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Found 15 papers, 3 papers with code
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging
Extensive empirical studies are conducted in terms of validating the effectiveness of NMF, especially its supervised variants (e. g., discriminative NMF, and supervised and constrained NMF with sparseness), and the comparison with principal component analysis (PCA), i. e., the collaborative representation-based dimensionality reduction technique derived from eigenvectors.
Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes
There is growing concern that the potential of black box AI may exacerbate health-related disparities and biases such as gender and ethnicity in clinical decision-making.
IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer
The classifier module first extracts image features from the input medical images and produces disease-related indicators with their corresponding states.
Proximal nested sampling with data-driven priors for physical scientists
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging.
Semantic Segmentation by Semantic Proportions
Utilising semantic proportions suggested in this work offers a promising direction for future research in the field of semantic segmentation.
GO-LDA: Generalised Optimal Linear Discriminant Analysis
Linear discriminant analysis (LDA) has been a useful tool in pattern recognition and data analysis research and practice.
3D Orientation Field Transform
It, however, has no counterpart in three-dimensional (3D) images due to the extremely complicated orientation in 3D compared to 2D.
A Two-stage Classification Method for High-dimensional Data and Point Clouds
High-dimensional data classification is a fundamental task in machine learning and imaging science.
Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)
From these three-dimensional crowns, we are able to measure individual tree biomass.
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation
The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respectively.
A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery
Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case).
Wavelet-Based Segmentation on the Sphere
Wavelets on the sphere have been developed to solve such problems for data defined on the sphere, which arise in numerous fields such as cosmology and geophysics.
A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)
In this paper, we propose a SLaT (Smoothing, Lifting and Thresholding) method with three stages for multiphase segmentation of color images corrupted by different degradations: noise, information loss, and blur.
Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests
There is much current interest in using multi-sensor airborne remote sensing to monitor the structure and biodiversity of forests.
Variational Image Segmentation Model Coupled with Image Restoration Achievements
Image segmentation and image restoration are two important topics in image processing with great achievements.
