Academic Articles & Research Papers

High Performance GPU Strategies for Real-Time In-Cabin Monitoring

With the expeditious advancement in multicarrier automotive in-vehicle monitoring for a bus conforming environment with computer vision, there is an exigency for a setup subsisting on inspection cameras and an onboard embedded computing dev...

A Self-Adaptive Traffic Signal System Integrating Real-Time Vehicle Detection and License Plate Recognition for Enhanced Traffic Management

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic viola...

A comprehensive analytical assessment of dino-based architectures for sustainable automated potato inspection in the Al Kharj Region

Abstract Self-supervised learning (SSL) has emerged as a pivotal paradigm in computer vision, particularly for applications where labeled data are limited, costly, or labor-intensive to obtain. This paradigm is especially relevant to sustai...

Discriminative feature encoding for intrinsic image decomposition

Abstract Intrinsic image decomposition is an important and long-standing computer vision problem. Given an input image, recovering the physical scene properties is ill-posed. Several physically motivated priors have been used to restrict th...

A lightweight optimization framework for real-time pedestrian detection in dense and occluded scenes

Pedestrian detection is one of the most widely applied tasks in industrial computer vision. It encapsulates three core challenges of object detection: detecting small objects, handling heavy occlusion, and balancing speed and accuracy for d...

Development of a hybrid artificial intelligence framework for pharmacotherapy optimization.

FOCUS: a feature optimization and cascaded unified screening strategy for computer-aided pathology-based glioma molecular subtype classification.

A Novel Mixed-Precision Quantization Approach for CNNs

Model size and inference speed have brought about major challenges for the deployment of Convolutional Neural Networks (CNNs) in many applications. An effective approach to address this issue is model quantization, which achieves network co...

Toward Automated Structural Design for Controlled Vibration Characteristics Using Topology Optimization and Computer Vision in Space Missions

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing custom-developed photogrammetry software, high-resolution ...

Scale Accuracy Evaluation of Image-Based 3D Reconstruction Strategies Using Laser Photogrammetry

Rapid developments in the field of underwater photogrammetry have given scientists the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This pa...

OmniVO: Toward Robust Omni Directional Visual Odometry With Multicamera Collaboration for Challenging Conditions

With the recent developments in computer vision, vision-based odometry plays an increasingly important role in the field of autonomous systems. However, using traditional visual odometry or simultaneous localization and mapping (vSLAM) only...

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in ...

Enhanced multimodal transformer framework for sarcasm detection with particle swarm optimization based feature fusion.

Cross-Attention Joint Denoising and Semantic Segmentation for Robust Image Analysis

Semantic segmentation assigns class labels at the pixel level, forming a key part of various computer vision applications, including autonomous driving, medical imaging, and robotics. However, segmentation accuracy often drops significantly...

Enhanced Skin Condition Prediction Through Machine Learning Using Dynamic Training and Testing Augmentation

In recent years, deep learning has taken the spotlight in automated medical bioimaging. However, the performance of current state-of-the-art score stems primarily from well-tuned parameters and architecture. There is still only limited rese...

An Efficient Optimization Technique for Training Deep Neural Networks

Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a neural network. It has many applications in the field of banking, automobile industry, agriculture, and healthcare industry. Deep learning has pl...

FIR-SDE: fast image restoration via mean-reverting stochastic differential equation

Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data...

Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks

One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, re...

Evaluation of Indoor Thermal Environments Using a Novel Predicted Mean Vote Model Based on Artificial Neural Networks

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefo...

Bayesian Regularization of Neural Networks

Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...