Academic Articles & Research Papers

Predictive comparison and evaluation of ANN and ANFIS as effective tools in modeling prodigiosin production by Serratia marcescens ND93.

Evaluating Adversarial Robustness of No-Reference Image and Video Quality Assessment Models with Frequency-Masked Gradient Orthogonalization Adversarial Attack

Neural-network-based models have made considerable progress in many computer vision areas over recent years. However, many works have exposed their vulnerability to malicious input data manipulation—that is, to adversarial attacks. Although...

Role of Smart Image Segmentation in Infection Diagnosis and Monitoring of Acute Surgical Wounds

Background/Purpose: There are challenges in applying artificial intelligence (AI) for acute wound healing assessment due to limited data availability, the dynamic nature of healing, and inter-patient variability. These challenges require fu...

Convolutional-de-convolutional neural networks for recognition of surgical workflow

Computer-assisted surgery (CAS) has occupied an important position in modern surgery, further stimulating the progress of methodology and technology. In recent years, a large number of computer vision-based methods have been widely used in ...

Feature Rescaling and Fusion for Tiny Object Detection

Recent years have witnessed rapid developments on computer vision, however, there are still challenges in detecting tiny objects in a large-scale background. The tiny objects knowledge become sparse and weak due to their tiny size, which ma...

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures

Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior wo...

A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model

Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details...

Evaluation of Modified FGSM-Based Data Augmentation Method for Convolutional Neural Network-Based Image Classification

Computer vision applications demand a significant amount of data for effective training and inference in many computer vision tasks. However, data insufficiency situations usually happen due to multiple reasons, resulting in computational m...

Optimal Coherent Point Selection for 3D Quality Inspection from Silhouette-Based Reconstructions

3D Geometric quality inspection involves assessing and comparing a reconstructed object to a predefined reference model or design that defines its expected volume. Achieving precise 3D object geometry reconstruction from multiple views can ...

Computer Vision, Models and Inspection

The main focus of this book is on the uses of computer vision for inspection and model based matching. It also provides a short, self contained introductory course on computer vision. The authors describe various state-of-the-art approaches...

Comparative examination of YOLO models for classification and utilization in nipple injuries.

Deep Learning for Understanding Satellite Imagery: An Experimental Survey

Translating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in compu...

Online visual tracking by integrating spatio‐temporal cues

The performance of online visual trackers has improved significantly, but designing an effective appearance‐adaptive model is still a challenging task because of the accumulation of errors during the model updating with newly obtained resul...

Monitoring pedestrian walking dynamics under environmental and policy influences in urban spaces

Abstract Understanding how environmental conditions and policy interventions jointly shape pedestrian mobility is essential for designing resilient, adaptive, and human-centered urban spaces. This study presents a computer-vision framework ...

Edge AI for Industrial Visual Inspection: YOLOv8-Based Visual Conformity Detection Using Raspberry Pi

This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieve...

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...

A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of freedom) pose assumptions are widely discussed and studied in the field. In the 3D object detection process, classifications are centered on the object’...

DEEP LEARNING-BASED DOOR AND WINDOW DETECTION FROM BUILDING FAÇADE

Detecting building façade elements is a crucial problem in computer vision for image interpretation. In Building Information Modeling (BIM) studies, the detection of building façade elements has an important role. BIM is a tool that allows ...

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 ...

Survey on Videos Data Augmentation for Deep Learning Models

In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult ...