Computer Vision – ACCV 2006
https://doi.org/10.1007/11612032
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https://doi.org/10.1007/11612032
https://doi.org/10.1016/j.aei.2019.100980
10.1016/j.bpj.2025.09.015
PMC: PMC12668823
10.1126/science.adr3817
PMC: PMC13730216
10.1002/advs.77017
PMC: PMC13455832
10.1002/advs.77076
PMC: PMC13460410
Reading text and unified text detection and recognition from natural images are the most challenging applications in computer vision and document analysis. Previously proposed end-to-end scene text reading methods do not consider the freque...
10.3390/app10134474
Efficient large-scale monitoring of methane emissions is crucial for sustainable environmental management, necessitating advanced remote sensing techniques that process hyperspectral imagery to provide detailed insights into the Earthȁ...
10.1109/ACCESS.2025.3597991
The traditional approach to forecasting lies in the assumption that, byapplying a set of powerful analytic tools, the future could be predictedaccurately enough to choose an appropriate strategic direction for it. Theprocess often involves ...
10.5604/01.3001.0055.6305
Melanoma is the skin cancer caused by the ultraviolet radiation from the Sun and has only 15-20% of survival rate. Late diagnosis of melanoma leads to the severe malignancy of disease, and metastasis expands to the other body organs i.e. li...
10.1109/ACCESS.2020.3035345
Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security...
10.3389/frai.2026.1884567
This paper presents the process of designing, fabricating, assembling, programming and optimizing a prototype nonlinear mechatronic Ball-Plate System (BPS) as a laboratory platform for engineering education STEM. Due to the nonlinearity and...
10.3390/s22051875
Background: Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a d...
10.1016/j.crfs.2024.100733
Computer vision, the automatic construction of scene descriptions from image input data, has just entered its second decade. Approaches have varied widely, especially in the amounts of symbolic, domain-dependent knowledge and inference that...
https://doi.org/10.1126/science.224.4655.1299
In object recognition (classification), it was known that the human brain processes visual information in semantic space mainly, that is, extracting the semantically meaningful features such as line-segments, boundaries, shape and so on. Bu...
https://doi.org/10.1109/coginf.2010.5599750
https://doi.org/10.1007/978-3-031-72980-5
10.1002/advs.76917
PMC: PMC13460417
10.1167/tvst.15.10.46
PMC: PMC13730113
Medical image segmentation is a fundamental task in computer vision and plays an important role in clinical diagnosis and treatment planning. Existing methods are mainly built on either convolutional neural networks (CNNs) or Transformer-ba...
10.3389/fbinf.2026.1849059
Background: Digital eye strain (DES) affects an estimated 60 million people worldwide, with a higher prevalence among populations that are occupationally reliant on digital display devices. Aim: This study investigated risk factors of DES ...
10.4102/aveh.v82i1.815