Beef jerky is a dried meat snack which is rich in protein but of low calorific value. This ready-to- eat meat snack is in high demand among hikers, bikers and travelers due to its compact nature and nutritional value. The current processing methods such as smoke house and home dehydrators take 6-10 h. Increasing market for this shelf-stable meat product increases the need for alternate efficient processing method. Also, this meat snack market depends on its textural characteristics which denote the consumer acceptability. In this research, three different methods of drying beef jerky were examined. Influences of pH and salt on different characteristics of beef jerky were investigated using combined microwave-convection drying. Also, the effects of relative humidity and airflow rates in forced air thin layer drying on jerky processing were studied. Samples of beef jerky dried using a combined microwave-convection drier and thin layer drying unit were compared with samples dried in a smoke house. The results obtained showed that pH and salt content had a significant influence on drying, physical and textural characteristics of jerky. It was found that samples with low pH (5.15) and high salt content (3.28% (w/w)) dried faster than samples with high pH and low salt content due to their high drying rates. These samples had shown high shrinkage and weight loss compared to samples with pH 5.65 and 1.28% (w/w) salt content. Analysis of the textural characteristics such as tensile force, puncture force and texture profile showed that the samples with high pH and low salt content were comparably softer than the rest of the samples. Results of the effect of relative humidity and airflow rate in forced air thin layer drying on jerky processing showed that relative humidity and airflow rate influenced the drying, physical, chemical and textural characteristics of beef jerky. Combination of low relative humidity and high airflow rate showed desirable drying characteristics. However, samples dried at this combination showed high shrinkage and weight loss. The hardness of the beef jerky increased with increase in airflow rate and reduction in relative humidity. A comparison of the drying methods revealed that different drying methods produced different desirable properties. Combined microwave-convection drying was found to be efficient and very rapid (8.25 min). The low shrinkage and weight losses along with high drying rate obtained using this method would pave a way to fast and efficient processing. The color and textural characteristics were different from those of samples dried in a smoke house. Surprisingly, combined microwave-convection drying method produced softer beef jerky than thin layer and smokehouse methods. However, the commercially available jerky is tougher than the one dried using combined microwave-convection drying. The samples dried in a thin layer drier had comparable color and textural characteristics with samples dried in a smoke-house. Also, forced-air thin layer drying method reduced drying time of beef jerky from 7 to 3 h. The forced air thin layer drier has the potential to produce beef jerky with similar color and textural characteristics to commercially available smoke house dried samples.

Ovarian follicles in women are fluid-filled structures in the ovary that contain oocytes (eggs). A dominant follicle is physiologically selected and ovulates during the menstrual cycle. We examined the echotexture in ultrasonographic images of the follicle wall of dominant ovulatory follicles in women during natural menstrual cycles and dominant anovulatory follicles which developed in women using oral contraceptives (OC). Texture features of follicle wall regions of both ovulatory and anovulatory dominant follicles were evaluated over a period of seven days before ovulation (natural cycles) or peak estradiol concentrations (OC cycles). Differences in echotexture between the two classes of follicles were found for two co-occurrence matrix derived texture features and two edge-frequency based texture features. Co-occurrence energy and homogeneity were significantly lower for ovulatory follicles while edge density and edge contrast were higher for ovulatory follicles. In the each feature space, the two classes of follicle were adequately separable.</p><p>This thesis employed several statistical approaches to analyses of texture features, such as plotting method and the Mann-Kendall method. A distinct change of feature trend was detected 3 or 4 days before the day of ovulation for ovulatory follicles in the two co-occurrence matrix derived texture features and two edge-frequency-based texture features. Anovulatory follicles, exhibited the biggest variation of the feature value 3 or 4 days before the day on which dominant follicles developed to maximum size. This discovery is believed to correspond to the ovarian follicles responding to system hormonal changes leading to presumptive ovulation.</p>

