Deep neural networks (DNNs) have been widely used in the medical imaging field. The large and high quality dataset is crucial for the performance of the deep learning models, but the medical data and ground-truth is often insufficient and very expensive in terms of time and human effort on the data collection. However, we can improve the performance of the deep learning model by augmenting the data we already have. In this work, we introduce a novel differential geometry-based quasi conformal (QC) mapping augmentation technique to augment the brain tumor images. The QC method lets the user specify or randomly generate a complex-valued function on the image domain via Beltrami coefficient. By solving the Beltrami equation with given Beltrami coefficient, the QC map, which can further guide the deformation of the image, is able to generate all possible linear and non-linear image warpings and it is flexible to allow the user to fully control the global and local deformations. Our experimental results demonstrate the efficiency and efficacy of the proposed method.
The hyper-spectrum data exhibits the structure, materials, and semantic meaning of a nature scene and its fast acquisition is of great importance due to its potential for parse these properties of dynamic scenes. Targeting for high speed hyperspectrum imaging of a nature scene, this paper proposes to capture the coded hyper-spectrum reflectance of a nature scene using low cost hardware and reconstruct the latent data using a corresponding decoding algorithm. Except for a wide spectrum light source, the imaging system includes mainly a commercially available projector color wheel and a high speed camera, which work at their own constant periods and are self-synchronized by our algorithm. The introduced light source and color wheel cost less than 50 dollars and makes the proposed approach widely available. The results on the data captured by our prototype system show that, the proposed approach can reconstruct the high precision hyper-spectrum data at real time.
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