Background-oriented schlieren (BOS) for scramjet inlet-isolator investigation

Background-oriented Schlieren (BOS) technique is a recently invented non-intrusive flow diagnostic method which has yet to be fully explored in its capabilities. In this paper, BOS technique has been applied for investigating the general flow field characteristics inside a generic scramjet inlet-isolator with Mach 5 flow. The difficulty in finding the delicate balance between measurement sensitivity and measurement area image focusing has been demonstrated. The differences between direct cross-correlation (DCC) and Fast Fourier Transform (FFT) raw data processing algorithm have also been demonstrated. As an exploratory study of BOS capability, this paper found that BOS is simple yet robust enough to be used to visualize complex flow in a scramjet inlet in hypersonic flow. However, in this case its quantitative data can be strongly affected by 3-dimensionality thus obscuring the density value with significant errors

A flexible hardware architecture for 2-D discrete wavelet transform: design and FPGA implementation

The Discrete Wavelet Transform (DWT) is a powerful signal processing tool that has recently gained widespread acceptance in the field of digital image processing. The multiresolution analysis provided by the DWT addresses the shortcomings of the Fourier Transform and its derivatives. The DWT has proven useful in the area of image compression where it replaces the Discrete Cosine Transform (DCT) in new JPEG2000 and MPEG4 image and video compression standards. The Cohen-Daubechies-Feauveau (CDF) 5/3 and CDF 9/7 DWTs are used for reversible lossless and irreversible lossy compression encoders in the JPEG2000 standard respectively. The design and implementation of a flexible hardware architecture for the 2-D DWT is presented in this thesis. This architecture can be configured to perform both the forward and inverse DWT for any DWTfamily, using fixed-point arithmetic and no auxiliary memory. The Lifting Scheme method is used to perform the DWT instead of the less efficient convolution-based methods. The DWT core is modeled using MATLAB and highly parameterized VHDL. The VHDL model is synthesized to a Xilinx FPGA to prove hardware functionality. The CDF 5/3 and CDF 9/7 versions of the DWT are both modeled and used as comparisons throughout this thesis. The DWT core is used in conjunction with a very simple image denoising module to demonstrate the potential of the DWT core to perform image processing techniques. The CDF 5/3 hardware produces identical results to its theoretical MATLAB model. The fixed point CDF 9/7 deviates very slightly from its floating-point MATLAB model with a ~59dB PSNR deviation for nine levels of DWT decomposition. The execution time for performing both DWTs is nearly identical at -14 clock cycles per image pixel for one level of DWT decomposition. The hardware area generated for the CDF 5/3 is -16,000 gates using only 5% of the Xilinx FPGA hardware area, 2.185 MHz maximum clock speed and 24 mW power consumption. The simple wavelet image denoising techniques resulted in cleaned images up to -27 PSNR

Segmentation of the glottal space from laryngeal images using the watershed transform

The present work describes a new method for the automatic detection of the glottal space from laryngeal images obtained either with high speed or with conventional video cameras attached to a laryngoscope. The detection is based on the combination of several relevant techniques in the field of digital image processing. The image is segmented with a watershed transform followed by a region merging, while the final decision is taken using a simple linear predictor. This scheme has successfully segmented the glottal space in all the test images used. The method presented can be considered a generalist approach for the segmentation of the glottal space because, in contrast with other methods found in literature, this approach does not need either initialization or finding strict environmental conditions extracted from the images to be processed. Therefore, the main advantage is that the user does not have to outline the region of interest with a mouse click. In any case, some a priori knowledge about the glottal space is needed, but this a priori knowledge can be considered weak compared to the environmental conditions fixed in former works

High-Fidelity and Perfect Reconstruction Techniques for Synthesizing Modulation Domain Filtered Images

