Intelligent Imaging and Analysis
Imaging and analysis are widely involved in various research fields, including biomedical applications, medical imaging and diagnosis, computer vision, autonomous driving, and robot controls. Imaging and analysis are now facing big changes regarding intelligence, due to the breakthroughs of artifici...
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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 06887naaaa2201837uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_50432 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03921-921-6 | ||
| 020 | |a 9783039219216 | ||
| 020 | |a 9783039219209 | ||
| 024 | 7 | |a 10.3390/books978-3-03921-921-6 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Hwang, Dosik |4 auth | |
| 700 | 1 | |a Kim, DaeEun |4 auth | |
| 245 | 1 | 0 | |a Intelligent Imaging and Analysis |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (492 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Imaging and analysis are widely involved in various research fields, including biomedical applications, medical imaging and diagnosis, computer vision, autonomous driving, and robot controls. Imaging and analysis are now facing big changes regarding intelligence, due to the breakthroughs of artificial intelligence techniques, including deep learning. Many difficulties in image generation, reconstruction, de-noising skills, artifact removal, segmentation, detection, and control tasks are being overcome with the help of advanced artificial intelligence approaches. This Special Issue focuses on the latest developments of learning-based intelligent imaging techniques and subsequent analyses, which include photographic imaging, medical imaging, detection, segmentation, medical diagnosis, computer vision, and vision-based robot control. These latest technological developments will be shared through this Special Issue for the various researchers who are involved with imaging itself, or are using image data and analysis for their own specific purposes. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a statistical body shape model | ||
| 653 | |a weighted kernel density estimation (WKDE) | ||
| 653 | |a greedy projection triangulation | ||
| 653 | |a n/a | ||
| 653 | |a classification methods | ||
| 653 | |a image classification | ||
| 653 | |a intelligent evaluation | ||
| 653 | |a magnetic resonance image | ||
| 653 | |a computational efficiency | ||
| 653 | |a pixel extraction | ||
| 653 | |a convolutional kernel parameter | ||
| 653 | |a computer-aided manufacturing | ||
| 653 | |a long-term and short-term memory blocks | ||
| 653 | |a cavitation bubble | ||
| 653 | |a data imbalance | ||
| 653 | |a optimization arrangement | ||
| 653 | |a sharpness | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a grey level co-occurrence matrix | ||
| 653 | |a image processing | ||
| 653 | |a adaptive evaluation window | ||
| 653 | |a Contrast Tomography (CT) | ||
| 653 | |a semi-automatic segmentation | ||
| 653 | |a mesh partitioning | ||
| 653 | |a non-referential method | ||
| 653 | |a correlation | ||
| 653 | |a PL-SLAM | ||
| 653 | |a contrast | ||
| 653 | |a computer vision | ||
| 653 | |a conformal mapping | ||
| 653 | |a iterative closest points | ||
| 653 | |a image inspection | ||
| 653 | |a intervertebral disc | ||
| 653 | |a shape from focus | ||
| 653 | |a threshold selection | ||
| 653 | |a rail surface defect | ||
| 653 | |a super-resolution | ||
| 653 | |a face sketch synthesis | ||
| 653 | |a normal distribution operator image filtering | ||
| 653 | |a underwater visual localization method | ||
| 653 | |a spline | ||
| 653 | |a high dynamic range | ||
| 653 | |a image enhancement | ||
| 653 | |a image alignment in medical images | ||
| 653 | |a feature extraction | ||
| 653 | |a incrementally probabilistic fusion | ||
| 653 | |a human parsing | ||
| 653 | |a face sketch recognition | ||
| 653 | |a segmentation | ||
| 653 | |a depth-estimation | ||
| 653 | |a self-intersection penalty term | ||
| 653 | |a road scenes | ||
| 653 | |a surface defect of steel sheet | ||
| 653 | |a signed pressure force function | ||
| 653 | |a patient-specific nuss bar | ||
| 653 | |a minimally invasive surgery | ||
| 653 | |a active contour model | ||
| 653 | |a convolutional neural network | ||
| 653 | |a CRF regularization | ||
| 653 | |a motion deburring | ||
| 653 | |a Inception-v3 | ||
| 653 | |a computerized numerical control bending machine | ||
| 653 | |a machine learning | ||
| 653 | |a midsagittal plane extraction | ||
| 653 | |a wear measurement | ||
| 653 | |a OpenCV | ||
| 653 | |a lumbar spine | ||
| 653 | |a local registration | ||
| 653 | |a defect inspection | ||
| 653 | |a graph-based segmentation | ||
| 653 | |a Image processing | ||
| 653 | |a sprocket teeth | ||
| 653 | |a image analysis | ||
| 653 | |a dual-channel | ||
| 653 | |a geological structure images | ||
| 653 | |a defect detection | ||
| 653 | |a saliency detection | ||
| 653 | |a gradient detection | ||
| 653 | |a medical image classification | ||
| 653 | |a low-rank and sparse decomposition | ||
| 653 | |a mesh parameterization | ||
| 653 | |a deviation of strabismus | ||
| 653 | |a 3D pose estimation | ||
| 653 | |a MR spine image | ||
| 653 | |a pectus excavatum | ||
| 653 | |a automated cover tests | ||
| 653 | |a symmetry detection | ||
| 653 | |a automatic training | ||
| 653 | |a medical image registration | ||
| 653 | |a computer-aided design | ||
| 653 | |a PCA | ||
| 653 | |a misalignment correction in MRI | ||
| 653 | |a local correlation | ||
| 653 | |a synthetic aperture radar (SAR) | ||
| 653 | |a pre-training strategy | ||
| 653 | |a sparse feedback | ||
| 653 | |a three-dimensional imaging | ||
| 653 | |a image retrieval | ||
| 653 | |a joint training model | ||
| 653 | |a spatial information | ||
| 653 | |a additional learning | ||
| 653 | |a colorfulness | ||
| 653 | |a nuss procedure | ||
| 653 | |a gray stretch maximum entropy | ||
| 653 | |a vertebral body | ||
| 653 | |a multimodal medical image registration | ||
| 653 | |a machine vision | ||
| 653 | |a deep learning | ||
| 653 | |a point cloud registration | ||
| 653 | |a image restoration | ||
| 653 | |a image segmentation | ||
| 653 | |a segnet | ||
| 653 | |a line segment features | ||
| 653 | |a UAV image | ||
| 653 | |a image adjustment | ||
| 653 | |a pupil localization | ||
| 653 | |a residual block | ||
| 653 | |a transfer learning | ||
| 653 | |a CT image | ||
| 653 | |a U-net | ||
| 653 | |a reverse engineering | ||
| 653 | |a texture mapping | ||
| 653 | |a image denoising | ||
| 653 | |a water hydraulic valve | ||
| 653 | |a fault pattern learning | ||
| 653 | |a fine grain segmentation | ||
| 653 | |a 3D semantic mapping | ||
| 653 | |a level set | ||
| 653 | |a GoogLeNet | ||
| 653 | |a oil slicks | ||
| 653 | |a capacity optimization | ||
| 653 | |a defect segmentation | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2059 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/50432 |7 0 |z DOAB: description of the publication |