Results 201 to 210 of about 9,721 (245)

Physiological sensing for situational awareness: a theory-driven integrative review of multimodal and unsupervised approaches for visual search and human-autonomy teaming. [PDF]

Front Neuroergon
Sekkati H, Lam T, Lapointe JF, Belliveau L, Im S.   +4 more
europepmc   +1 more source

Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging. [PDF]

Imaging Neurosci (Camb)
Codina T, Blankertz B, von Lühmann A.
europepmc   +1 more source

Quality prediction using multiscale convolutional VAEs for thin plate parts. [PDF]

Sci Rep
Su X, Liu Y, Li J.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Unsupervised Hyperspectral Band Selection by Dominant Set Extraction

IEEE Transactions on Geoscience and Remote Sensing, 2016
Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands.
Jingsheng Lei, Zhongqin Bi
exaly   +2 more sources

Unsupervised Band Selection by Integrating the Overall Accuracy and Redundancy

IEEE Geoscience and Remote Sensing Letters, 2015
Band selection is of great significance to alleviate the curse of dimensionality for hyperspectral (HSI) image application. In this letter, we propose a novel unsupervised band selection method for HSI classification. This method integrates both the overall accuracy and redundancy into the band selection process by formulating an optimization model. In
Chenhong Sui, Yan Tian, Yong Xie
exaly   +2 more sources

Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

IEEE Geoscience and Remote Sensing Letters, 2008
Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded ...
Qian Du
exaly   +2 more sources

Unsupervised Hyperspectral Image Band Selection via Column Subset Selection

IEEE Geoscience and Remote Sensing Letters, 2015
In this letter, we proposed a novel band selection algorithm for hyperspectral images (HSIs) based on column subset selection. The main idea of the proposed algorithm comes from the column subset selection problem in numerical linear algebra. It selects a group of bands, which maximizes the volume of the selected subset of columns.
Chi Wang, Maoguo Gong, Mingyang Zhang
exaly   +2 more sources

Superpixel-Based Unsupervised Band Selection for Classification of Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2018
This paper presents an unsupervised approach to band selection in hyperspectral images that considers both spectral and spatial information in data dimensionality reduction. The approach exploits the concepts of superpixel and chunklets for identifying the spectral channels most suitable to be used in classification for discriminating land-cover ...
Chen Yang, Lorenzo Bruzzone, Haishi Zhao   +2 more
exaly   +3 more sources