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Articles 31 - 45 of 45
Full-Text Articles in Engineering
Discovering The Merit Of The Wavelet Transform For Object Classification, Matthew D. Eyster
Discovering The Merit Of The Wavelet Transform For Object Classification, Matthew D. Eyster
Theses and Dissertations
Vision is the primary sense by which most biological systems collect information about their environment. Computer vision is a branch of artificial intelligence concerned with endowing machines with the ability to understand images. Object recognition is a key part of machine vision with far reaching benefits ranging from target recognition, surveillance systems, to automation systems. Extraction of salient features from an image is one of the key steps in object recognition. Typically, geometric primitives are extracted from an image using local analysis. However, the wavelet transform provides a global approach with good locality. Additionally, the directional and multiresolution properties may …
Reconstruction Algorithm Characterization And Performance Monitoring In Limited-Angle Chromotography, Kevin C. Gustke
Reconstruction Algorithm Characterization And Performance Monitoring In Limited-Angle Chromotography, Kevin C. Gustke
Theses and Dissertations
Hyperspectral data collection and analysis is an increasing priority with the growing need to obtain greater classification precision than offered by traditional spatial imagery. In this thesis, trends in hyperspectral chromotomographic reconstruction are explored where reconstruction is performed using a series of spatial-chromatic images. Chromotomography involves capturing a series of two-dimensional images where each image is created by placing a prism in front of the focal plane array; causing spectral dispersion corresponding to a series of prism angles over a single rotation. Before testing reconstruction, synthetic data is produced, approximating what would be produced from prism dispersion on the focal …
Classification Of Radar Targets Using Invariant Features, Gregory J. Meyer
Classification Of Radar Targets Using Invariant Features, Gregory J. Meyer
Theses and Dissertations
Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the …
Fast Compression Of Imagery With High Frequency Content, D. Scott Anderson
Fast Compression Of Imagery With High Frequency Content, D. Scott Anderson
Theses and Dissertations
Image compression is an active area due to the many applications involving electronic media. Much research has been focused on image quality versus bit rate and/or algorithm speed. Here, we seek an effective image coder with a weighted constraint on speed. However, the compression must not taint the quality of impulsive features on the image. Moreover, the camera is operated in a mode that creates a dominant fixed pattern noise across the image array, degrading visual quality and disrupting compression performance. We propose a method that efficiently compresses such an image. We begin by characterizing and removing the fixed pattern …
Redundant Discrete Wavelet Transform Based Super-Resolution Using Sub-Pixel Image Registration, Daniel L. Ward
Redundant Discrete Wavelet Transform Based Super-Resolution Using Sub-Pixel Image Registration, Daniel L. Ward
Theses and Dissertations
The limited resolution of video imagery taken by aircraft, over geographical areas of interest, hinders the accurate extraction of useful information. The frame resolution of the video is determined by the camera that created it. Information exists about the camera which can be used to increase frame resolution beyond the resolution capability of the camera. This is achieved by a process called super-resolution, which uses multiple low-resolution video frames to create one high-resolution image.
Feature Guided Image Registration Applied To Phase And Wavelet-Base Optic Flow, Kate R. Duffy
Feature Guided Image Registration Applied To Phase And Wavelet-Base Optic Flow, Kate R. Duffy
Theses and Dissertations
Optic Flow algorithms are useful in problems such as computers vision, navigational systems, and robotics. However, current algorithms are computationally expensive or lack the accuracy to be effective compared with traditionally navigation systems. Recently, lower accuracy inertial navigation systems (INS) based on Microelectromechanical systems (MEMS) technology have been proposed to replace more accurate traditional navigation systems.
