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Found 10 papers, 2 papers with code
Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment
Understanding and identifying musical shape plays an important role in music education and performance assessment.
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies
The objective of the framework is to au-tomatically identify, and crop heat loss sources caused by weak insulation, while also eliminating obstructive objects present in those images.
Nondestructive Quality Control in Powder Metallurgy using Hyperspectral Imaging
Measuring the purity in the metal powder is critical for preserving the quality of additive manufacturing products.
Nondestructive Testing of Composite Fibre Materials with Hyperspectral Imaging : Evaluative Studies in the EU H2020 FibreEUse Project
Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture and chemical composition.
PCA-domain Fused Singular Spectral Analysis for fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral Classification
The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI).
SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning
First, we pre-train a Semantic Rectifying Network (SRN) to rectify semantic space with a semantic loss and a rectifying loss.
Does Normalization Methods Play a Role for Hyperspectral Image Classification?
For Hyperspectral image (HSI) datasets, each class have their salient feature and classifiers classify HSI datasets according to the class's saliency features, however, there will be different salient features when use different normalization method.
Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification
To tackle these two problems, in this paper, we propose a new framework for ELM based spectral-spatial classification of HSI, where probabilistic modelling with sparse representation and weighted composite features (WCF) are employed respectively to derive the op-timized output weights and extract spatial features.
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification
Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values.
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances.