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

Melanoma is the deadliest form of skin cancer if left untreated. Incidence rates of melanoma have been increasing, especially among young adults, but survival rates are high if detected early. Unfortunately, the time and costs required for dermatologists to screen all patients for melanoma are prohibitively expensive. There is a need for an automated system to assess a patient's risk of melanoma using photographs of their skin lesions. Dermatologists could use the system to aid their diagnosis without the need for special or expensive equipment. One challenge in implementing such a system is locating the skin lesion in the digital image. Most existing skin lesion segmentation algorithms are designed for images taken using a special instrument called the dermatoscope. The presence of illumination variation in digital images such as shadows complicates the task of finding the lesion. The goal of this research is to develop a framework to automatically correct and segment the skin lesion from an input photograph. The first part of the research is to model illumination variation using a proposed multi-stage illumination modeling algorithm and then using that model to correct the original photograph. Second, a set of representative texture distributions are learned from the corrected photograph and a texture distinctiveness metric is calculated for each distribution. Finally, a texture-based segmentation algorithm classifies regions in the photograph as normal skin or lesion based on the occurrence of representative texture distributions. The resulting segmentation can be used as an input to separate feature extraction and melanoma classification algorithms. The proposed segmentation framework is tested by comparing lesion segmentation results and melanoma classification results to results using other state-of-the-art algorithms. The proposed framework has better segmentation accuracy compared to all other tested algorithms. The segmentation results produced by the tested algorithms are used to train an existing classification algorithm to identify lesions as melanoma or non-melanoma. Using the proposed framework produces the highest classification accuracy and is tied for the highest sensitivity and specificity.

El análisis de texturas es un área de estudio interesante con suficiente peso específico dentro de los diferentes campos que componen la visión por ordenador. En este trabajo hemos desarrollado métodos específicos para resolver aspectos importantes de dicha área. El primer acercamiento al tema viene de la mano de un problema de segmentación de un tipo de texturas muy concreto como son las imágenes microscópicas de láminas de mármol. Este primer tipo de imágenes se componen de un conjunto de granos cuyas formas y tamaños sirven a los especialistas para identificar, catalogar y determinar el origen de dichas muestras. Identificar y analizar los granos que componen tales imágenes de manera individual necesita de una etapa de segmentación. En esencia, esto implica la localización de las fronteras representadas en este caso por valles que separan zonas planas asociadas a los granos. De los diferentes métodos estudiados para la detección de dichos valles y para el caso concreto de imágenes petrográficas son los basados en técnicas de morfología matemática los que han dado mejores resultados. Además, la segmentación requiere un filtrado previo para el que se han estudiado nuevamente un conjunto de posibilidades entre las que cabe destacar los algoritmos multirresolución basados en wavelets.El segundo problema que hemos atacado en este trabajo es la clasificación de imágenes de textura. En él también hemos utilizado técnicas multirresolución como base para su resolución. A diferencia de otros enfoques de carácter global que encontramos extensamente en la literatura sobre texturas, nos hemos centrado en problemas donde las diferencias visuales entre las clases de dichas texturas son muy pequeñas. Y puesto que no hemos establecido restricciones fuertes en este análisis, las estrategias desarrolladas son aplicables a un extenso espectro de texturas, como pueden ser las baldosas cerámicas, las imágenes microscópicas de pigmentos de efecto, etc.El enfoque que hemos seguido para la clasificación de texturas implica la consecución de una serie de pasos. Hemos centrado nuestra atención en aquellos pasos asociados con las primeras etapas del proceso requeridas para identificar las características importantes que definen la textura, mientras que la clasificación final de las muestras ha sido realizada mediante métodos de clasificación generales. Para abordar estos primeros pasos dentro del análisis hemos desarrollado una estrategia mediante la cual las características de una imagen se ajustan a un modelo que previamente hemos definido, uno de entre varios modelos que están ordenados por complejidad. Estos modelos están asociados a algoritmos específicos y sus parámetros así como a los cálculos que de ellos se derivan. Eligiendo el modelo adecuado, por tanto, evitamos realizar cálculos que no nos aportan información útil para la clasificación.En un tercer enfoque hemos querido llegar a una descripción de textura que nos permita de forma sencilla su clasificación y su síntesis. Para conseguir este objetivo hemos adoptado por un modelo probabilístico. Dicha descripción de la textura nos permitirá la clasificación a través de la comparación directa de modelos, y también podremos, a partir del modelo probabilístico, sintetizar nuevas imágenes.Para finalizar, comentar que en las dos líneas de trabajo que hemos expuesto, la segmentación y la clasificación de texturas, hemos llegado a soluciones prácticas que han sido evaluadas sobre problemas reales con éxito y además las metodologías propuestas permiten una fácil extensión o adaptación a nuevos casos. Como líneas futuras asociadas a estos temas trataremos por un lado de adaptar la segmentación a imágenes que poco o nada tienen que ver con las texturas, en las que se perseguirá la detección de sujetos y objetos dentro de escenas, como apuntamos más adelante en esta misma memoria. Por otro lado, y relacionado con la clasificación, abordaremos un problema todavía sin solución como es el de la ingeniería inversa en pigmentos de efecto, en otras palabras la determinación de los constituyentes en pinturas metalizadas, y en el que utilizaremos los estudios aquí presentados como base para llegar a una posible solución. / An interesting problem in computer vision is the analysis of texture images. In this work, we have developed specific methods to solve important aspects of this problem. The first approach involves segmentation of a specific type of textures, i.e. those of microscopy images of thin marble sections. These images comprise a pattern of grains whose sizes and shapes help specialists to identify the origin and quality of marble samples. To identify and analyze individual grains in these images represents a problem of image segmentation. In essence, this involves identifying boundary lines represented by valleys which separate flat areas corresponding to grains. Of several methods tested, we found those based on mathematical morphology particularly successful for segmentation of petrographical images. This involves a pre-filtering step for which again several approaches have been explored, including multiresolution algorithms based on wavelets. In the second approach we have also used multiresolution analyses to address the problem of classifying texture images. In contrast to more global approaches found in the literature, we have explored situations where visual differences between textures are rather subtle. Since we have tried to impose relatively few restrictions on these analyses, we have developed strategies that are applicable to a wide range of related texture images, such as images of ceramic tiles, microscopic images of effect pigments, etc.The approach we have used for the classification of texture images involves several technical steps. We have focused our attention in the initial low-level analyses required to identify the general features of the image, whereas the final classification of samples has been performed using generic classification methods. To address the early steps of image analysis, we have developed a strategy whereby the general features of the image fit one of several pre-defined models with increasing levels of complexity. These models are associated to specific algorithms, parameters and calculations for the analysis of the image, thus avoiding calculations that do not provide useful information. Finally, in a third approach we want to arrive to a description of textures in such a way that it should be able to classify and synthesize textures. To reach this goal we adopt a probabilistic model of the texture. This description of the texture allows us to compare textures through comparison of probabilistic models, and also use those probabilities to generate new similar images.In conclusion, we have developed strategies of segmentation and classification of textures that provide solutions to practical problems and are potentially applicable with minor modifications to a wide range of situations. Future research will explore (i) the possibility of adapting segmentation to the analysis of images that do not necessarily involve textures, e.g. localization of subjects in scenes, and (ii) classification of effect pigment images to help identify their components.

