Results 31 to 40 of about 523,433 (260)
Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Sparse representation (SR)-based approaches and collaborative representation (CR)-based methods are proved to be effective to detect the anomalies in a hyperspectral image (HSI).
Sheng Lin +5 more
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Multiple Sparse Representations Classification
PLOS ONE, 2015 Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels.
Esben, Plenge +4 more
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Robust Sparse Representation for Incomplete and Noisy Data
Information, 2015 Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on ...
Jiarong Shi, Xiuyun Zheng, Wei Yang
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Shock and Vibration, 2020 The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing.
Zhan Xing +3 more
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Spatial relationships over sparse representations [PDF]
2009 24th International Conference Image and Vision Computing New Zealand, 2009 New imaging devices provide image data at very high spatial resolution acquisition and throughput rate. In satellite or medical two-dimensional images, high-content and large image issues plead for more high semantic level interactions between the computer vision systems and the end-users in order to leverage the cognitive symbiosis between both ...
Lomenie, Nicolas, Racoceanu, Daniel
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Discriminative collaborative representation for multimodal image classification
International Journal of Advanced Robotic Systems, 2017 Sparse representation has been widely researched for image-based classification. However, sparse representation classification directly treats training samples as a dictionary, so it needs a large training set and is time consuming, especially for a ...
Dawei Sun +3 more
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Sparse representation-based synthetic aperture radar imaging [PDF]
, 2011 There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying ...
Cetin, Mujdat +3 more
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Sparse representation of Gravitational Sound [PDF]
Journal of Sound and Vibration, 2018 Gravitational Sound clips produced by the Laser Interferometer Gravitational-Wave Observatory (LIGO) and the Massachusetts Institute of Technology (MIT) are considered within the particular context of data reduction. It is shown that these types of signals can be approximated at high quality using much less elementary components than those required ...
Rebollo Neira, Laura +1 more
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Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle
The Scientific World Journal, 2014 As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently ...
Xiangwei Xing +3 more
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A First Step to Convolutive Sparse Representation
, 2008 In this paper an extension of the sparse decomposition problem is considered and an algorithm for solving it is presented. In this extension, it is known that one of the shifted versions of a signal s (not necessarily the original signal itself) has a ...
Babaie-Zadeh, Massoud +3 more
core +2 more sources