<p>Fluoroskopi är benämningen på kontinuerlig röntgengenomlysning av en patient. Eftersom patienten och även läkaren då utsätts för kontinuerlig röntgenstrålning måste strålningsdosen hållas låg, vilket leder till brusiga bilder. Det är därför önskvärt att genom bildbehandling förbättra bilderna. Bildförbättringen måste dock ske i realtid och därför kan inte konventionella metoder användas.</p><p>Detta examensarbete avser att undersöka hur ortogonala s k. derivataoperatorer kan användas för att förbättra läsbarheten av fluoroskopibilder med hjälp av brusundertryckning och kantförstärkning. Derivataoperatorer är separerbara vilket gör dem extremt beräkningsvänliga och lätta att infoga i en skalpyramid. Skalpyramiden ger möjlighet att processa strukturer och detaljer av olika storlek var för sig samtidigt som nedsamplingsmekanismen gör att denna uppdelning inte nämnvärt ökar beräkningsbördan. I den fullständiga lösningen införes också struktur-/brusseparering för att förhindra förstärkning av och undertrycka bidrag från de frekvensband där en pixel domineras av brus.</p><p>Resultaten visar att brus verkligen kan undertryckas medan kanter och linjer bevaras bra eller förstärkes om så önskas. Den riktade filtreringen gör dock att det lätt uppstår maskliknande strukturer i bruset, men detta kan undvikas med rätt parameterinställning av struktur-/brussepareringen. Förhållandet mellan riktad och icke-riktad filtrering är likaledes styrbart via en parameter som kan optimeras med hänsyn till behov och önskemål vid varje tillämpning.</p> / <p>In X-ray technology, fluoroscopy stands for continuous irradiation. For the sake of both patients and doctors the dose has to be kept low, which leads to noisy images and the question of possible enhancement by digital image processing. Since such enhancement has to be done in real-time, most conventional and available methods are unsuitable.</p><p>The purpose of this thesis is to examine how derivative operators can be used to improve fluoroscopy images in terms of noise reduction and edge enhancement. Since the derivative operators are designed as highly separable convolution kernels the image derivatives can be computed very efficiently with a scheme that is readily embedded in a scale-space pyramid. In this pyramid, structures and details of different sizes can be processed separately with optimal parameter settings. In the final solution we also discriminate between structure and noise in order to avoid amplification, even suppress contributions from frequency bands where a certain pixel position is dominated by noise.</p><p>Experimental results show that noise can indeed be suppressed while edges and lines are enhanced. Oriented filtering may induce false structures in areas where only noise is present, something that can be avoided by correcting the parameters in the noise/structure discriminator. The relation between oriented and non-oriented filtering is likewise controllable with a parameter that can be optimized for application dependent needs and desires.</p>

The consumption of wine has served as an international communication expedient for thousands of years. From classical symposiums of ancient times to religious ceremonies practiced for centuries, wine has played a significant part in countless social gatherings across the ages and continents. Recent growth in international wine trade, however, has impacted an increasingly disrupted natural environment through amplified carbon output, overuse of synthetic chemicals, topsoil erosion, and water mismanagement. Vintners, or winemakers, have been tasked by the implementation of new legal standards, as well as the urging of ecologically aware prospective consumers, to instill a winemaking process that is green, or environmentally friendly, in order to demonstrate the employment of proactive measures for the long-term sustainability of an unstable Earth. As New World wine producers, Australia commands specific attention as many vineyards in this particular geographic area are actively advancing green wine production standards. Fueled by the emergent field of environmental communication, this dissertation investigates the sustainable practices being implemented by South Eastern Australian vintners during their winemaking process to offset environmental degradation and examines the new marketing discourse communicated via wine bottle labels to construct an environmentally friendly image. Ultimately, this research compares the green product marketing strategies between organic and non-organic wineries to determine the extent to which ecological messages are being promoted on wine bottle labels as a form of environmental communication.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Enhancement methods applied on various satellite images (ASTER, ETM and RADAR SAT-1) facilitated the identification and mapping of tectonic fractures in the Zagros fold-and-thrust belt in southwest Iran. The results of the fracture analysis on these enhanced images reveal four principal fracture sets within each fold structure: (i) an axial set defined by normal faults oriented parallel to the fold axial trace, (ii) a cross-axial, extensional fracture set oriented perpendicular to the fold axial trace, (iii) and two sets of intersecting shear fractures, oriented at an acute angle to the cross-axial set. Study of the enhanced images also revealed five fracture sets along the Kazerun fault zone: (i) Riedel R- and R'-shear fracture sets, (ii) extensional T fracture set oriented at a high angle to the trace of the main Kazerun fault, (iii) oblique, synthetic P-shear fracture set, at a low angle to the trace of the main Kazerun fault, and (iv) synthetic Y-shear displacement fracture set, oriented sub-parallel to the main trace of the fault. The estimated mean azimuths of the shortening that developed the fold- and fault-related fracture systems are remarkably close, and are oriented perpendicular to the general NW-SE trend of the Zagros fold-and-thrust belt. The sampling and analysis of the fold- and fault-related fracture systems were done in a GIS environment. This study shows that an analysis of enhanced satellite images can reveal significant information on the deformation style, timing, and kinematics of the Zagros fold-and-thrust belt. This study suggests that the Zagros orogenic belt, which has mainly been forming since Miocene, due to the convergence of the Iranian and Arabian subplates, has evolved both by thin- and thick-skinned tectonics. Reconfiguration of the Precambrian basement blocks, and the ensuing slip and rotation along the Precambrian faults during the Zagros orogeny, have deformed the folds, and redistributed the fold-related fractures through rigid-body rotation.

Diss. (sammanfattning) Linköping : Linköpings universitet, 2008. / Härtill 6 uppsatser. Includes bibliographical references.

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Filtering is a fundamental operation in image science in general and in medical image science in particular. The most central applications are image enhancement, registration, segmentation and feature extraction. Even though these applications involve non-linear processing a majority of the methodologies available rely on initial estimates using linear filters. Linear filtering is a well established cornerstone of signal processing, which is reflected by the overwhelming amount of literature on finite impulse response filters and their design. Standard techniques for multidimensional filtering are computationally intense. This leads to either a long computation time or a performance loss caused by approximations made in order to increase the computational efficiency. This dissertation presents a framework for realization of efficient multidimensional filters. A weighted least squares design criterion ensures preservation of the performance and the two techniques called filter networks and sub-filter sequences significantly reduce the computational demand. A filter network is a realization of a set of filters, which are decomposed into a structure of sparse sub-filters each with a low number of coefficients. Sparsity is here a key property to reduce the number of floating point operations required for filtering. Also, the network structure is important for efficiency, since it determines how the sub-filters contribute to several output nodes, allowing reduction or elimination of redundant computations. Filter networks, which is the main contribution of this dissertation, has many potential applications. The primary target of the research presented here has been local structure analysis and image enhancement. A filter network realization for local structure analysis in 3D shows a computational gain, in terms of multiplications required, which can exceed a factor 70 compared to standard convolution. For comparison, this filter network requires approximately the same amount of multiplications per signal sample as a single 2D filter. These results are purely algorithmic and are not in conflict with the use of hardware acceleration techniques such as parallel processing or graphics processing units (GPU). To get a flavor of the computation time required, a prototype implementation which makes use of filter networks carries out image enhancement in 3D, involving the computation of 16 filter responses, at an approximate speed of 1MVoxel/s on a standard PC.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Title from first page of PDF file (viewed Feb. 15, 2008). Includes bibliographical references.