Global illumination algorithms are used to render photorealistic images of 3D scenes taking into account both direct lighting from the light source and light reflected from other surfaces in the scene. Algorithms based on computing radiosity were among the first to be used to calculate indirect lighting, although they make assumptions that work only for diffusely reflecting surfaces. The classic radiosity approach divides a scene into multiple patches and generates a linear system of equations which, when solved, gives the values for the radiosity leaving each patch. This process can require extensive calculations and is therefore very slow. An alternative to solving a large system of equations is to use a Monte Carlo method of random sampling. In this approach, a large number of rays are shot from each patch into its surroundings and the irradiance values obtained from these rays are averaged to obtain a close approximation to the real value. <br /><br /> This thesis proposes the use of a Monte Carlo method to generate radiosity texture maps on graphics hardware. By storing the radiosity values in textures, they are immediately available for rendering, making this algorithm useful for interactive implementations. We have built a framework to run this algorithm and using current graphics cards (NV6800 or higher) it is possible to execute it almost interactively for simple scenes and within relatively low times for more complex scenes.

Ovarian follicles in women are fluid-filled structures in the ovary that contain oocytes (eggs). A dominant follicle is physiologically selected and ovulates during the menstrual cycle. We examined the echotexture in ultrasonographic images of the follicle wall of dominant ovulatory follicles in women during natural menstrual cycles and dominant anovulatory follicles which developed in women using oral contraceptives (OC). Texture features of follicle wall regions of both ovulatory and anovulatory dominant follicles were evaluated over a period of seven days before ovulation (natural cycles) or peak estradiol concentrations (OC cycles). Differences in echotexture between the two classes of follicles were found for two co-occurrence matrix derived texture features and two edge-frequency based texture features. Co-occurrence energy and homogeneity were significantly lower for ovulatory follicles while edge density and edge contrast were higher for ovulatory follicles. In the each feature space, the two classes of follicle were adequately separable.</p><p>This thesis employed several statistical approaches to analyses of texture features, such as plotting method and the Mann-Kendall method. A distinct change of feature trend was detected 3 or 4 days before the day of ovulation for ovulatory follicles in the two co-occurrence matrix derived texture features and two edge-frequency-based texture features. Anovulatory follicles, exhibited the biggest variation of the feature value 3 or 4 days before the day on which dominant follicles developed to maximum size. This discovery is believed to correspond to the ovarian follicles responding to system hormonal changes leading to presumptive ovulation.</p>

