Academic Articles & Research Papers

An Input-Perceptual Reconstruction Adversarial Network for Paired Image-to-Image Conversion

Image-to-image conversion based on deep learning techniques is a topic of interest in the fields of robotics and computer vision. A series of typical tasks, such as applying semantic labels to building photos, edges to photos, and raining t...

A Methodological Review on Prediction of Multi-Stage Hypovigilance Detection Systems Using Multimodal Features

Several hypovigilance detection systems (HDx) were developed to avoid road-side accidents due to driver fatigue. They have suffered from several limitations. Notably many of these are focused on center-head position to define an area of int...

Machine learning and computer vision approaches for phenotypic profiling

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to ...

Integrative functional optical imaging approaches: Optics, microfluidics, and machine learning for neuroscience in organoids and small-animal models.

Application of plant viruses in biotechnology with relation to artificial intelligence, machine learning, and nanotechnology.

Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across Modalities.

A novel nondestructive detection approach for seed cotton lint percentage using deep learning

Abstract Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price. The traditional measuring method of lint percentage is labor-intensive and time-consumi...

Geo-Temporal Selective Approach for Dynamic Depth Estimation in Outdoor Object Detection and Distance Measurement

Accurate depth information is crucial for various computer vision applications such as augmented reality, 3D modeling, and autonomous vehicles. Recent advancements have significantly improved both supervised and self-supervised methods for ...

Hyperspectral Methane Plume Segmentation Through Foundation Computer Vision Models

Efficient large-scale monitoring of methane emissions is crucial for sustainable environmental management, necessitating advanced remote sensing techniques that process hyperspectral imagery to provide detailed insights into the Earth&#x201...

Bridging the performance–efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection

Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security...

Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence

Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes ar...

Fruit freshness detection based on multi-task convolutional neural network

Background: Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a d...

Kernel Methods in Computer Vision

Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric ...

Federated Learning in Computer Vision

Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings. Computer vision tasks deal with hu...

Learning techniques used in computer vision for food quality evaluation: a review

Deep Learning-based Classification of Apical Hypertrophic Cardiomyopathy using Left Ventricular Opacification and Enhanced LV Apex Imaging.

DUCK-Net: automated deep learning segmentation of ductular reactions in murine liver injury captures multicellular niche dynamics from H&E morphology.

Performance Benchmarking: Pre-trained Models and Custom Convolutional Neural Networks in Deep Learning

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial ta...

Improved U-Net Model for Road Crack Detection Based on Residual and Attention Mechanism

Road cracks are an important part of road safety detection，and with the development of deep learning and computer vision，methods for extracting crack information in road images using deep learning methods are maturing.Existing deep learning...

Risk factors of digital eye strain among computer users at Al-Baha University

Background: Digital eye strain (DES) affects an estimated 60 million people worldwide, with a higher prevalence among populations that are occupationally reliant on digital display devices. Aim: This study investigated risk factors of DES ...