This paper proposes an image augmentation model of limited samples on the mobile platform for object tracking. The augmentation method mainly aims at the detection failure caused by the small number of effective samples, jitter of tracking platform,...
As augmented reality (AR) technologies become increasingly integrated into everyday life, privacy-maintenance concerns about their capacity to capture and process sensitive visual data also increase. Visual data sanitization and obfuscation may effec...
Augmented Reality (AR) superimposes computer graphics (CG) onto a user’s view of the real world. A key quality problem in this field is to achieve coherence between reality and CG when the user’s eyes refocus or change pupil size. We designed and eva...
The effective recognition of patterns from blurred images presents a fundamental difficulty for many practical vision tasks. In the era of deep learning, the main ideas to cope with this difficulty are data augmentation and deblurring. However, both...
The combination of gesture recognition and aerospace exploration robots can realize the efficient non-contact control of the robots. In the harsh aerospace environment, the captured gesture images are usually blurred and damaged inevitably. The motio...
Military object detection from Unmanned Aerial Vehicle (UAV) reconnaissance images faces challenges, including lack of image data, images with poor quality, and small objects. In this work, we simulate UAV low-altitude reconnaissance and construct th...
Introduction: Cone-beam computed tomography (CBCT) has been applied to implant dentistry. The increasing use of this technology produces a critical number of images that can be used for training artificial intelligence (AI). Objectives: To investigat...
Using a deep learning algorithm in the development of a computer-aided system for colon polyp detection is effective in reducing the miss rate. This study aimed to develop a system for colon polyp detection and classification. We used a data augmenta...
Reconstructing animatable humans, together with their surrounding static environments, from monocular, motion-blurred videos is still challenging for current neural rendering methods. Existing monocular human reconstruction approaches achieve impress...
The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of dron...
Visual search becomes challenging when the time to find the target is limited. Here we focus on how performance in visual search can be improved via a subtle saliency-aware modulation of the scene. Specifically, we investigate whether blurring salien...
Data augmentation (DA) is an effective way to enrich the richness of data and improve a model’s generalization ability. It has been widely used in many advanced vision tasks (e.g., classification, recognition, etc.), while it can hardly be seen...
Visual information is crucial in human life, not only providing critical support for communication, learning, and decision-making, but also playing a key role in psychology, medicine, and science. Eye-tracking and gaze estimation have promoted the de...
New technology has dramatically changed online games and blurred the boundary between active and passive activities. This study aims to explore the meanings and values of augmented reality online games by examining users’ Pokémon Go experiences throu...
Vehicle re-identification (ReID) tasks are an important part of smart cities and are widely used in public security. It is extremely challenging because vehicles with different identities are generated from a uniform pipeline and cannot be distinguis...
In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. Th...
In order to reach the highest level of automation, autonomous vehicles (AVs) are required to be aware of surrounding objects and detect them even in adverse weather. Detecting objects is very challenging in sandy weather due to characteristics of the...
Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditi...
We previously reported on ‘Tear Film Oriented Diagnosis’ (TFOD), a method for the dry eye (DE) subtype classification using fluorescein staining and an examination of fluorescein breakup patterns via slit-lamp biomicroscopy. Here, we repo...
Reliable and efficient detection of persimmons provides the foundation for precise maturity evaluation. Persimmon ripeness detection remains challenging due to small target sizes, frequent occlusion by foliage, and motion- or focus-induced blur that...
Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be rep...
In this work, we detail a methodology based on Convolutional Neural Networks (CNNs) to detect falls from non-invasive thermal vision sensors. First, we include an agile data collection to label images in order to create a dataset that describes sever...
In industrial automation, detecting defects in threaded components is challenging due to their complex geometry and the concealment of micro-flaws. This paper presents an integrated vision system capable of inspecting both internal and external threa...
The paper describes the process of designing a simple fiducial marker. The marker is meant for use in augmented reality applications. Unlike other systems, it does not encode any information, but it can be used for obtaining the position, rotation, r...
