Reprint
Intelligent Imaging and Analysis
Edited by
March 2020
492 pages
- ISBN978-3-03921-920-9 (Paperback)
- ISBN978-3-03921-921-6 (PDF)
This book is a reprint of the Special Issue Intelligent Imaging and Analysis that was published in
Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
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.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
image inspection; non-referential method; feature extraction; fault pattern learning; weighted kernel density estimation (WKDE); rail surface defect; UAV image; defect detection; gray stretch maximum entropy; image enhancement; defect segmentation; semi-automatic segmentation; MR spine image; vertebral body; graph-based segmentation; correlation; surface defect of steel sheet; image segmentation; saliency detection; low-rank and sparse decomposition; intervertebral disc; segmentation; convolutional neural network; fine grain segmentation; U-net; deep learning; magnetic resonance image; lumbar spine; image adjustment; colorfulness; contrast; sharpness; high dynamic range; local registration; iterative closest points; multimodal medical image registration; machine vision; point cloud registration; greedy projection triangulation; local correlation; three-dimensional imaging; optimization arrangement; cavitation bubble; water hydraulic valve; defect inspection; image processing; feature extraction; classification methods; medical image registration; image alignment in medical images; misalignment correction in MRI; midsagittal plane extraction; symmetry detection; PCA; conformal mapping; mesh parameterization; mesh partitioning; pixel extraction; texture mapping; image analysis; image retrieval; spatial information; image classification; computer vision; image restoration; motion deburring; image denoising; sparse feedback; Image processing; segmentation; spline; grey level co-occurrence matrix; gradient detection; threshold selection; OpenCV; machine learning; transfer learning; Inception-v3; geological structure images; convolutional neural networks; image segmentation; active contour model; level set; signed pressure force function; image segmentation; deep learning; synthetic aperture radar (SAR); oil slicks; segnet; pectus excavatum; nuss procedure; patient-specific nuss bar; minimally invasive surgery; computerized numerical control bending machine; computer-aided design; computer-aided manufacturing; statistical body shape model; self-intersection penalty term; 3D pose estimation; 3D semantic mapping; incrementally probabilistic fusion; CRF regularization; road scenes; deep learning; medical image classification; additional learning; CT image; automatic training; GoogLeNet; intelligent evaluation; automated cover tests; deviation of strabismus; pupil localization; shape from focus; wear measurement; sprocket teeth; normal distribution operator image filtering; adaptive evaluation window; reverse engineering; human parsing; depth-estimation; computational efficiency; capacity optimization; underwater visual localization method; line segment features; PL-SLAM; face sketch synthesis; face sketch recognition; joint training model; data imbalance; Contrast Tomography (CT); pre-training strategy; segmentation; super-resolution; dual-channel; residual block; convolutional kernel parameter; long-term and short-term memory blocks; n/a