Gaussian Process Modeling for Upsampling Algorithms With Applications in Computer Vision and Computational Fluid Dynamics

Across a variety of fields, interpolation algorithms have been used to upsample lowresolution or coarse data fields. In this work, novel Gaussian Process based methodsare employed to solve a variety of upsampling problems. Specifically threeapplications are explored: coarse data prolongation in Adaptive Mesh Refinement(AMR) in the field of Computational Fluid Dynamics, accurate document imageupsampling to enhance Optical Character Recognition (OCR) accuracy, and fastand accurate Single Image Super Resolution (SISR). For AMR, a new, efficient,and “3rd order accurate” algorithm called GP-AMR is presented. Next, a novel,non-zero mean, windowed GP model is generated to upsample low resolution documentimages to generate a higher OCR accuracy, when compared to the industrystandard. Finally, a hybrid GP convolutional neural network algorithm is used togenerate a computationally efficient and high quality SISR model

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Segmentation-Free Thinning and Enhancement of Grayscale Images by Shock Filter and Diffusion Fields

In the scope of gray-level image processing and understanding, thinning is certainly a central shape descriptor for image analysis and pattern recognition. Enhancement is also an essential tool in facilitating the visual interpretation and understanding of images, especially for noisy and blurry ones. The lack of general unified frameworks necessitates the investigation of these problems in a coherent fashion, using partial differential equations. In this paper, we present a method for thinning and enhancing images by using a shock filter derived from our previously work introduced on enhancement. This new filter incorporates specific diffusion fields and since each such field is characteristic of a given application, it brings a new degree of freedom to the shock filters, in order to address problems of greater practical interests. Probative results on handwritten documents illustrate the performance and efficiency of our model. Other applications have been added in order to highlight its efficiency.L’amincissement est assurément un descripteur de forme majeur pour l’analyse d’image et la reconnaissance de forme. Le rehaussement est aussi un outil essentiel pour faciliter l’interprétation visuelle et la compréhension des images de documents notamment celles qui sont bruitées et floues. Nous décrivons dans cet article une méthode d’amincissement et de rehaussement utilisant un filtre de chocs dérivant de celui introduit par Remaki et Cheriet pour le rehaussement. Ce nouveau filtre utilise un champ de diffusion spécifique initial. L’utilisation de tels champs apporte un nouveau degré de liberté aux filtres de chocs, puisque ceux-ci sont spécifiques aux applications (amincissement, rehaussement) et permettent ainsi au même filtre d’être utilisé pour différentes applications. Nous illustrons la performance de notre méthode par des résultats probants obtenus sur des images manuscrites

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

SCHLIEREN SEQUENCE ANALYSIS USING COMPUTER VISION

Computer vision-based methods are proposed for extraction and measurement of flow structures of interest in schlieren video. As schlieren data has increased with faster frame rates, we are faced with thousands of images to analyze. This presents an opportunity to study global flow structures over time that may not be evident from surface measurements. A degree of automation is desirable to extract flow structures and features to give information on their behavior through the sequence. Using an interdisciplinary approach, the analysis of large schlieren data is recast as a computer vision problem. The double-cone schlieren sequence is used as a testbed for the methodology; it is unique in that it contains 5,000 images, complex phenomena, and is feature rich. Oblique structures such as shock waves and shear layers are common in schlieren images. A vision-based methodology is used to provide an estimate of oblique structure angles through the unsteady sequence. The methodology has been applied to a complex flowfield with multiple shocks. A converged detection success rate between 94% and 97% for these structures is obtained. The modified curvature scale space is used to define features at salient points on shock contours. A challenge in developing methods for feature extraction in schlieren images is the reconciliation of existing techniques with features of interest to an aerodynamicist. Domain-specific knowledge of physics must therefore be incorporated into the definition and detec- tion phases. Known location and physically possible structure representations form a knowledge base that provides a unique feature definition and extraction. Model tip location and the motion of a shock intersection across several thousand frames are identified, localized, and tracked. Images are parsed into physically meaningful labels using segmentation. Using this representation, it is shown that in the double-cone flowfield, the dominant unsteady motion is associated with large scale random events within the aft-cone bow shock. Small scale organized motion is associated with the shock-separated flow on the fore-cone surface. We show that computer vision is a natural and useful extension to the evaluation of schlieren data, and that segmentation has the potential to permit new large scale measurements of flow motion

Skin Hydration and Solvent Penetration Measurements by Opto-thermal Radiometry, AquaFlux and Fingerprint Sensor

The aim of this study is to develop new data analysis techniques and new measurement methodologies for skin hydration and solvent penetration measurements by using Opto-Thermal Transient Emission Radiometry (OTTER), AquaFlux and capacitive contact imaging based on Fingerprint sensor, three novel technologies developed by our research group. This research work is divided into three aspects: the theoretical work, the experimental work and the portable opto-thermal radiometry hardware design work. In the theoretical work, a) an effective image retrieval method based on Gabor wavelet transform has been developed, the results show that it is particularly useful for retrieving the grayscale capacitive skin images; b) an algorithm based on Grey Level Co-occurrence Matrix (GLCM) has been developed to analyze the grayscale capacitive skin images; c) a comparison study of Gabor wavelet transform, Grey level co-occurrence matrix (GLCM) and Principal Component Analysis (PCA) has been conducted in order to understand the performance of each algorithm, and to find out which algorithm is suitable for what type of images. In the opto-thermal radiometry hardware design work, a new, low cost, portable opto-thermal radiometry instrument, based on a broadband Infrared emitter and a room temperature PbS detector, has been designed and developed. The results show that it can work on any unprepared sample surfaces. In the experimental work, various in-vivo and in-vitro measurements were performed in order to study skin hydration and solvent penetration through skin and membranes. The results show that, combined with tape stripping, capacitive skin imaging can be a powerful tool for skin hydration, skin texture and solvent penetration measurements. The effect of three different parameters of Fingerprint sensor and its detection depth are also studied. The outcomes of this work have provided a better understanding for skin hydration and solvent penetration measurements and have generated several publications