Academic Articles & Research Papers

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...

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- th...

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...

Deep Learning-Based Eye-Writing Recognition with Improved Preprocessing and Data Augmentation Techniques

Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and...

A lightweight fully convolutional neural network for efficient and accurate tunnel crack detection

Introduction: Crack damage is a significant issue in tunnel environments, requiring efficient and reliable detection to ensure structural integrity. Many existing solutions are computationally expensive, making them unsuitable for the tight...

Integrating Computer Vision: Advancements in Fire and Smoke Detection Using CNNs

Conventional fire detection systems, which depend on smoke and fire sensors, frequently have problems with calibration, susceptibility to environmental changes, and the requirement for ongoing maintenance. These flaws can result in unfavora...

Data Efficient Vision Transformer Based Model for Brain Tumor Classification in MRI

Accurate detection of brain tumors at early stages is crucial for clinical diagnosis and treatment planning. Magnetic Resonance Imaging (MRI) is the most popular method due to its soft-tissue contrast and spatial resolution. However, manual...

Real time distracted driver detection using Xception architecture and Raspberry Pi

Researchers are concentrating on developing technologies to identify and caution drivers against driving while distracted because it is a major cause of traffic accidents. According to the National Highway Traffic Safety Administrator's rep...

Application of Graph Structures in Computer Vision Tasks

On the one hand, the solution of computer vision tasks is associated with the development of various kinds of images or random fields mathematical models, i.e., algorithms, that are called traditional image processing. On the other hand, no...

A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction

Neural networks have been used in various computer vision applications, including noise removal. However, removing seismic noise via deep learning approaches faces a specific issue: the scarcity of labeled data. To address this difficulty, ...

Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection

The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environmen...

Potential of machine learning approaches for predicting mechanical properties of spruce wood in the transverse direction

Abstract To predict the mechanical properties of wood in the transverse direction, this study used machine learning to extract the anatomical features of wood from cross-sectional stereograms. Specimens with different orientations of the ra...

Euclid Quick Data Release (Q1)

Strong gravitational lensing (SL) by galaxy clusters is a powerful probe of their inner mass distribution and a key test bed for cosmological models. However, the detection of SL events in wide-field surveys such as Euclid requires robust a...

Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM

Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, on...

A Guide to Convolutional Neural Networks for Computer Vision

A review of convolutional neural networks in computer vision

Abstract In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of dee...

Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity

The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by ...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution–Pooling Method

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCN...

CG-DINO: Improved Network for Small Object Detection in Uncrewed Aerial Vehicle Imagery Based on DINO

Object detection in uncrewed aerial vehicle (UAV) acquired images remains a challenging task due to extreme scale variation, dense object layouts, and complex backgrounds, etc. Although convolutional neural networks (CNNs), transformers, an...