Academic Articles & Research Papers

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...

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...

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’...

DSpix2pix: A New Dual-Style Controlled Reconstruction Network for Remote Sensing Image Super-Resolution

Super-resolution reconstruction is a critical task in remote sensing image classification, and generative adversarial networks (GANs) have emerged as a dominant approach in this field. Traditional generative networks often produce low-quali...

Computer Perceptual Organization in Computer Vision

This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (P...

Computer Vision and Pattern Recognition 2020

This special issue covers a wide range of topics from the area of Computer Vision, Pattern Recognition, and Machine Learning.This breadth of scope is reflected by the papers included in this special issue, which touch topics including geome...

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...

Enhancing Analysis of Diadochokinetic Speech Using Deep Neural Networks.

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 ...

Thangka Mural Line Drawing Based on Cross Dense Residual Architecture and Hard Pixel Balancing

Thangka murals are precious cultural heritage for Tibetan history, literature, and art. Digital line drawing of Thangka murals plays a vital role not only as an abstracted expression of Thangka for art appreciation but also as a fundamental...

The long non-coding RNA LINC00152 is essential for cell cycle progression through mitosis in HeLa cells

Abstract In recent years, long non-coding RNA (lncRNA) research has identified essential roles of these transcripts in virtually all physiological cellular processes including tumorigenesis, but their functions and molecular mechanisms are ...

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 Survey of Deep Learning Techniques and Computer Vision in Robotic and Drone with Applications

The methods of deep learning have lately demonstrated outstanding outcomes of robotic objects such as imagination, localization and striping. Its exceptional abilities in order to learn idealizations from complicated data gathered in the re...

Assessment of the possibility of machine learning for electronic equipment quality prediction

The major algorithms and methods of machine learning are considered. A possibility of machine learning and neural network using for electronic equipment quality prediction is assessed. The paper provides examples of the successful applicati...

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...

Color in Computer Vision

While the field of computer vision drives many of today’s digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color i...

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...

Visual Localization and Target Perception Based on Panoptic Segmentation

Visual localization is a core part of many computer vision and geospatial perception applications; however, the ever-changing time phase and environment present challenges. Moreover, the ever-enriching spatial data types and sensors create ...