Image inpainting is a crucial task in computer vision that aims to restore missing and occluded parts of damaged images. Deep-learning-based image inpainting methods have gained popularity in recent research. One such method is the deep image prior,...
Strawberries are highly perishable fruits prone to postharvest losses due to defects, diseases, and uneven ripening. This study proposes a deep learning-based approach for automated quality assessment using the YOLOv8n object detection model. A custo...
The operational reliability of wind turbines is critical for sustainable energy production in smart grids. This study proposes a remote monitoring approach using perceptually enhanced satellite imagery. Sentinel-2 multispectral data (10 m resolution)...
Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and a...
Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is avai...
Breast cancer is recognized as an aggressive cancer with the highest rate of mortality. Ultrasound imaging is a non-invasive and cost-effective strategy which is most frequently utilized in clinical methods. Especially, in ultrasound scan, breast tum...
In object detection, Transformer-based models such as DETR have exhibited state-of-the-art performance, capitalizing on the attention mechanism to handle spatial relations and feature dependencies. One inherent challenge these models face is the inte...
Aiming at the problems of low-resolution steel surface defects imaging, such as defect type confusion, feature blurring, and low classification accuracy, this paper proposes an autocorrelation semantic enhancement network (ASENet) for the classificat...
The analysis and follow up of asphalt infrastructure using image processing techniques has received increased attention recently. However, the vast majority of developments have focused only on determining the presence or absence of road damages, for...
Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operation...
Over the past several years, many children have died from suffocation due to being left inside a closed vehicle on a sunny day. Various vehicle manufacturers have proposed a variety of technologies to locate an unattended child in a vehicle, includin...
In the rapidly evolving field of biometric authentication, deep learning has become a cornerstone technology for face detection and recognition tasks. However, traditional optimizers often struggle with challenges such as overfitting, slow convergenc...
Camera pose estimation is vital in fields like robotics, medical imaging, and augmented reality. Fiducial markers, specifically ArUco and Apriltag, are preferred for their efficiency. However, their accuracy and viewing angle are limited when used as...
Reliable object detection is crucial for autonomous driving, particularly in highway construction zones where early hazard recognition ensures safety. This paper introduces an enhanced YOLOv8s-based detection system incorporating a motion-temporal at...
This study presents a segmentation framework for images of 220 kV cable insulation that addresses sample scarcity and blurred boundaries. The framework integrates data augmentation using the Wasserstein Generative Adversarial Network with Gradient Pe...
Mineral recognition technology is crucial for improving mining efficiency and advancing smart mining development. To enable the efficient deployment of graphite ore grade detection on edge computing devices, we propose Stellar-YOLO, a YOLO11-based de...
Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent ye...
12 December 2024
Tang Dynasty female terracotta figurines, as important relics of ceramics art, have commonly suffered from natural and man-made damages, among which facial damage is severe. Image inpainting is widely used in cultural heritage fields such as murals a...
The greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Tradition...
To address the issues of low recognition rates and poor detection accuracy for road negative obstacles caused by insufficient feature representation, we propose a novel detection framework: the Negative Road Obstacles Segmentation Network (NROSegNet)...
Wafer characters are used to record the transfer of important information in industrial production and inspection. Wafer character recognition is usually used in the traditional template matching method. However, the accuracy and robustness of the te...
The automated detection of distress such as cracks or potholes is a key basis for assessing the condition of pavements and deciding on their maintenance. A fine-grained pavement distress-detection algorithm based on integrated data using a digital tw...
In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today...
Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural Network (CNN...
Real-time 3D scene reconstruction has attracted a great amount of attention in the fields of augmented reality, virtual reality and robotics. Previous works usually assumed slow sensor motions to avoid large interframe differences and strong image bl...
Depth estimation represents a prevalent research focus within the realm of computer vision. Existing depth estimation methodologies utilizing LiDAR (Light Detection and Ranging) technology typically obtain sparse depth data and are associated with el...
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