Applications of Computer Vision, 2nd Edition
The objective of this reprint is to immerse the reader in the latest advances in computer vision, where leading experts share their insights, research findings, and challenges for the future using images or videos for inspection applications. The research works included use data collected using a wi...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Teme: | |
| Online pristup: | ONIX_20250812T110751_9783725838677_220 |
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| _version_ | 1869522640346021888 |
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| collection | Directory of Open Access Books |
| description | The objective of this reprint is to immerse the reader in the latest advances in computer vision, where leading experts share their insights, research findings, and challenges for the future using images or videos for inspection applications. The research works included use data collected using a wide range of technologies, such as unmanned aerial vehicles (UAVs), remote sensing, color camaras, and X-rays. In terms of objectives, they cover inspection tasks such as vehicle detection or identification, traffic sign detection, automatic QR code classification, defect or target detection in images, and contraband control through small-object detection or image classification. Other applications include image segmentation, image quality enhancement, or face recognition. The last topic discusses the issue of race and gender biases in deep learning for facial recognition. Our goal is to uncover the potential and promise of state-of-the-art methods, such as popular deep learning techniques based on convolutional neural networks (CNNs) and You Only Look Once (YOLO) architectures, by offering an exhaustive comparison between the proposed algorithms and the state-of-the-art methods. This Special Issue paves the way to a future where computer vision can assist or facilitate tedious inspection tasks normally performed by humans. |
| format | Online |
| id | doab-20.500.12854ir-165465 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1654652025-08-12T09:33:52Z Applications of Computer Vision, 2nd Edition Cernadas, Eva computer vision artificial intelligence machine learning image classification text classification deep learning target detection object detection vehicle detection image enhancement semantic segmentation unmanned aerial vehicles UAV drone aerial photography remote sensing attention mechanism transformer vision transformer detection transformer DETR traffic sign detection feature fusion context fusion attentional feature fusion facial beauty prediction multi-task learning discriminative feature learning illumination awareness contraband detection X-ray images transfer learning learning rate overfitting batch size steel rail wear dynamic measurement line-structured light binocular vision iterative closest-point algorithm ICP algorithm workpiece detection loss function feature refinement steel defect detection SSD single-shot detector global context block adaptive equalization network regression loss function binarization QR code integral image logistics biometrics deep learning bias facial recognition race bias convolutional neural network CNN You Only Look Once YOLO thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general The objective of this reprint is to immerse the reader in the latest advances in computer vision, where leading experts share their insights, research findings, and challenges for the future using images or videos for inspection applications. The research works included use data collected using a wide range of technologies, such as unmanned aerial vehicles (UAVs), remote sensing, color camaras, and X-rays. In terms of objectives, they cover inspection tasks such as vehicle detection or identification, traffic sign detection, automatic QR code classification, defect or target detection in images, and contraband control through small-object detection or image classification. Other applications include image segmentation, image quality enhancement, or face recognition. The last topic discusses the issue of race and gender biases in deep learning for facial recognition. Our goal is to uncover the potential and promise of state-of-the-art methods, such as popular deep learning techniques based on convolutional neural networks (CNNs) and You Only Look Once (YOLO) architectures, by offering an exhaustive comparison between the proposed algorithms and the state-of-the-art methods. This Special Issue paves the way to a future where computer vision can assist or facilitate tedious inspection tasks normally performed by humans. 2025-08-12T09:33:50Z 2025-08-12T09:33:50Z 2025 book ONIX_20250812T110751_9783725838677_220 9783725838677 9783725838684 https://directory.doabooks.org/handle/20.500.12854/165465 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10812 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3868-4 10.3390/books978-3-7258-3868-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725838677 9783725838684 358 open access |
| spellingShingle | computer vision artificial intelligence machine learning image classification text classification deep learning target detection object detection vehicle detection image enhancement semantic segmentation unmanned aerial vehicles UAV drone aerial photography remote sensing attention mechanism transformer vision transformer detection transformer DETR traffic sign detection feature fusion context fusion attentional feature fusion facial beauty prediction multi-task learning discriminative feature learning illumination awareness contraband detection X-ray images transfer learning learning rate overfitting batch size steel rail wear dynamic measurement line-structured light binocular vision iterative closest-point algorithm ICP algorithm workpiece detection loss function feature refinement steel defect detection SSD single-shot detector global context block adaptive equalization network regression loss function binarization QR code integral image logistics biometrics deep learning bias facial recognition race bias convolutional neural network CNN You Only Look Once YOLO thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general Applications of Computer Vision, 2nd Edition |
| title | Applications of Computer Vision, 2nd Edition |
| title_full | Applications of Computer Vision, 2nd Edition |
| title_fullStr | Applications of Computer Vision, 2nd Edition |
| title_full_unstemmed | Applications of Computer Vision, 2nd Edition |
| title_short | Applications of Computer Vision, 2nd Edition |
| title_sort | applications of computer vision 2nd edition |
| topic | computer vision artificial intelligence machine learning image classification text classification deep learning target detection object detection vehicle detection image enhancement semantic segmentation unmanned aerial vehicles UAV drone aerial photography remote sensing attention mechanism transformer vision transformer detection transformer DETR traffic sign detection feature fusion context fusion attentional feature fusion facial beauty prediction multi-task learning discriminative feature learning illumination awareness contraband detection X-ray images transfer learning learning rate overfitting batch size steel rail wear dynamic measurement line-structured light binocular vision iterative closest-point algorithm ICP algorithm workpiece detection loss function feature refinement steel defect detection SSD single-shot detector global context block adaptive equalization network regression loss function binarization QR code integral image logistics biometrics deep learning bias facial recognition race bias convolutional neural network CNN You Only Look Once YOLO thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| topic_facet | computer vision artificial intelligence machine learning image classification text classification deep learning target detection object detection vehicle detection image enhancement semantic segmentation unmanned aerial vehicles UAV drone aerial photography remote sensing attention mechanism transformer vision transformer detection transformer DETR traffic sign detection feature fusion context fusion attentional feature fusion facial beauty prediction multi-task learning discriminative feature learning illumination awareness contraband detection X-ray images transfer learning learning rate overfitting batch size steel rail wear dynamic measurement line-structured light binocular vision iterative closest-point algorithm ICP algorithm workpiece detection loss function feature refinement steel defect detection SSD single-shot detector global context block adaptive equalization network regression loss function binarization QR code integral image logistics biometrics deep learning bias facial recognition race bias convolutional neural network CNN You Only Look Once YOLO thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| url | ONIX_20250812T110751_9783725838677_220 |