[Truncated abstract] The field of pattern recognition aims to develop algorithms and computer programs that can learn patterns from data, where learning encompasses the problems of recognition, representation, classification and prediction. Syntactic pattern recognition recognises that patterns may be hierarchically structured. Formal language theory is an example of a syntactic approach, and is used extensively in computer languages and speech processing. However, the underlying structure of language and speech is strictly one-dimensional. The application of syntactic pattern recognition to the analysis of images requires an extension of formal language theory. Thus, this thesis extends and generalises formal language theory to apply to data that have possibly multi-dimensional underlying structure and also hierarchic structure . . . As in the case for curves, shapes are modelled as a sequence of local relationships between the curves, and these are estimated using a training sample. Syntactic square detection was extremely successful – detecting 100% of squares in images containing only a single square, and over 50% of the squares in images containing ten squares highly likely to be partially or severely occluded. The detection and classification of polygons was successful, despite a tendency for occluded squares and rectangles to be confused. The algorithm also peformed well on real images containing fish. The success of the syntactic approaches for detecting edges, detecting curves and detecting, classifying and counting occluded shapes is evidence of the potential of syntactic models.

Orientador: Anderson de Rezende Rocha / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-23T03:37:08Z (GMT). No. of bitstreams: 1 Silva_EwertonAlmeida_M.pdf: 20654769 bytes, checksum: cd66fa66dedc48d34c5feb0fa0311759 (MD5) Previous issue date: 2012 / Resumo: Neste trabalho, nós investigamos duas abordagens para detecção de manipulações de Cópia-colagem (Copy-move Forgery) em imagens digitais. A primeira abordagem é baseada no algoritmo PatchMatch Generalizado [4], cuja proposta é encontrar correspondências de patches (blocos de pixels de tamanho definido) em uma ou mais imagens. A nossa abordagem consiste na aplicação do PatchMatch Generalizado em uma dada imagem com o propósito de encontrar, para cada patch desta, um conjunto de patches similares com base nas distâncias de seus histogramas. Em seguida, nós verificamos as correspondências de cada patch para decidir se eles são segmentos de uma região duplicada. A segunda abordagem, que consiste em nossa principal contribuição, é baseada em um processo de Votação e Análise Multiescala da imagem. Dada uma imagem suspeita, extraímos pontos de interesse robustos a operações de escala e rotação, encontramos correspondências entre eles e os agrupamos em regiões com base em certas restrições geométricas, tais como a distância física e a inclinação da reta que os liga. Após a aplicação das restrições geométricas, criamos uma pirâmide multiescala que representará o espaço de escalas da imagem. Nós examinamos, em cada imagem, os grupos criados usando um descritor robusto a rotações, redimensionamentos e compressões. Este processo diminui o domínio de busca de regiões duplicadas e gera um mapa de detecção para cada escala. A decisão final é dada a partir de uma votação entre todos os mapas, na qual um segmento é considerado duplicado se este assim o é na maioria das escalas. Nós validamos ambos os métodos em uma base de imagens que construímos. A base _e composta por 108 clonagens originais e com elevado grau de realismo. Comparamos os métodos propostos com outros do estado da arte nessa mesma base de imagens / Abstract: In this work, we investigate two approaches toward Copy-move Forgery detection in digital images. The first approach relies on the Generalized PatchMatch algorithm [4], which aims at finding patch correspondences in one or more images. Our approach consists in applying the Generalized PatchMatch algorithm in a certain image in order to obtain, for each of its patches, a set of similar patches based on their histogram distances. Next, we check the correspondences of each patch to decide whether or not they are portions of a duplicated region. Our second approach is based on a Voting and Multiscale Analysis process of an image. Given a suspicious image, we extract its interest points robust to scale and rotation transformations and we find possible correspondences among them. Next, we group the correspondent points into regions considering some geometric constraints, such as physical distance and inclination of the line between points of interest. After that, we construct a multiscale pyramid to represent the image scale-space. In each image, we examine the created groups using a descriptor robust to rotation, scaling and compression. This process decreases the search space of duplicated regions and yields a detection map. The final decision depends on a voting among all the detected maps, in which a pixel is considered as part of a manipulation if it is marked as so in the majority of the pyramid scales. We validate both methods using a dataset we have built comprising 108 original and realistic clonings. We compare the proposed methods to others from the state-of-the-art using such cloning dataset / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

