Che-Hao Hsu

TL;DR: This study attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques and introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images.

TL;DR: An efficient automatic method for classifying a document image using a probabilistic decision strategy that is tailored to inexpensive hardware and significantly reduces both the running time and memory requirements compared to the existing algorithms, while substantially improving the classification accuracy.

TL;DR: The experimental results and user studies verify that the proposed HoloTabletop system offers excellent stereo vision while no visual fatigue will be caused to human eyes.

TL;DR: The proposed display system has the advantages of ease of use, low production cost, high portability and mobility, and the user study verified that the proposed display offers satisfactory depth perception for various viewing distances and angles without noticeable discomfort.

TL;DR: The proposed CrossbowCam is suitable for multi-viewpoint image acquisition, smooth switching, alignment and seamless stitching applications, and can potentially change the consumer habits and becomes the new type of home-use handheld camcorder system in the future.