Generic camera calibration for omnifocus imaging, depth estimation and a train monitoring system

Calibrating an imaging system for its geometric properties is an important step toward understanding the process of image formation and devising techniques to invert this process to decipher interesting properties of the imaged scene. In this dissertation, we propose new optically and physically motivated models for achieving state-of-the-art geometric and photometric camera calibration. The calibration parameters are then applied as input to new algorithms in omnifocus imaging, 3D scene depth from focus and machine vision based intermodal freight train analysis. In the first prat of this dissertation, we present new progress made in the areas of camera calibration with application to omnifocus imaging and 3D scene depth from focus and point spread function calibration. In camera calibration, we propose five new calibration methods for cameras whose imaging model can represented by ideal perspective projection with small distortions due to lens shape (radial distortion) or misaligned lens-sensor configuration (decentering). In the first calibration method, we generalize pupil-centric imaging model to handle arbitrarily rotated lens-sensor configuration, where we consider the sensor tilt to be about the physical optic axis. For such a setting, we derive an analytical solution to linear camera calibration based on collinearity constraint relating the known world points and measured image points assuming no radial distortion. Our second method considers a much simpler case of Gaussian thin-lens imaging model along with non-frontal image sensor and proposes analytical solution to the linear calibration equations derived from collinearity constraint. In the third method, we generalize radial alignment constraint to non-frontal sensor configuration and derive analytical solution to the resulting linear camera calibration equations. In the fourth method, we propose the use of focal stack images of a known checkerboard scene to calibrate cameras having non-frontal sensor. In the fifth method, we show that radial distortion is a result of changing entrance pupil location as a function of incident image rays and propose a collinearity based camera calibration method under this imaging model. Based on this model, we propose a new focus measure for omnifocus imaging and apply it to compute 3D scene depth from focus. We then propose a point spread function calibration method which computes the point spread function (PSF) of a CMOS image sensor using Hadamard patterns displayed on an LCD screen placed at a fixed distance from the sensor. In the second part of the dissertation, we describe a machine vision based train monitoring system, where we propose a motion-based background subtraction method to remove background between the gaps of an inter-modal freight train. The background subtracted image frames are used to compute a panoramic mosaic of the train and compute gap length in pixels. The gap length computed in metric units using the calibration parameters of the video camera allows for analyzing the fuel efficiency of loading pattern of the given inter-modal freight train

MOTION-BASED BACKGROUND SUBTRACTION AND PANORAMIC MOSAICING FOR FREIGHT TRAIN ANALYSIS

We propose a new motion-based background removal technique which along with panoramic mosaicing forms the core of a vision system we have developed for analyzing the loading efficiency of intermodal freight trains. This analysis is critical for estimating the aerodynamic drag caused by air gaps present between loads in freight trains. The novelty of our background removal technique lies in using conventional motion estimates to design a cost function which can handle challenging texture-less background regions, e.g. clear blue sky. Supplemented with domain knowledge, we have built a system which has outperformed some recent background removal methods applied to our problem. We also build an orthographic mosaic of the freight train allowing identification of load types and gap lengths between them. The complete system has been installed near Sibley, Missouri, US and processes about 20-30 (5-10 GB/train video data depending on train length) trains per day with high accuracy. Index Terms — background removal, panoramic mosaicing, intermodal freight train, wayside inspection 1

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion