In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination plat...
Destructive wildfires pose a serious threat to ecosystems, economic development, and human life and property safety. If wildfires can be extinguished in a relatively short period of time after they occur, the losses caused by wildfires will be greatl...
Solar filaments are a significant solar activity phenomenon, typically observed in full-disk solar observations in the H-alpha band. They are closely associated with the magnetic fields of solar active regions, solar flare eruptions, and coronal mass...
Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods...
Background: Separated endodontic instruments are a significant complication in root canal treatment, affecting disinfection and long-term prognosis. Their detection on panoramic radiographs is challenging, particularly in complex anatomy or for less...
Highlight removal is a critical and challenging problem. In view of the complex highlight phenomenon on the surface of smooth liquor bottles in natural scenes, the traditional highlight removal algorithms cannot semantically disambiguate between all-...
Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on bio...
The main challenge in extracting coastal aquaculture ponds is how to weaken the influence of the “same-spectrum foreign objects” effect and how to improve the definition of the boundary and accuracy of the extraction results of coastal aq...
Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net archite...
Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks....
A vision-based lane detection system is difficult to implement in vehicles due to the unique characteristics of algorithms, neural network architectures, limitations, and strict hardware/software requirements. This paper proposes a new architecture,...
Face unmasking is a critical task in image restoration, as masks conceal essential facial features like the mouth, nose, and chin. Current inpainting methods often struggle with structural fidelity when handling large-area occlusions, leading to blur...
Individual tree species (ITS) classification is one of the key issues in forest resource management. Compared with traditional classification methods, deep learning networks may yield ITS classification results with higher accuracy. In this research,...
Crop disease leaf image segmentation (CDLIS) is the premise of disease detection, disease type recognition and disease degree evaluation. Various convolutional neural networks (CNN) and their modified models have been provided for CDLIS, but their tr...
Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be pr...
The pigments of cultural color paintings have faded with the passage of time. Color segmentations are essential for digital color reconstruction, but the complexity of color paintings makes it challenging to achieve high-precision segmentation using...
Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell les...
Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual i...
Accurate segmentation of coronary artery calcifications (CAC) from cardiac CT is challenged by class imbalance, small lesion size, and anatomical ambiguity. We present an anatomically guided, cascaded framework that couples heart and vessel priors wi...
Accurate brain tumor magnetic resonance imaging (MRI) segmentation is essential for objective lesion assessment and treatment planning, yet it remains challenging because tumors exhibit large-scale variations, irregular morphologies, heterogeneous in...
Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its...
State-of-the-art semantic segmentation methods rely too much on complicated deep networks and thus cannot train efficiently. This paper introduces a novel Circle-U-Net architecture that exceeds the original U-Net on several standards. The proposed mo...
The identification of vascular network structures is one of the key fields of research in medical imaging. The segmentation of dorsal hand vein patterns form NIR images is not only the basis for reliable biometric identification, but would also provi...
Image colorization aims to add plausible colors to grayscale images. However, existing methods often suffer from detail loss, dull colors, and unrealistic results. To address these issues, we propose a novel image colorization method based on a resid...
Convolutional neural networks (CNNs) with different layers have performed with excellent results in watermark removal. However, how to extract robust and effective features via CNNs of black box in watermark removal is very important. In this paper,...
Automated segmentation of brain tumors from Magnetic Resonance Imaging (MRI) images is helpful for clinical diagnosis, surgical planning, and post-treatment monitoring. In recent years, the U-Net architecture has been observed as one of the most popu...
Accurate seamount identification is important for understanding submarine tectonic and magmatic processes and for supporting deep-sea geomorphological analysis. However, seamount recognition faces a severe class imbalance as abyssal plains constitute...
Automatic 3D abdominal organ segmentation in magnetic resonance imaging presents a major challenge: data heterogeneity, caused by differences in acquisition protocol, contrast, spatial resolution, and imaging characteristics, which can degrade model...
Accurate segmentation of renal anatomical structures is essential for informed clinical decision-making in nephropathology, supporting precise diagnosis, treatment planning, and longitudinal monitoring of kidney diseases. In this work, we propose an...
The identification of stay cable icing is crucial for robot deicing to improve efficiency and prevent damage to stay cables. Therefore, it is significant to identify the areas and degree of icing in the images of stay cables. This study proposed a tw...
Image classification and segmentation are important tasks in computer vision. ResNet and U-Net are representative networks for image classification and image segmentation, respectively. Although many scholars used to fuse these two networks, most int...
Background: Integrating artificial intelligence into clinical photography offers great potential for monitoring skin conditions such as actinic keratosis (AK) and skin field cancerization. Identifying the extent of AK lesions often requires more than...
Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from...
Evaluation of the 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) magnetic resonance imaging (MRI) provides excellent bone contrast and can potentially be used in the place of computed tomogra...
Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based varia...
The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguis...
Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and speci...
Traffic sign segmentation is a fundamental component of intelligent transportation systems and autonomous driving, where reliable pixel-level perception is required under challenging real-world conditions such as illumination variations, occlusion, s...
Ancient documents that have undergone physical and visual degradation pose significant challenges in the digital recognition and preservation of information. This research aims to evaluate the effectiveness of ten classic binarization methods, includ...
Background: Quantum Machine Learning (QML) has attracted significant attention in recent years. With quantum computing achievements in computationally costly domains, discovering its potential in improving the performance and efficiency of deep learn...
Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver dise...
Crack detection and analysis are essential for maintaining the stability and longevity of infrastructure; however, traditional manual inspections or simple image processing techniques are inefficient. To address this, automated crack segmentation usi...
Climate change and global warming lead to changes in the sea level and shoreline, which pose a huge threat to island regions. Therefore, it is important to effectively detect the shoreline changes. Taiwan is a typical island, located at the junction...
Existing encoder–decoder architectures operating in the field of deep learning-based image segmentation face fundamental limitations such as information loss, performance degradation as network depth increases, and high computational costs. To...
Brain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, t...
Accurate analysis of Fluorodeoxyglucose (FDG)-Positron Emission Tomography (PET) images is crucial for the diagnosis, treatment assessment, and monitoring of patients suffering from various cancer types. FDG-PET images provide valuable insights by re...
Sound event detection (SED) is the task of finding the identities of sound events, as well as their onset and offset timings from audio recordings. When complete timing information is not available in the training data, but only the event identities...
Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net...
In today’s high-order health examination, imaging examination accounts for a large proportion. Computed tomography (CT), which can detect the whole body, uses X-rays to penetrate the human body to obtain images. Its presentation is a high-resol...
Numerous variants of the basic deep segmentation model—U-Net—have emerged in recent years, achieving reliable performance across different benchmarks. In this paper, we propose an improved version of U-Net with higher performance and redu...
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