Learnable hyperspectral measures

Galal, Abdulrahman; Hasan, Haswadi; Imam, Ibrahim F.

doi:10.1016/j.eij.2012.04.004

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…No-cosmic-noise groups can undoubtedly act as reference spectra, as pre-and post-cosmic noise indexes are associatively related to the localized cosmic noise indexes to remove the cosmic noise from M LN and M HN groups. 27,28 Since the cosmic noise correction algorithm requires the "optimal PCA threshold," it was estimated via the SAM technique. First, the reference spectra were estimated by vector quantization of M WN (185 975, 1509) via the k-means technique.…”

Section: ■ Hyperspectral Analysis Of Lpp Specimenmentioning

confidence: 99%

“…SAM coefficient "zero" means that the test and reference spectrum are identical, and vice versa. 28 The SAM score is equivalent to the optimal PCA threshold as both use the euclidean distance for vector quantization. Hence, the lowest SAM score becomes the optimal PCA threshold for despiking.…”

Section: ■ Hyperspectral Analysis Of Lpp Specimenmentioning

confidence: 99%

“…Therefore, the no-cosmic-noise group dataset was split into subclusters using k -means. The mean of each subcluster provides a list of all possible reference spectra that will be used to correct the cosmic-noise spectra. , SAM coefficient between reference spectra and cosmic-noise spectrum provides the numerical measure of the dissimilarity metric between them, and this SAM coefficient “dissimilarity metric” is the optimal PCA threshold required to run the modified-PCA despiking algorithm (MDA) to eliminate the cosmic peaks from each cosmic-noise spectrum (see Figure S2). , …”

Section: Cosmic Noise Elimination: “Bottleneck Issue”mentioning

confidence: 99%

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Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics

et al. 2021

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Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexities involved in the end-to-end Raman analytics pipeline: baseline removal, cosmic noise elimination, and extraction of trusted spectral signatures and abundance maps. Elimination of cosmic noise is the bottleneck in the entire Raman analytics pipeline. Unless this issue is addressed, the realization of autonomous Raman analytics is impractical. Here, we present a learner-predictor strategy-based “automated hyperspectral Raman analysis framework” to rapidly fingerprint the molecular variations in the hyperspectral 2D/3D Raman dataset. We introduce the spectrum angle mapper (SAM) technique to eradicate the cosmic noise from the hyperspectral Raman dataset. The learner-predictor strategy eludes the necessity of human inference, and analytics can be done in autonomous mode. The learner owns the ability to learn; it automatically eliminates the baseline and cosmic noise from the Raman dataset, extracts the predominant spectral signatures, and renders the respective abundance maps. In a nutshell, the learner precisely learned the spectral features space during the hyperspectral Raman analysis. Afterward, the learned spectral features space was translated into a neural network (LNN) model. In the predictor, machine-learned intelligence (LNN) is utilized to predict the alternate batch specimen’s abundance maps in real time. The qualitative/quantitative evaluation of abundance maps implicitly lays the foundation for monitoring the offline/inline industrial qualitative/quantitative quality control (QA/QC) process. The present strategy is best suited for 2D/3D/four-dimensional (4D) hyperspectral Raman spectroscopic techniques. The proposed ML framework is intuitive because it obviates human intelligence, sophisticated computational hardware, and solely a personal computer is enough for the end-to-end pipeline.

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Section: ■ Hyperspectral Analysis Of Lpp Specimenmentioning

confidence: 99%

Section: ■ Hyperspectral Analysis Of Lpp Specimenmentioning

confidence: 99%

Section: Cosmic Noise Elimination: “Bottleneck Issue”mentioning

confidence: 99%

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Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics

et al. 2021

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“…with r as the bandwidth parameter and SAM as the Spectral angle mapper, one spectral similarity measure explored by Galal, Hasan & Imam (2012).…”

Section: Indirect Recovery Of the Shading Factormentioning

confidence: 99%

Intrinsic RGB and multispectral images recovery by independent quadratic programming

Krebs¹,

Benezeth²,

Marzani³

2020

PeerJ Computer Science

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This work introduces a method to estimate reflectance, shading, and specularity from a single image. Reflectance, shading, and specularity are intrinsic images derived from the dichromatic model. Estimation of these intrinsic images has many applications in computer vision such as shape recovery, specularity removal, segmentation, or classification. The proposed method allows for recovering the dichromatic model parameters thanks to two independent quadratic programming steps. Compared to the state of the art in this domain, our approach has the advantage to address a complex inverse problem into two parallelizable optimization steps that are easy to solve and do not require learning. The proposed method is an extension of a previous algorithm that is rewritten to be numerically more stable, has better quantitative and qualitative results, and applies to multispectral images. The proposed method is assessed qualitatively and quantitatively on standard RGB and multispectral datasets.

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“…with r as the bandwidth parameter and SAM as the Spectral Angle Mapper, one spectral similarity measure explored by Galal et al (2012).…”

Section: Indirect Recovery Of the Shading Factormentioning

confidence: 99%

Peer Review #1 of "Intrinsic RGB and multispectral images recovery by independent quadratic programming (v0.1)"

2020

View full text Add to dashboard Cite

This work introduces a method to estimate reflectance, shading, and specularity from a single image. Reflectance, shading, and specularity are intrinsic images derived from the dichromatic model. Estimation of these intrinsic images has many applications in computer vision such as shape recovery, specularity removal, segmentation or classification. The proposed method allows for recovering the dichromatic model parameters thanks to two independent quadratic programming steps. Compared to the state of the art in this domain, our approach has the advantage to address a complex inverse problem into two parallelizable optimization steps that are easy to solve and do not require learning. The proposed method is an extension of a previous algorithm that is rewritten to be numerically more stable, has better quantitative and qualitative results, and applies to multispectral images. The proposed method is assessed qualitatively and quantitatively on standard RGB and multispectral datasets.

show abstract

Learnable hyperspectral measures

Cited by 6 publications

References 12 publications

Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics

Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics

Intrinsic RGB and multispectral images recovery by independent quadratic programming

Peer Review #1 of "Intrinsic RGB and multispectral images recovery by independent quadratic programming (v0.1)"

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