Heart disease is the major cause of death in the developed world. Due to its fast, portable, low-cost and harmless way of imaging the heart, echocardiography has become the most frequent tool for diagnosis of cardiac function in clinical routine. However, visual assessment of heart function from echocardiography is challenging, highly operatordependant and is subject to intra- and inter observer errors. Therefore, development of automated methods for echocardiography analysis is important towards accurate assessment of cardiac function. In this thesis we develop new ways to model echocardiography data using Bayesian machine learning methods and concern three problems: (i) wall motion analysis in 2D stress echocardiography, (ii) segmentation of the myocardium in 3D echocardiography, and (iii) standard views extraction from 3D echocardiography. Firstly, we propose and compare four discriminative methods for feature extraction and wall motion classification of 2D stress echocardiography (images of the heart taken at rest and after exercise or pharmalogical stress). The four methods are based on (i) Support Vector Machines, (ii) Relevance Vector Machines, (iii) Lasso algorithm and Regularised Least Squares, (iv) Elastic Net regularisation and Regularised Least Squares. Although all the methods are shown to have superior performance to the state-of-the-art, one conclusion is that good segmentation of the myocardium in echocardiography is key for accurate assessment of cardiac wall motion. We investigate the application of one of the most promising current machine learning techniques, called Decision Random Forests, to segment the myocardium from 3D echocardiograms. We demonstrate that more reliable and ultrasound specific descriptors are needed in order to achieve the best results. Specifically, we introduce two sets of new features to improve the segmentation results: (i) LoCo and GloCo features with a local and a global shape constraint on coupled endoand epicardial boundaries, and (ii) FA features, which use the Feature Asymmetry measure to highlight step-like edges in echocardiographic images. We also reinforce the traditional features such as Haar and Rectangular features by aligning 3D echocardiograms. For that we develop a new registration technique, which is based on aligning centre lines of the left ventricles. We show that with alignment performance is boosted by approximately 15%. Finally, a novel approach to detect planes in 3D images using regression voting is proposed. To the best of our knowledge we are the first to use a one-step regression approach for the task of plane detection in 3D images. We investigate the application to standard views extraction from 3D echocardiography to facilitate efficient clinical inspection of cardiac abnormalities and diseases. We further develop a new method, called the Class- Specific Regression Forest, where class label information is incorporating into the training phase to reinforce the learning from semantically relevant to the problem classes. During testing the votes from irrelevant classes are excluded from voting to maximise the confidence of output predictors. We demonstrate that the Class-Specific Regression Random Forest outperforms the classic Regression Random Forest and produces results comparable to the manual annotations.

Light microscopes are essential research tools in biology and medicine. Cell and tissue staining methods have improved immensely over the years and microscopes are now equipped with digital image acquisition capabilities. The image data produced require development of specialized analysis methods. This thesis presents digital image analysis methods for cell image data in 2D, 3D and time sequences. Stem cells have the capability to differentiate into specific cell types. The mechanism behind differentiation can be studied by tracking cells over time. This thesis presents a combined segmentation and tracking algorithm for time sequence images of neural stem cells.The method handles splitting and merging of cells and the results are similar to those achieved by manual tracking. Methods for detecting and localizing signals from fluorescence stained biomolecules are essential when studying how they function and interact. A study of Smad proteins, that serve as transcription factors by forming complexes and enter the cell nucleus, is included in the thesis. Confocal microscopy images of cell nuclei are delineated using gradient information, and Smad complexes are localized using a novel method for 3D signal detection. Thus, the localization of Smad complexes in relation to the nuclear membrane can be analyzed. A detailed comparison between the proposed and previous methods for detection of point-source signals is presented, showing that the proposed method has better resolving power and is more robust to noise. In this thesis, it is also shown how cell confluence can be measured by classification of wavelet based texture features. Monitoring cell confluence is valuable for optimization of cell culture parameters and cell harvest. The results obtained agree with visual observations and provide an efficient approach to monitor cell confluence and detect necrosis. Quantitative measurements on cells are important in both cytology and histology. The color provided by Pap (Papanicolaou) staining increases the available image information. The thesis explores different color spaces of Pap smear images from thyroid nodules, with the aim of finding the representation that maximizes detection of malignancies using color information in addition to quantitative morphological parameters. The presented methods provide useful tools for cell image analysis, but they can of course also be used for other image analysis applications.

