Specifically, we introduce a concept for sparse joint activity, channel and data detection in the context of the Coded ALOHA (FDMA) protocol. ...
We will mathematically analyze the system accordingly and provide expressions for the capture probabilities of the underlying sparse multiuser detector. ...
MP8a2-4 Dictionary Learning from Quadratic Measurements in Block Sparse Models Piya Pal, University of Maryland, College Park, United States This paper introduces the problem of dictionary learning from ...
doi:10.1109/acssc.2015.7421170
dblp:conf/acssc/CraciunescuMMKP15
fatcat:qm6mki5z6bcvrfimkmqjyrxaxm
Ping-pong beam training for reciprocal channels with delay spread
2015
2015 49th Asilomar Conference on Signals, Systems and Computers
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We propose a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace. ...
We develop an image-domain target detector for forward-looking ground penetrating radar (FLGPR). ...
MP8a2-4 Dictionary Learning from Quadratic Measurements in Block Sparse Models Piya Pal, University of Maryland, College Park, United States This paper introduces the problem of dictionary learning from ...
doi:10.1109/acssc.2015.7421451
dblp:conf/acssc/CarvalhoA15
fatcat:mqokuvnh3zg45licnfbgxyvxfu
Tensor Methods in Computer Vision and Deep Learning
2021
Proceedings of the IEEE
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2021 pre-print arXiv:2107.03436v1 fatcat:s5qce5ksuzcyhm25ang5xlc5l4
With the advent of the deep learning paradigm shift in computer vision, tensors have become even more fundamental. ...
This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer ...
Tensor regression for visual classification Low-rank tensorregression models are well-suited for classification of large-scale visual data, such as face and body images and videos, as well as hyperspectral ...
doi:10.1109/jproc.2021.3074329
fatcat:nlms4qhk2ffepmu4yte3tnzsfa
Design of large polyphase filters in the Quadratic Residue Number System
2010
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
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We apply this model to sample hyperspectral data and show that these techniques learn a dictionary that: 1) contains a meaningful spectral decomposition for hyperspectral imagery, 2) admit representations ...
WA7a-4 9:30 AM A Unified FoCUSS Framework for Learning Sparse Dictionaries and Non-squared Error Brandon Burdge, Kenneth Kreutz-Delgado, Joseph Murray, University of California, San Diego FOCUSS is an ...
doi:10.1109/acssc.2010.5757589
fatcat:ccxnu5owr5fyrcjcqukumerueq
2021 Index IEEE Internet of Things Journal Vol. 8
2021
IEEE Internet of Things Journal
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, JIoT March 1, 2021 3554-3566 Hyperspectral imaging A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System. ...
., +, JIoT Aug. 15, 2021 12847-12854 Image recognition A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System. ...
doi:10.1109/jiot.2022.3141840
fatcat:42a2qzt4jnbwxihxp6rzosha3y
Hyperspectral Image Classification of Satellite Images Using Compressed Neural Networks
2020
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Convolutional neural networks have proven to be a successful instrument for hyperspectral image classification tasks in recent years. ...
These images are useful due to their higher spectral range and precision compared to RGB images, e.g., for satellite imagery. ...
Hyperspectral Classification Just like deep learning methods are popular for image classification tasks, CNNs have been frequently used for HSI classification tasks [13] . ...
doi:10.18452/21355
fatcat:kwxeku4z2recdf7liqhiyxn6hu
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
2018
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learning, • advanced exploration of valuable information in 'big data' by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ...
towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep ...
based recommendations with deep learning image classification. ...
doi:10.22032/dbt.37846
fatcat:abpbnzmatncp7piugi3udxzcxu
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
2019
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learning, • advanced exploration of valuable information in 'big data' by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ...
towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep ...
learning image classification. ...
doi:10.22032/dbt.38375
fatcat:qwx5h42r4zdibdhwjxtpcia6oe
Tensor methods for high-dimensional analysis and visualization
2017
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In this context, tensor decompositions constitute a powerful mathematical framework for compactly representing and operating on both dense and sparse data. ...
In this context, tensor decompositions constitute a promising mathematical framework for compactly representing and operating on both dense and sparse data. ...
., 2005] , [Vasilescu and Terzopoulos, 2007] , deep learning [Novikov et al., 2015] , etc. ...
doi:10.5167/uzh-149680
fatcat:2sv3s2vy4ndsrpznfkfqs3oi4u
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