Results 31 to 40 of about 5,853,511 (292)
Remote Sensing, 2020 Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s ...
Chu He +3 more
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Face manifold: manifold learning for synthetic face generation
Multimedia Tools and Applications, 2023 Face is one of the most important things for communication with the world around us. It also forms our identity and expressions. Estimating the face structure is a fundamental task in computer vision with applications in different areas such as face recognition and medical surgeries.
Kimia Dinashi +2 more
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Learning deformable shape manifolds [PDF]
Pattern Recognition, 2012 We propose an approach to shape detection of highly deformable shapes in images via manifold learning with regression. Our method does not require shape key points be defined at high contrast image regions, nor do we need an initial estimate of the shape. We only require sufficient representative training data and a rough initial estimate of the object
Samuel, Rivera, Aleix, Martinez
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Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning
IEEE Access, 2017 Fault feature can be extracted by traditional manifold learning algorithms, which construct neighborhood graphs by Euclidean distance (ED). It is difficult to get an excellent dimensionality reduction result when processed data has strong correlations ...
Beibei Yao +3 more
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An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals
IEEE Access, 2020 Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation.
Lorenzo Manoni +2 more
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Sensors, 2022 Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible.
Muhammad Zafran Muhammad Zaly Shah +4 more
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Perturbing low dimensional activity manifolds in spiking neuronal networks.
PLoS Computational Biology, 2019 Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative ...
Emil Wärnberg, Arvind Kumar
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Multilabel Learning with Incomplete Using Dual-Manifold Mapping [PDF]
Jisuanji gongchengIn multilabel learning, the classification performance can be improved through the effective use of label correlations. However, owing to the subjectivity of manual tagging and the similarity of label semantics in practical applications, an incomplete ...
XU Zhilei, HUANG Rui
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Tangent space estimation for smooth embeddings of Riemannian manifolds [PDF]
, 2012 Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learning methods require the estimation of the tangent space of the manifold at
Frossard, Pascal +2 more
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Extendable and invertible manifold learning with geometry regularized autoencoders [PDF]
2020 IEEE International Conference on Big Data (Big Data), 2020 A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data in two or three dimensions.
Andres F. Duque +3 more
semanticscholar +1 more source