Medical image analysis aims at developing techniques to extract information from medical images. Among its many sub-fields, image registration and segmentation are two important topics. In this report, we present four pieces of work, addressing different problems as well as coupling them into a unified framework of shape based image segmentation. Specifically: 1. We link the image registration with the point set registration, and propose a globally optimal diffeomorphic registration technique for point set registration. 2. We propose an image segmentation technique which incorporates the robust statistics of the image and the multiple contour evolution. Therefore, the method is able to simultaneously extract multiple targets from the image. 3. By combining the image registration, statistical learning, and image segmentation, we perform a shape based method which not only utilizes the image information but also the shape knowledge. 4. A multi-scale shape representation based on the wavelet transformation is proposed. In particular, the shape is represented by wavelet coefficients in a hierarchical way in order to decompose the shape variance in multiple scales. Furthermore, the statistical shape learning and shape based segmentation is performed under such multi-scale shape representation framework.

Image classification is a fundamental information extraction procedure in remote sensing that is used in land-cover and land-use mapping. Despite being considered as a replacement for manual mapping, it still requires some degree of analyst intervention. This makes the process of image classification time consuming, subjective, and error prone. For example, in unsupervised classification, pixels are automatically grouped into classes, but the user has to manually label the classes as one land-cover type or another. As a general rule, the larger the number of classes, the more difficult it is to assign meaningful class labels. A fully automated post-classification procedure for class labeling was developed in an attempt to alleviate this problem. It labels spectral classes by matching their spectral characteristics with reference spectra. A Landsat TM image of an agricultural area was used for performance assessment. The algorithm was used to label a 20- and 100-class image generated by the ISODATA classifier. The 20-class image was used to compare the technique with the traditional manual labeling of classes, and the 100-class image was used to compare it with the Spectral Angle Mapper and Maximum Likelihood classifiers. The proposed technique produced a map that had an overall accuracy of 51%, outperforming the manual labeling (40% to 45% accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39%), but underperformed compared to the Maximum Likelihood technique (53% to 63%). The newly developed class-labeling algorithm provided better results for alfalfa, beans, corn, grass and sugar beet, whereas canola, corn, fallow, flax, potato, and wheat were identified with similar or lower accuracy, depending on the classifier it was compared with. / vii, 93 leaves : ill., maps (some col.) ; 29 cm

No description available.

Development of new asphalt technologies to reduce both energy consumption and CO2 production has attracted great interest in recent years. The use of foam bitumen, as one of them, is attractive due to the low investment and production cost. Formation and decay of foam bitumen is a highly dynamic temperature dependent process which makes characterization difficult. In this thesis, new experimental tools were developed and applied for characterizing the foam bitumen during the hot foaming process.  One of the main goals of this study was to improve understanding and characterization of the foam bitumen formation and decay. X-ray radiography was used to study the formation and decay of foam bitumen in 2D representation. The results demonstrate that the morphology of bubble formation depends on the types of bitumen used. Moreover, theoretical investigation based on the 3D X-ray computed tomography scan dataset of bubble merging showed that the disjoining pressure increased as the gap between the bubbles in the surface layer (foam film) decreased with time and finally was ruptured.   Examining the foam bitumen stream right at the nozzle revealed that foam bitumen at a very early stage contains fragmented pieces of irregular size rather resembling a liquid than foam. The result from thermogravimetric analysis demonstrated that residual water content depends on the initial water content, and was found to be between 38 wt% and 48 wt% of the initial water content of 4 wt% to 6 wt%. Moreover the influence of viscosity and surface tension on bubble shape and rise velocity of the bubbles using level-set method was implemented in finite element method. The modeling results were compared with bubble shape correlation map from literature. The results indicated that the bubble shapes are more dependent on the surface tension parameters than to the viscosity of the bitumen, whereas the bitumen viscosity is dominant for bubble rising velocity. / <p>QC 20160303</p>