Biomimetic processing inspired by biological vision systems has long been a goal of the image processing research community, both to further understanding of what it means to perceive and interpret image content and to facilitate advancements in applications ranging from processing large volumes of image data to engineering artificial intelligence systems. In recent years, the AM-FM transform has emerged as a useful tool that enables processing that is intuitive to human observers but would be difficult or impossible to achieve using traditional linear processing methods. The transform makes use of the multicomponent AM-FM image model, which represents imagery in terms of amplitude modulations, representative of local image contrast, and frequency modulations, representative of local spacing and orientation of lines and patterns. The model defines image components using an array of narrowband filterbank channels that is designed to be similar to the spatial frequency channel decomposition that occurs in the human visual system. The AM-FM transform entails the computation of modulation functions for all components of an image and the subsequent exact recovery of the image from those modulation functions. The process of modifying the modulation functions to alter visual information in a predictable way and then recovering the modified image through the AM-FM transform is known as modulation domain filtering. Past work in modulation domain filtering has produced dramatic results, but has faced challenges due to phase wrapping inherent in the transform computations and due to unknown integration constants associated with modified frequency content. The approaches developed to overcome these challenges have led to a loss of both stability and intuitive simplicity within the AM-FM model. In this dissertation, I have made significant advancements in the underlying processes that comprise the AM-FM transform. I have developed a new phase unwrapping method that increases the stability of the AM-FM transform, allowing higher quality modulation domain filtering results. I have designed new reconstruction techniques that allow for successful recovery from modified frequency modulations. These developments have allowed the design of modulation domain filters that, for the first time, do not require any departure from the simple and intuitive nature of the basic AM-FM model. Using the new modulation domain filters, I have produced new and striking results that achieve a variety of image processing tasks which are motivated by biological visual perception. These results represent a significant advancement relative to the state of the art and are a foundation from which future advancements in the field may be attained

Subspace methods for portfolio design

Financial signal processing (FSP) is one of the emerging areas in the field of signal processing. It is comprised of mathematical finance and signal processing. Signal processing engineers consider speech, image, video, and price of a stock as signals of interest for the given application. The information that they will infer from raw data is different for each application. Financial engineers develop new solutions for financial problems using their knowledge base in signal processing. The goal of financial engineers is to process the harvested financial signal to get meaningful information for the purpose. Designing investment portfolios have always been at the center of finance. An investment portfolio is comprised of financial instruments such as stocks, bonds, futures, options, and others. It is designed based on risk limits and return expectations of investors and managed by portfolio managers. Modern Portfolio Theory (MPT) offers a mathematical method for portfolio optimization. It defines the risk as the standard deviation of the portfolio return and provides closed-form solution for the risk optimization problem where asset allocations are derived from. The risk and the return of an investment are the two inseparable performance metrics. Therefore, risk normalized return, called Sharpe ratio, is the most widely used performance metric for financial investments. Subspace methods have been one of the pillars of functional analysis and signal processing. They are used for portfolio design, regression analysis and noise filtering in finance applications. Each subspace has its unique characteristics that may serve requirements of a specific application. For still image and video compression applications, Discrete Cosine Transform (DCT) has been successfully employed in transform coding where Karhunen-Loeve Transform (KLT) is the optimum block transform. In this dissertation, a signal processing framework to design investment portfolios is proposed. Portfolio theory and subspace methods are investigated and jointly treated. First, KLT, also known as eigenanalysis or principal component analysis (PCA) of empirical correlation matrix for a random vector process that statistically represents asset returns in a basket of instruments, is investigated. Auto-regressive, order one, AR(1) discrete process is employed to approximate such an empirical correlation matrix. Eigenvector and eigenvalue kernels of AR(1) process are utilized for closed-form expressions of Sharpe ratios and market exposures of the resulting eigenportfolios. Their performances are evaluated and compared for various statistical scenarios. Then, a novel methodology to design subband/filterbank portfolios for a given empirical correlation matrix by using the theory of optimal filter banks is proposed. It is a natural extension of the celebrated eigenportfolios. Closed-form expressions for Sharpe ratios and market exposures of subband/filterbank portfolios are derived and compared with eigenportfolios. A simple and powerful new method using the rate-distortion theory to sparse eigen-subspaces, called Sparse KLT (SKLT), is developed. The method utilizes varying size mid-tread (zero-zone) pdf-optimized (Lloyd-Max) quantizers created for each eigenvector (or for the entire eigenmatrix) of a given eigen-subspace to achieve the desired cardinality reduction. The sparsity performance comparisons demonstrate the superiority of the proposed SKLT method over the popular sparse representation algorithms reported in the literature

