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Found 24 papers, 3 papers with code
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational complexities with high dimensional data.
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7. 6.
Efficient quantum image representation and compression circuit using zero-discarded state preparation approach
The encoding of images for representation and compression in quantum systems is different from classical ones.
Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation
Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud.
Ranked #1 on
3D Object Classification
on ModelNet40
A novel state connection strategy for quantum computing to represent and compress digital images
Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value.
Vision-Based Robust Lane Detection and Tracking under Different Challenging Environmental Conditions
However, detecting lane is highly challenging when the visibility of a road lane marking is low due to real-life challenging environment and adverse weather.
Analysis and prediction of heart stroke from ejection fraction and serum creatinine using LSTM deep learning approach
With the availability of a large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped to predict the future trend of any health problems.
Rice Leaf Disease Classification and Detection Using YOLOv5
We have annotate 1500 collected data sets and propose a rice leaf disease classification and detection method based on YOLOv5 deep learning.
Efficient Motion Modelling with Variable-sized blocks from Hierarchical Cuboidal Partitioning
Specifically, we have constructed motion-compensated current frame using the cuboidal partitioning information of the anchor frame in a group-of-picture (GOP).
Efficient dynamic point cloud coding using Slice-Wise Segmentation
In the proposed method, the entire point cloud has been cross-sectioned into variable-sized slices based on the number of self-occluded points so that data loss can be minimized in the patch generation process and projection.
Dynamic Point Cloud Compression with Cross-Sectional Approach
However, to broadcast successfully, the dynamic point clouds require higher compression due to their huge volume of data compared to the traditional video.
Dilated convolutional neural network-based deep reference picture generation for video compression
Motion estimation and motion compensation are indispensable parts of inter prediction in video coding.
Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection
In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.
Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring
We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement.
Human-Machine Collaborative Video Coding Through Cuboidal Partitioning
This is because video coding targets human perception, while feature coding aims for machine vision tasks.
Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images
Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
MAVIDH Score: A COVID-19 Severity Scoring using Chest X-Ray Pathology Features
As the primary contribution, this method correlates well to patient severity in different stages of disease progression with competitive results compared to other existing, more complex methods.
Potential Features of ICU Admission in X-ray Images of COVID-19 Patients
X-ray images may present non-trivial features with predictive information of patients that develop severe symptoms of COVID-19.
Hyperspectral Imaging to detect Age, Defects and Individual Nutrient Deficiency in Grapevine Leaves
Hyperspectral (HS) imaging was successfully employed in the 380 nm to 1000 nm wavelength range to investigate the efficacy of detecting age, healthiness and individual nutrient deficiency of grapevine leaves collected from vineyards located in central west NSW, Australia.
Rain Streak Removal in a Video to Improve Visibility by TAWL Algorithm
In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy.
A Practical Blockchain Framework using Image Hashing for Image Authentication
As a blockchain system can be useful for maintaining data integrity, image authentication has the potential to be enhanced by blockchain.
Computer Vision For COVID-19 Control: A Survey
The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population.
Automatically Assessing Quality of Online Health Articles
The information ecosystem today is overwhelmed by an unprecedented quantity of data on versatile topics are with varied quality.
Enhanced Transfer Learning with ImageNet Trained Classification Layer
Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task.