The swelling and caking propensity of coals may cause operational problems such as channelling and excessive pressure build-up in combustion, gasification and specifically in fluidised-bed and fixed-bed operations. As a result, the swelling and caking characteristics of certain coals make them less suitable for use as feedstock in applications where swelling and/or caking is undesired. Therefore, various studies have focused on the manipulation of the swelling and/or caking propensity of coals, and have proven the viability of using additives to reduce the swelling and caking of powdered coal (<500 μm). However, there is still a lack of research specifically focused on large coal particle devolatilisation behaviour, particularly swelling and caking, and the reduction thereof using additives. A comprehensive study was therefore proposed to investigate the swelling and caking behaviour of large coal particles (5, 10, and 20 mm) of typical South African coals, and the influence of the selected additive (potassium carbonate) thereon. Three different South African coals were selected based on their Free Swelling Index (FSI): coal TSH is a high swelling coal (FSI 9) from the Limpopo province, GG is a medium swelling coal (FSI 5.5-6.5) from the Waterberg region, and TWD is a non-swelling coal (FSI 0) from the Highveld region. Image analysis was used to semi-quantitatively describe the transient swelling and shrinkage behaviour of large coal particles (-20+16 mm) during lowtemperature devolatilisation (700 °C, N2 atmosphere, 7 K/min). X-ray computed tomography and mercury submersion were used to quantify the degree of swelling of large particles, and were compared to conventional swelling characteristics of powdered coals. The average swelling ratios obtained for TWD, GG, and TSH were respectively 1.9, 2.1 and 2.5 from image analysis and 1.8, 2.2 and 2.5 from mercury submersion. The results showed that coal swelling measurements such as FSI, and other conventional techniques used to describe the plastic behaviour of powdered coal, can in general not be used for the prediction of large coal particle swelling. The large coal particles were impregnated for 24 hours, using an excess 5.0 M K2CO3 impregnation solution. The influence of K2CO3-addition on the swelling behaviour of different coal particle sizes was compared, and results showed that the addition of K2CO3 resulted in a reduction in swelling for powdered coal (-212 μm), as well as large coal particles (5, 10, and 20 mm). For powdered coal, the addition of 10 wt.% K2CO3 decreased the free swelling index of GG and TSH coals from 6.5 to 0 and from 9.0 to 4.5, respectively. The volumetric swelling ratios (SRV) of the 20 mm particles were reduced from 3.0 to 1.8 for the GG coal, and from 5.7 to 1.4 for TSH. In contrast to the non-swelling (FSI 0) behaviour of the TWD powders, the large particles exhibited average SRV values of 1.7, and was found not be influenced by K2CO3-impregnation. It was found that the maximum swelling coefficient, kA, was reduced from 0.025 to 0.015 oC-1 for GG, and from 0.045 to 0.027 oC-1 for TSH, as a results of impregnation. From the results it was concluded that K2CO3-impregnation reduces the extent of swelling of coals such as GG (medium-swelling) and TSH (high-swelling), which exhibit significant plastic deformation. Results obtained from the caking experiments indicated that K2CO3-impregnation influenced the physical behaviour of the GG coal particles (5, 10, and 20 mm) the most. The extent of caking of GG was largely reduced due to impregnation, while the wall thickness and porosity also decreased. The coke from the impregnated GG samples had a less fluid-like appearance compared to coke from the raw coal. Bridging neck size measurements were performed, which quantitatively showed a 25-50% decrease in the caking propensity of GG particles. Coal TWD did not exhibit any caking behaviour. The K2CO3-impregnation did not influence the surface texture or porosity of the TWD char, but increased the overall brittleness of the devolatilised samples. Both the extent of caking and porosity of TSH coke were not influenced by impregnation. However, impregnation resulted in significantly less and smaller opened pores on the surface of the devolatilised samples, and also reduced the average wall thickness of the TSH coke. The overall conclusion made from this investigation is that K2CO3 (using solution impregnation) can be used to significantly reduce the caking and swelling tendency of large coal particles which exhibits a moderate degree of fluidity, such as GG (Waterberg region). The results obtained during this investigation show the viability of using additive addition to reduce the caking and swelling tendency of large coal particles. Together with further development, this may be a suitable method for modifying the swelling and caking behaviour of specific coals for use in fixed-bed and fluidised-bed gasification operations. / PhD (Chemical Engineering), North-West University, Potchefstroom Campus, 2015

