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Found 19 papers, 8 papers with code
Interactive Manipulation and Visualization of 3D Brain MRI for Surgical Training
In modern medical diagnostics, magnetic resonance imaging (MRI) is an important technique that provides detailed insights into anatomical structures.
Feature CAM: Interpretable AI in Image Classification
We then extend the same for eight CNN-based architectures to compare the differences in visualization and thus interpretability.
Subjective and Objective Visual Quality Assessment of Textured 3D Meshes
Based on analysis of the results, we propose two new metrics for visual quality assessment of textured mesh, as optimized linear combinations of accurate geometry and texture quality measurements.
Parkinson's Disease Detection with Ensemble Architectures based on ILSVRC Models
In this work, we explore various neural network architectures using Magnetic Resonance (MR) T1 images of the brain to identify Parkinson's Disease (PD), which is one of the most common neurodegenerative and movement disorders.
Parkinson's Disease Detection Using Ensemble Architecture from MR Images
We find that detection accuracy increases drastically when we focus on the Gray Matter (GM) and White Matter (WM) regions from the MR images instead of using whole MR images.
An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation
GenInSAR's Phase, and Coherence Root-Mean-Squared-Error and Phase Cosine Error have average improvements of 0. 54, 0. 07, and 0. 05 respectively compared to the related methods.
Adaptive Dithering Using Curved Markov-Gaussian Noise in the Quantized Domain for Mapping SDR to HDR Image
We vary the magnitude and structure of the noise pattern adaptively based on the luma of the quantized pixel and the slope of the inverse-tone mapping function.
CNN-based InSAR Denoising and Coherence Metric
Interferometric Synthetic Aperture Radar (InSAR) imagery for estimating ground movement, based on microwaves reflected off ground targets is gaining increasing importance in remote sensing.
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance
We propose a novel direction to improve the denoising quality of filtering-based denoising algorithms in real time by predicting the best filter parameter value using a Convolutional Neural Network (CNN).
CNN-based InSAR Coherence Classification
Interferometric Synthetic Aperture Radar (InSAR) imagery based on microwaves reflected off ground targets is becoming increasingly important in remote sensing for ground movement estimation.
Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment
However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are difficult to obtain and standardise.
DeepInSAR: A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation
In this work, we propose a deep convolutional neural network (CNN) based model DeepInSAR to intelligently solve both the phase filtering and coherence estimation problems.
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow
This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm.
A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants
We detect the ventricles as blobs using a fast linear Maximally Stable Extremal Regions algorithm.
Towards the identification of Parkinson's Disease using only T1 MR Images
Our proposed method has three main steps : 1) Preprocessing, 2) Fea- ture Extraction, and 3) Classification.
Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selection
Secondly, we propose a region selection strategy to label two ERELs as lumen and media based on the stability of their textural information.
Learning Local Distortion Visibility From Image Quality Data-sets
In this paper, we explore a different perspective, and we investigate whether it is possible to learn local distortion visibility from image quality scores.
Highlighting objects of interest in an image by integrating saliency and depth
However, the depth information acquired from stereo can also be used along with saliency to highlight certain objects in a scene.
Entropy-difference based stereo error detection
As a remedy, we propose a novel error detection approach based solely on the input image and its depth map.
