The idea of semantic segmentation is to recognize and understand what is in an image at the pixel-level.
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Exploiting long-range contextual information is key for pixel-wise prediction tasks such as semantic segmentation.
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue.
Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging.
A CNN segmentation model was trained based on the augmented training data by leave-one-out strategy.
Deep learning has largely reduced the need for manual feature selection in image segmentation.
We observed a maximum speed-up of 35. 58x and a minimum speed-up of 10. 21x on popular image processing benchmarks.
For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a downstream document intelligence task.
Our approach is inherently more efficient than the previous two-stage state-of-the-art method, and outperforms it by a margin of 3% IoU for the inpainted foreground regions on Cityscapes.
A detailed comparative analysis is also conducted on the performance of the cGAN network between predicting fluorescence channels based on phase contrast or based on another fluorescence channel using human breast cancer MDA-MB-231 cell line as a test case.
We present an end-to-end trainable approach for optical character recognition (OCR) on printed documents.