The swelling and caking propensity of coals may cause operational problems such as channelling and excessive pressure build-up in combustion, gasification and specifically in fluidised-bed and fixed-bed operations. As a result, the swelling and caking characteristics of certain coals make them less suitable for use as feedstock in applications where swelling and/or caking is undesired. Therefore, various studies have focused on the manipulation of the swelling and/or caking propensity of coals, and have proven the viability of using additives to reduce the swelling and caking of powdered coal (<500 μm). However, there is still a lack of research specifically focused on large coal particle devolatilisation behaviour, particularly swelling and caking, and the reduction thereof using additives. A comprehensive study was therefore proposed to investigate the swelling and caking behaviour of large coal particles (5, 10, and 20 mm) of typical South African coals, and the influence of the selected additive (potassium carbonate) thereon. Three different South African coals were selected based on their Free Swelling Index (FSI): coal TSH is a high swelling coal (FSI 9) from the Limpopo province, GG is a medium swelling coal (FSI 5.5-6.5) from the Waterberg region, and TWD is a non-swelling coal (FSI 0) from the Highveld region. Image analysis was used to semi-quantitatively describe the transient swelling and shrinkage behaviour of large coal particles (-20+16 mm) during lowtemperature devolatilisation (700 °C, N2 atmosphere, 7 K/min). X-ray computed tomography and mercury submersion were used to quantify the degree of swelling of large particles, and were compared to conventional swelling characteristics of powdered coals. The average swelling ratios obtained for TWD, GG, and TSH were respectively 1.9, 2.1 and 2.5 from image analysis and 1.8, 2.2 and 2.5 from mercury submersion. The results showed that coal swelling measurements such as FSI, and other conventional techniques used to describe the plastic behaviour of powdered coal, can in general not be used for the prediction of large coal particle swelling. The large coal particles were impregnated for 24 hours, using an excess 5.0 M K2CO3 impregnation solution. The influence of K2CO3-addition on the swelling behaviour of different coal particle sizes was compared, and results showed that the addition of K2CO3 resulted in a reduction in swelling for powdered coal (-212 μm), as well as large coal particles (5, 10, and 20 mm). For powdered coal, the addition of 10 wt.% K2CO3 decreased the free swelling index of GG and TSH coals from 6.5 to 0 and from 9.0 to 4.5, respectively. The volumetric swelling ratios (SRV) of the 20 mm particles were reduced from 3.0 to 1.8 for the GG coal, and from 5.7 to 1.4 for TSH. In contrast to the non-swelling (FSI 0) behaviour of the TWD powders, the large particles exhibited average SRV values of 1.7, and was found not be influenced by K2CO3-impregnation. It was found that the maximum swelling coefficient, kA, was reduced from 0.025 to 0.015 oC-1 for GG, and from 0.045 to 0.027 oC-1 for TSH, as a results of impregnation. From the results it was concluded that K2CO3-impregnation reduces the extent of swelling of coals such as GG (medium-swelling) and TSH (high-swelling), which exhibit significant plastic deformation. Results obtained from the caking experiments indicated that K2CO3-impregnation influenced the physical behaviour of the GG coal particles (5, 10, and 20 mm) the most. The extent of caking of GG was largely reduced due to impregnation, while the wall thickness and porosity also decreased. The coke from the impregnated GG samples had a less fluid-like appearance compared to coke from the raw coal. Bridging neck size measurements were performed, which quantitatively showed a 25-50% decrease in the caking propensity of GG particles. Coal TWD did not exhibit any caking behaviour. The K2CO3-impregnation did not influence the surface texture or porosity of the TWD char, but increased the overall brittleness of the devolatilised samples. Both the extent of caking and porosity of TSH coke were not influenced by impregnation. However, impregnation resulted in significantly less and smaller opened pores on the surface of the devolatilised samples, and also reduced the average wall thickness of the TSH coke. The overall conclusion made from this investigation is that K2CO3 (using solution impregnation) can be used to significantly reduce the caking and swelling tendency of large coal particles which exhibits a moderate degree of fluidity, such as GG (Waterberg region). The results obtained during this investigation show the viability of using additive addition to reduce the caking and swelling tendency of large coal particles. Together with further development, this may be a suitable method for modifying the swelling and caking behaviour of specific coals for use in fixed-bed and fluidised-bed gasification operations. / PhD (Chemical Engineering), North-West University, Potchefstroom Campus, 2015