What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Hyperspectral imaging, Wavelet and Computer vision.
His work carried out in the field of Artificial intelligence brings together such families of science as Quantization and Inverse problem.
His work in Pattern recognition covers topics such as Feature which are related to areas like Reduction.
His Full spectral imaging study, which is part of a larger body of work in Hyperspectral imaging, is frequently linked to Mixing, bridging the gap between disciplines.
His Wavelet study also includes fields such as
- Image texture which connect with Color image,
- Image segmentation which intersects with area such as Grading.
His Computer vision research is multidisciplinary, relying on both Extrapolation and Interpolation.
His most cited work include:
- Hyperspectral Remote Sensing Data Analysis and Future Challenges (952 citations)
- Statistical texture characterization from discrete wavelet representations (539 citations)
- Maximum-likelihood estimation of Rician distribution parameters (327 citations)
What are the main themes of his work throughout his whole career to date?
Paul Scheunders mostly deals with Artificial intelligence, Pattern recognition, Hyperspectral imaging, Computer vision and Wavelet.
His study involves Image processing, Image resolution, Multispectral image, Contextual image classification and Image segmentation, a branch of Artificial intelligence.
His Pattern recognition research includes elements of Image, Image fusion and Noise reduction.
His research integrates issues of Pixel, Data set and Algorithm in his study of Hyperspectral imaging.
His Algorithm research integrates issues from Simplex and Mathematical optimization.
His Wavelet study is mostly concerned with Wavelet transform and Stationary wavelet transform.
He most often published in these fields:
- Artificial intelligence (60.21%)
- Pattern recognition (43.66%)
- Hyperspectral imaging (40.14%)
What were the highlights of his more recent work (between 2014-2021)?
- Hyperspectral imaging (40.14%)
- Artificial intelligence (60.21%)
- Pattern recognition (43.66%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Hyperspectral imaging, Artificial intelligence, Pattern recognition, Endmember and Computer vision.
His Hyperspectral imaging research is included under the broader classification of Remote sensing.
His work deals with themes such as Machine learning and Inverse problem, which intersect with Artificial intelligence.
The concepts of his Pattern recognition study are interwoven with issues in Signal-to-noise ratio, Ground truth and Spectral bands.
His study in Endmember is interdisciplinary in nature, drawing from both Algorithm, Mathematical optimization, Minification and Active appearance model.
His work on Filter and Image as part of general Computer vision study is frequently linked to Block, therefore connecting diverse disciplines of science.
Between 2014 and 2021, his most popular works were:
- A Multilinear Mixing Model for Nonlinear Spectral Unmixing (83 citations)
- Close range hyperspectral imaging of plants: A review (81 citations)
- Noise Reduction in Hyperspectral Imagery: Overview and Application (70 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Paul Scheunders focuses on Hyperspectral imaging, Artificial intelligence, Pattern recognition, Endmember and Remote sensing.
His study looks at the intersection of Hyperspectral imaging and topics like Pixel with Cluster analysis and Projection.
His studies deal with areas such as Computational complexity theory, Inverse problem, Computer vision and Minification as well as Artificial intelligence.
His Pattern recognition research integrates issues from Algorithm design, Distribution, Spectral bands, Noise reduction and Data set.
His research in Endmember focuses on subjects like Bilinear interpolation, which are connected to Mathematical optimization, Free parameter, Divergence and Statistical physics.
Paul Scheunders has included themes like Classifier and Feature extraction in his Remote sensing study.
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