104 research outputs found
Medical Image Analysis using Deep Relational Learning
In the past ten years, with the help of deep learning, especially the rapid
development of deep neural networks, medical image analysis has made remarkable
progress. However, how to effectively use the relational information between
various tissues or organs in medical images is still a very challenging
problem, and it has not been fully studied. In this thesis, we propose two
novel solutions to this problem based on deep relational learning. First, we
propose a context-aware fully convolutional network that effectively models
implicit relation information between features to perform medical image
segmentation. The network achieves the state-of-the-art segmentation results on
the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain
Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new
hierarchical homography estimation network to achieve accurate medical image
mosaicing by learning the explicit spatial relationship between adjacent
frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and
our hierarchical homography estimation network outperforms the other
state-of-the-art mosaicing methods while generating robust and meaningful
mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778
Tele-immersive display with live-streamed video.
Tang Wai-Kwan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 88-95).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Applications --- p.3Chapter 1.2 --- Motivation and Goal --- p.6Chapter 1.3 --- Thesis Outline --- p.7Chapter 2 --- Background and Related Work --- p.8Chapter 2.1 --- Panoramic Image Navigation --- p.8Chapter 2.2 --- Image Mosaicing --- p.9Chapter 2.2.1 --- Image Registration --- p.10Chapter 2.2.2 --- Image Composition --- p.12Chapter 2.3 --- Immersive Display --- p.13Chapter 2.4 --- Video Streaming --- p.14Chapter 2.4.1 --- Video Coding --- p.15Chapter 2.4.2 --- Transport Protocol --- p.18Chapter 3 --- System Design --- p.19Chapter 3.1 --- System Architecture --- p.19Chapter 3.1.1 --- Video Capture Module --- p.19Chapter 3.1.2 --- Video Streaming Module --- p.23Chapter 3.1.3 --- Stitching and Rendering Module --- p.24Chapter 3.1.4 --- Display Module --- p.24Chapter 3.2 --- Design Issues --- p.25Chapter 3.2.1 --- Modular Design --- p.25Chapter 3.2.2 --- Scalability --- p.26Chapter 3.2.3 --- Workload distribution --- p.26Chapter 4 --- Panoramic Video Mosaic --- p.28Chapter 4.1 --- Video Mosaic to Image Mosaic --- p.28Chapter 4.1.1 --- Assumptions --- p.29Chapter 4.1.2 --- Processing Pipeline --- p.30Chapter 4.2 --- Camera Calibration --- p.33Chapter 4.2.1 --- Perspective Projection --- p.33Chapter 4.2.2 --- Distortion --- p.36Chapter 4.2.3 --- Calibration Procedure --- p.37Chapter 4.3 --- Panorama Generation --- p.39Chapter 4.3.1 --- Cylindrical and Spherical Panoramas --- p.39Chapter 4.3.2 --- Homography --- p.41Chapter 4.3.3 --- Homography Computation --- p.42Chapter 4.3.4 --- Error Minimization --- p.44Chapter 4.3.5 --- Stitching Multiple Images --- p.46Chapter 4.3.6 --- Seamless Composition --- p.47Chapter 4.4 --- Image Mosaic to Video Mosaic --- p.49Chapter 4.4.1 --- Varying Intensity --- p.49Chapter 4.4.2 --- Video Frame Management --- p.50Chapter 5 --- Immersive Display --- p.52Chapter 5.1 --- Human Perception System --- p.52Chapter 5.2 --- Creating Virtual Scene --- p.53Chapter 5.3 --- VisionStation --- p.54Chapter 5.3.1 --- F-Theta Lens --- p.55Chapter 5.3.2 --- VisionStation Geometry --- p.56Chapter 5.3.3 --- Sweet Spot Relocation and Projection --- p.57Chapter 5.3.4 --- Sweet Spot Relocation in Vector Representation --- p.61Chapter 6 --- Video Streaming --- p.65Chapter 6.1 --- Video Compression --- p.66Chapter 6.2 --- Transport Protocol --- p.66Chapter 6.3 --- Latency and Jitter Control --- p.67Chapter 6.4 --- Synchronization --- p.70Chapter 7 --- Implementation and Results --- p.71Chapter 7.1 --- Video Capture --- p.71Chapter 7.2 --- Video Streaming --- p.73Chapter 7.2.1 --- Video Encoding --- p.73Chapter 7.2.2 --- Streaming Protocol --- p.75Chapter 7.3 --- Implementation Results --- p.76Chapter 7.3.1 --- Indoor Scene --- p.76Chapter 7.3.2 --- Outdoor Scene --- p.78Chapter 7.4 --- Evaluation --- p.78Chapter 8 --- Conclusion --- p.83Chapter 8.1 --- Summary --- p.83Chapter 8.2 --- Future Directions --- p.84Chapter A --- Parallax --- p.8
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