Translation And Rotation Invariant Multiscale Image Registration, Jennifer L. Manfra
Translation And Rotation Invariant Multiscale Image Registration, Jennifer L. Manfra
Theses and Dissertations
The most recent research involved registering images in the presence of translations and rotations using one iteration of the redundant discrete wavelet transform. We extend this work by creating a new multiscale transform to register two images with translation or rotation differences, independent of scale differences between the images. Our two-dimensional multiscale transform uses an innovative combination of lowpass filtering and the continuous wavelet transform to mimic the two-dimensional redundant discrete wavelet transform. This allows us to obtain multiple subbands at various scales while maintaining the desirable properties of the redundant discrete wavelet transform. Whereas the discrete wavelet transform produces …
Image Registration Using Redundant Wavelet Transforms, Richard K. Brown
Image Registration Using Redundant Wavelet Transforms, Richard K. Brown
Theses and Dissertations
Imagery is collected much faster and in significantly greater quantities today compared to a few years ago. Accurate registration of this imagery is vital for comparing the similarities and differences between multiple images. Since human analysis is tedious and error prone for large data sets, we require an automatic, efficient, robust, and accurate method to register images. Wavelet transforms have proven useful for a variety of signal and image processing tasks, including image registration. In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and …
An Objective Evaluation Of Four Sar Image Segmentation Algorithms, Jason B. Gregga
An Objective Evaluation Of Four Sar Image Segmentation Algorithms, Jason B. Gregga
Theses and Dissertations
Because of the large number of SAR images the Air Force generates and the dwindling number of available human analysts, automated methods must be developed. A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods. This thesis evaluates four SAR image segmentation algorithms by running them on a common set of data and objectively comparing them to each other and to human segmentors. This objective comparison uses a multi-metric a approach with a set of …
Automatic Target Cueing Of Hyperspectral Image Data, Terry A. Wilson
Automatic Target Cueing Of Hyperspectral Image Data, Terry A. Wilson
Theses and Dissertations
Modern imaging sensors produce vast amounts data, overwhelming human analysts. One such sensor is the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The AVIRIS sensor simultaneously collects data in 224 spectral bands that range from 0.4µm to 2.5µm in approximately 10nm increments, producing 224 images, each representing a single spectral band. Autonomous systems are required that can fuse "important" spectral bands and then classify regions of interest if all of this data is to be exploited. This dissertation presents a comprehensive solution that consists of a new physiologically motivated fusion algorithm and a novel Bayes optimal self-architecting classifier …
Evaluation Of A Maximum A-Posteriori Slope Estimator For A Hartmann Wavefront Sensor, Troy B. Van Caster
Evaluation Of A Maximum A-Posteriori Slope Estimator For A Hartmann Wavefront Sensor, Troy B. Van Caster
Theses and Dissertations
Current methods for estimating the wavefront slope at the aperture of a telescope using a Hartmann wavefront sensor are based upon a centroid shift estimator. The centroid shift estimator determines the displacement, or shift, of the centroid off the optical axis using a moment calculation of the intensity distributions recorded in each subaperture. This centroid shift is proportional to the average slope of the wavefront in each subaperture. A maximum a-posteriori (MAP) slope estimator takes advantage of a-priori knowledge of the wavefront slope statistics and total irradiance falling on the subaperture detector arrays when determining the shift estimate. In order …
A Mammographic Registration Method Based On Optical Flow And Multiresolution Computing, Kevin A. Lee
A Mammographic Registration Method Based On Optical Flow And Multiresolution Computing, Kevin A. Lee
Theses and Dissertations
Mammography is a potent weapon in the fight against Breast Cancer, due in large part to its widespread availability and low cost. Despite the fact that mammography can detect small lesions as early as two years before they become palpable on physical exam, between 10 and 30 percent of cancerous lesions go undetected during evaluation by the radiologist. One approach to improving detection rates involves comparing mammograms of the same breast from successive years. Since most forms of breast cancer develop slowly, multiple view techniques might be able to detect subtle changes indicative of cancerous growth. This thesis proposes a …
The Role Of Frame Selection And Bispectrum Phase Reconstruction For Speckle Imaging Through Atmospheric Turbulence, Elizabeth A. Harpold
The Role Of Frame Selection And Bispectrum Phase Reconstruction For Speckle Imaging Through Atmospheric Turbulence, Elizabeth A. Harpold
Theses and Dissertations
Frame selection using quality sharpness metrics have been shown in previous AFIT theses, to be effective in improving the final product of images obtained using adaptive optics. This thesis extends this idea to noncompensated speckle image data. Speckle image reconstruction is simulated with and without frame selection. Speckle images require the processing of hundreds of data frames. Frame selection is a method of reducing the amount of data required to reconstruct the image. A collection of short exposure image data frames of a single object are sorted based on sharpness metrics. Only the highest quality frames are retained and processed …
Perceptual Based Image Fusion With Applications To Hyperspectral Image Data, Terry A. Wilson
Perceptual Based Image Fusion With Applications To Hyperspectral Image Data, Terry A. Wilson
Theses and Dissertations
Development of new imaging sensors has created a need for image processing techniques that can fuse images from different sensors or multiple images produced by the same sensor. The methods presented here focus on combining image data from the Airborne Visual and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor into a single or smaller subset of images while maintaining the visual information necessary for human analysis. Three hierarchical multi-resolution image fusion techniques are implemented and tested using the AVIRIS image data and test images that contain various levels of correlated or uncorrelated noise. Two of the algorithms are published fusion methods …
Color Image Segmentation, Kimberley A. Mccrae
Color Image Segmentation, Kimberley A. Mccrae
Theses and Dissertations
The most difficult stage of automated target recognition ATR is segmentation. Current AFIT segmentation problems include faces and tactical targets previous efforts to segment these objects have used intensity and motion cues. This thesis develops a color preprocessing scheme to be used with the other segmentation techniques. A neural network is trained to identify the color of a desired object, eliminating all but that color from the scene. Gabor correlations and 2D wavelet transformations will be performed on stationary images and 3D wavelet transforms on multispectral data will incorporate color and motion detection into the machine visual system. The thesis …