Robust Localization in 3D Prior Maps for Autonomous Driving.

In order to navigate autonomously, many self-driving vehicles require precise localization within an a priori known map that is annotated with exact lane locations, traffic signs, and additional metadata that govern the rules of the road. This approach transforms the extremely difficult and unpredictable task of online perception into a more structured localization problem—where exact localization in these maps provides the autonomous agent a wealth of knowledge for safe navigation. This thesis presents several novel localization algorithms that leverage a high-fidelity three-dimensional (3D) prior map that together provide a robust and reliable framework for vehicle localization. First, we present a generic probabilistic method for localizing an autonomous vehicle equipped with a 3D light detection and ranging (LIDAR) scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the z-height and reflectivity distribution of the environment—which we rasterize to facilitate fast and exact multiresolution inference. Second, we propose a visual localization strategy that replaces the expensive 3D LIDAR scanners with significantly cheaper, commodity cameras. In doing so, we exploit a graphics processing unit to generate synthetic views of our belief environment, resulting in a localization solution that achieves a similar order of magnitude error rate with a sensor that is several orders of magnitude cheaper. Finally, we propose a visual obstacle detection algorithm that leverages knowledge of our high-fidelity prior maps in its obstacle prediction model. This not only provides obstacle awareness at high rates for vehicle navigation, but also improves our visual localization quality as we are cognizant of static and non-static regions of the environment. All of these proposed algorithms are demonstrated to be real-time solutions for our self-driving car.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133410/1/rwolcott_1.pd

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement

We present a novel spatiotemporal saliency detection method to estimate salient regions in videos based on the gradient flow field and energy optimization. The proposed gradient flow field incorporates two distinctive features: 1) intra-frame boundary information and 2) inter-frame motion information together for indicating the salient regions. Based on the effective utilization of both intra-frame and inter-frame information in the gradient flow field, our algorithm is robust enough to estimate the object and background in complex scenes with various motion patterns and appearances. Then, we introduce local as well as global contrast saliency measures using the foreground and background information estimated from the gradient flow field. These enhanced contrast saliency cues uniformly highlight an entire object. We further propose a new energy function to encourage the spatiotemporal consistency of the output saliency maps, which is seldom explored in previous video saliency methods. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Super Resolution of Wavelet-Encoded Images and Videos

In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images