Beef jerky is a dried meat snack which is rich in protein but of low calorific value. This ready-to- eat meat snack is in high demand among hikers, bikers and travelers due to its compact nature and nutritional value. The current processing methods such as smoke house and home dehydrators take 6-10 h. Increasing market for this shelf-stable meat product increases the need for alternate efficient processing method. Also, this meat snack market depends on its textural characteristics which denote the consumer acceptability. In this research, three different methods of drying beef jerky were examined. Influences of pH and salt on different characteristics of beef jerky were investigated using combined microwave-convection drying. Also, the effects of relative humidity and airflow rates in forced air thin layer drying on jerky processing were studied. Samples of beef jerky dried using a combined microwave-convection drier and thin layer drying unit were compared with samples dried in a smoke house. The results obtained showed that pH and salt content had a significant influence on drying, physical and textural characteristics of jerky. It was found that samples with low pH (5.15) and high salt content (3.28% (w/w)) dried faster than samples with high pH and low salt content due to their high drying rates. These samples had shown high shrinkage and weight loss compared to samples with pH 5.65 and 1.28% (w/w) salt content. Analysis of the textural characteristics such as tensile force, puncture force and texture profile showed that the samples with high pH and low salt content were comparably softer than the rest of the samples. Results of the effect of relative humidity and airflow rate in forced air thin layer drying on jerky processing showed that relative humidity and airflow rate influenced the drying, physical, chemical and textural characteristics of beef jerky. Combination of low relative humidity and high airflow rate showed desirable drying characteristics. However, samples dried at this combination showed high shrinkage and weight loss. The hardness of the beef jerky increased with increase in airflow rate and reduction in relative humidity. A comparison of the drying methods revealed that different drying methods produced different desirable properties. Combined microwave-convection drying was found to be efficient and very rapid (8.25 min). The low shrinkage and weight losses along with high drying rate obtained using this method would pave a way to fast and efficient processing. The color and textural characteristics were different from those of samples dried in a smoke house. Surprisingly, combined microwave-convection drying method produced softer beef jerky than thin layer and smokehouse methods. However, the commercially available jerky is tougher than the one dried using combined microwave-convection drying. The samples dried in a thin layer drier had comparable color and textural characteristics with samples dried in a smoke-house. Also, forced-air thin layer drying method reduced drying time of beef jerky from 7 to 3 h. The forced air thin layer drier has the potential to produce beef jerky with similar color and textural characteristics to commercially available smoke house dried samples.

Cast multi-crystalline silicon substrates are used in more than 50% of the photovoltaic modules produced today. The random grain orientations of multi-crystalline silicon wafers inhibit the formation of an effective surface texturing using conventional techniques. The other main substrate used is single crystalline Czochralski wafers (~30% of the market share). Czochralski silicon material is known to suffer from the formation of a metastable defect under carrier injection, sometimes referred to as light induced degradation (LID). Light induced degradation in low-cost photovoltaic grade silicon is studied. Trap formation is shown to occur at temperatures above 200oC. Efficiency degradation reduced from 0.75% to 0.24% when the cell thickness was reduced from 378 to 157m. The presence of light induced degradation in ribbon silicon solar cells is documented for the first time in this thesis. Trap generation and annihilation are observed in high lifetime regions of multi-crystalline silicon samples. No degradation was observed over a 24-hour period at 25oC in high efficiency ribbon solar cells (>16%), but at an elevated temperature of ~75oC, appreciable efficiency degradation was observed. Czochralski silicon solar cells showed full degradation within 24 hours at 25o C. Part two of this thesis involves the development of a surface texturing suitable for all crystalline silicon. Only 6 to 10 seconds in a 200:1 HF to HNO3 solution at room temperature allows for the formation of an effective porous silicon anti-reflection coating. This resulted in a porous silicon anti-reflection coated solar cell efficiency of 15.3% on a float zone Si sample with an excellent fill factor (78.7%). The typical process used in the literature involves porous silicon etching as the final step in the solar cell fabrication sequence. The major problem associated with this process sequence is fill factor degradation. This problem was overcome in this research by porous silicon etching prior to cell processing. It is shown that incorporating an acid texture prior to porous silicon etching can improve the surface reflectance for cast multi-crystalline and Czochralski silicon samples. Solar cell efficiencies of 14.8% for Cz Si and 13.6% for cast mc-Si were achieved.