Academic Articles & Research Papers

Natural language processing with Python

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'...

Collectively encoding protein properties enriches protein language models

Abstract Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such p...

Documenting Computer Vision Datasets

In industrial computer vision, discretionary decisions surrounding the production of image training data remain widely undocumented. Recent research taking issue with such opacity has proposed standardized processes for dataset documentatio...

An image based application in Matlab for automated modelling and morphological analysis of insect wings

Abstract Despite extensive research on the biomechanics of insect wings over the past years, direct mechanical measurements on sensitive wing specimens remain very challenging. This is especially true for examining delicate museum specimens...

Autonomous Driving Roadway Feature Interpretation Using Integrated Semantic Analysis and Domain Adaptation

Lane detection is fundamental to autonomous driving, yet remains challenging in complex environments with occlusions, ambiguous markings, and varied lighting. We introduce the Global Semantic Enhancement Network (GSENet), a groundbreaking f...

Perceptual organization in computer vision: a review and a proposal for a classificatory structure

The role of perceptual organization in computer vision systems is explored. This is done from four vantage points. A brief history of perceptual organization research in both humans and computer vision is offered. A classificatory structure...

An Overview of the Attention Mechanisms in Computer Vision

Abstract Deep convolutional neural network (CNN) plays an important role in the field of computer vision and image processing. In order to further improve the performance of CNN, scholars have conducted a series of new explorations, such as...

UPANets: Learning from the Universal Pixel Attention Neworks

With the successful development in computer vision, building a deep convolutional neural network (CNNs) has been mainstream, considering the character of shared parameters in a convolutional layer. Stacking convolutional layers into a deep ...

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...

MSDNet: A Multi-Scale Feature Representation Network Model for Tunnel Bolt Detection

Bolts are critical components of tunnel linings, essential for ensuring safe tunnel operation. Due to their susceptibility to corrosion and detachment, it is vital to closely monitor them during maintenance. However, identifying a large num...

Membrane protein solubilization and structure determination using de novo-designed proteins.

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 ...

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a st...

Deep Learning for Computer Vision: A Brief Review

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief ...

VISION-LINK AI Smart Glass: An Edge-Cloud Wearable AI System for Workflow Automation and Digital Inclusion in Emerging Economies

The rapid advancement of Artificial Intelligence (AI), wearable computing, and Internet of Things (IoT) technologies has significantly transformed digital interactions across various sectors. However, the adoption of smart wearable technolo...

AN IMPROVED MASK R-CNN: EXTRACTION OF DOOR AND WINDOW INSTANCES ON VILLAGE BUILDING FAÇADE IMAGES

Rapid access to the basic structure of village buildings is conducive to the investigation of the load-bearing bodies of village houses and provides data support for disaster assessment and post-disaster rescue and reconstruction. The devel...

A review of computer vision–based structural health monitoring at local and global levels

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to the computer vision technology application a...

PAMPAS: real-valued graphical models for computer vision

Probabilistic models have been adopted for many computer vision applications, however inference in high-dimensional spaces remains problematic. As the state-space of a model grows, the dependencies between the dimensions lead to an exponent...

Image Motion Extraction of Structures Using Computer Vision Techniques: A Comparative Study

Vibrational measurements play an important role for structural health monitoring, e.g., modal extraction and damage diagnosis. Moreover, conditions of civil structures can be mostly assessed by displacement responses. However, installing di...

Adversarial Attack on Object Detection via Object Feature-Wise Attention and Perturbation Extraction

Deep neural networks are commonly used in computer vision tasks, but they are vulnerable to adversarial samples, resulting in poor recognition accuracy. Although traditional algorithms that craft adversarial samples have been effective in a...