Development of high-resolution optical tomography with a larger-size projection acquisition

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on November 11, 2008)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Biological engineering.In the industrial countries, the mortality from arteriosclerosis and cardiovascular diseases has been decreased, but they remain the most common circulatory disease. Recently, new tissue engineering technologies such as tissue-engineered blood vessels (TEBV), produced from a patient's own cells, are becoming available to treat these diseases. In order to monitor the quality of TEBV and assess biomechanical properties, a nondestructive and minimally invasive method is necessary. Optical Tomography (OT) is a proper imaging method because it satisfies the above requirements. Besides, the benefits of OT include: low cost, simple design and ultra fast acquisition. The purpose of this project was to develop an ultra-fast 3D OT scanner by using a beam expander and larger-size CCD cameras instead of pencil beam and smaller-size detectors. The new device is capable of imaging an axial section of about 4mm height in one single revolution. The new OT scanner will primarily be used to obtain the biomechanical properties, the geometry and defects of TEBV. The new device was evaluated by using phantom studies and then tested its performance for the thin tissue layer of TEBV, obtained from Cytograft Tissue Engineering. The results show that the new system has the potential to be a low cost, high tissue contrast, rapid and simple scanner

Delving into Motion-Aware Matching for Monocular 3D Object Tracking

Recent advances of monocular 3D object detection facilitate the 3D multi-object tracking task based on low-cost camera sensors. In this paper, we find that the motion cue of objects along different time frames is critical in 3D multi-object tracking, which is less explored in existing monocular-based approaches. In this paper, we propose a motion-aware framework for monocular 3D MOT. To this end, we propose MoMA-M3T, a framework that mainly consists of three motion-aware components. First, we represent the possible movement of an object related to all object tracklets in the feature space as its motion features. Then, we further model the historical object tracklet along the time frame in a spatial-temporal perspective via a motion transformer. Finally, we propose a motion-aware matching module to associate historical object tracklets and current observations as final tracking results. We conduct extensive experiments on the nuScenes and KITTI datasets to demonstrate that our MoMA-M3T achieves competitive performance against state-of-the-art methods. Moreover, the proposed tracker is flexible and can be easily plugged into existing image-based 3D object detectors without re-training. Code and models are available at https://github.com/kuanchihhuang/MoMA-M3T.Comment: Accepted by ICCV 2023. Code is available at https://github.com/kuanchihhuang/MoMA-M3

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are available on http://vllab.ucmerced.edu/wlai24/LapSRN