Object’s Optical Geometry Measurements Based on EDoF Approach

Machine vision applications are getting more popular in many manufacturing applications. Although vision techniques have many advantages there are still numerous problems related with those methods. One of the drawbacks is that when measuring or performing an inspection task the image resolution must be as high as possible. When inspecting an object of complicated geometry, with a specific lens and camera to achieve a given accuracy, the field of view, or the depth of field might be insufficient for the task. Using cameras placed on manipulators, or on moving stages leads to solving the problem, but it also causes various implementation issues. During the measurement process when the camera or the inspected object is moving, images are acquired and need to be processed separately. If the inspection task is a simple feature extraction might be sufficient. On the other hand if the image processing is more complex it might take time to process each image separately. For example when a feature is located on the border of a image, to measure it or properly assess, two or more images with the feature need to be combined. When it comes to field of view limitations, there are known methods of image stitching, and combining [1,2]. When the depth of field is narrow, for example when using fixed telecentric lenses the problem is more complex. The Extended Depth of Field (EDoF) is an approach known in microscopy imagining. It allows to stitch images taken form a range of distances that are minimum spaced. Acquiring images of the same object with differently placed depth of field reveals elements otherwise hidden (due to shallow depth of field). The methods of extracting information form a set of images taken with different depths of field is known in microscopy and wieldy used [3,4]. On the other hand using EDoF in non-microscopic inspections is not utilized because of the fact of changing the focal distance from the inspected object leads to resizing the object in the frame. The longer the focal length the higher is the compression rate of the imagining. The authors propose a method of using EDoF in macro inspections using bi-telecentric lenses and a specially designed experimental machine setup, allowing accurate focal distance changing. Also a software method is presented allowing EDoF image reconstruction using the continuous wavelet transform (CWT). Exploited method results are additionally compared with measurements performed with Keyence’s LJ-V Series in-line Profilometer for reference matters

New wavelet based space-frequency analysis methods applied to the characterisation of 3-dimensional engineering surface textures.

The aim of this work was to use resources coming from the field of signal and image processing to make progress solving real problems of surface texture characterisation. A measurement apparatus like a microscope gives a representation of a surface textures that can be seen as an image. This is actually an image representing the relief of the surface texture. From the image processing point of view, this problem takes the form of texture analysis. The introduction of the problem as one of texture analysis is presented as well as the proposed solution: a wavelet based method for texture characterisation. Actually, more than a simple wavelet transform, an entire original characterisation method is described. A new tool based on the frequency normalisation of the well-known wavelet transform has been designed for the purpose of this study and is introduced, explained and illustrated in this thesis. This tool allows the drawing of a real space-frequency map of any image and especially textured images. From this representation, which can be compared to music notation, simple parameters are calculated. They give information about texture features on several scales and can be compared to hybrid parameters commonly used in surface roughness characterisation. Finally, these parameters are used to feed a decision-making system. In order to come back to the first motivation of the study, this analysis strategy is applied to real engineered surface characterisation problems. The first application is the discrimination of surface textures, which superficially have similar characteristics according to some standard parameters. The second application is the monitoring of a grinding process. A new approach to the problem of surface texture analysis is introduced. The principle of this new approach, well known in image processing, is not to give an absolute measure of the characteristics of a surface, but to classify textures relative to each other in a space where the distance between them indicates their similarity

Wide-baseline keypoint detection and matching with wide-angle images for vision based localisation

This thesis addresses the problem of detecting and describing the same scene points in different wide-angle images taken by the same camera at different viewpoints. This is a core competency of many vision-based localisation tasks including visual odometry and visual place recognition. Wide-angle cameras have a large field of view that can exceed a full hemisphere, and the images they produce contain severe radial distortion. When compared to traditional narrow field of view perspective cameras, more accurate estimates of camera egomotion can be found using the images obtained with wide-angle cameras. The ability to accurately estimate camera egomotion is a fundamental primitive of visual odometry, and this is one of the reasons for the increased popularity in the use of wide-angle cameras for this task. Their large field of view also enables them to capture images of the same regions in a scene taken at very different viewpoints, and this makes them suited for visual place recognition. However, the ability to estimate the camera egomotion and recognise the same scene in two different images is dependent on the ability to reliably detect and describe the same scene points, or ‘keypoints’, in the images. Most algorithms used for this purpose are designed almost exclusively for perspective images. Applying algorithms designed for perspective images directly to wide-angle images is problematic as no account is made for the image distortion. The primary contribution of this thesis is the development of two novel keypoint detectors, and a method of keypoint description, designed for wide-angle images. Both reformulate the Scale- Invariant Feature Transform (SIFT) as an image processing operation on the sphere. As the image captured by any central projection wide-angle camera can be mapped to the sphere, applying these variants to an image on the sphere enables keypoints to be detected in a manner that is invariant to image distortion. Each of the variants is required to find the scale-space representation of an image on the sphere, and they differ in the approaches they used to do this. Extensive experiments using real and synthetically generated wide-angle images are used to validate the two new keypoint detectors and the method of keypoint description. The best of these two new keypoint detectors is applied to vision based localisation tasks including visual odometry and visual place recognition using outdoor wide-angle image sequences. As part of this work, the effect of keypoint coordinate selection on the accuracy of egomotion estimates using the Direct Linear Transform (DLT) is investigated, and a simple weighting scheme is proposed which attempts to account for the uncertainty of keypoint positions during detection. A word reliability metric is also developed for use within a visual ‘bag of words’ approach to place recognition