Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2017-05-11T19:49:56Z No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-05-15T11:30:14Z (GMT) No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-05-15T11:30:14Z (GMT). No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-04-13 / Contextualization: Technologies like CAD systems, become ideal as a second opinion, that is, to work in partnership with the doctor. For example, PneumoCAD can be used to perform a diagnosis of absence or absence of pneumonia in children 1 to <5 years of age using X-ray radiographs. Problems: However, the use of PneumoCAD requires a region of interest, referring to the lungs, manually. Based on difficulty and difficulty, we sought a solution that was not found in any research, applied to radiographs, involving PneumoCAD, through the active model, having as a patient children between the ages of 1 and <5 years. Proposal: It is then proposed the use of the active model, associated with the technique developed and called nsAlterar in improvement to segmentation based on ns . Materials and Methods: Fifty-six “padrão ouro” radiographs were submitted to MATLAB, in 8 steps, through modified and improved algorithms, as well as implemented support tools, such as: As well as measures of similarity to investigate quantitatively , On an efficiency of all resources employed for the same purpose. Results: With this question, we obtained, after an analysis of the experiments, a taxon of hits for the right of spraying 75.61% and for the left one in 63.41%, in which nsAlterar promoted the improvement in the distributions, even if They were not segmented correctly, through approximations properly. Conclusions: Based on the active model associated with nsAlterar and other resources, it was possible to complement a functionality of the PneumoCAD system, through the use of segmentation in reais, thus contributing to a higher efficiency and better results. / Contextualização: As tecnologias como sistemas CAD, tornam-se ideais como segunda opinião, ou seja, para trabalhar em parceria com o médico. Por exemplo, o PneumoCAD pode ser utilizado para realização do diagnóstico de ausência ou não de pneumonia em crianças de 1 e < 5 anos de idade, através das radiografias de raios-X. Problemática: Entretanto, a utilização do PneumoCAD exige que uma região de interesse, referente aos pulmões, sejam determinadas manualmente. Baseado nesta exigência e dificuldade buscou-se alguma solução que não foi encontrada em nenhuma pesquisa, aplicada a radiografias, envolvendo o PneumoCAD, através do Active Shape Model, tendo como paciente crianças com idade entre 1 e < 5 anos. Proposta: É, então, proposto o uso do Active Shape Model, associado à técnica desenvolvida e denominada nsAlterar em melhora à segmentação baseada no ns . Materiais e Métodos: Foram submetidas, no MATLAB, 56 amostras de radiografias do “padrão-ouro”, em 8 etapas, através de algoritmos modificados e aperfei- çoados, além de ferramentas implementadas de apoio, como: para o treinamento a partir de exemplos; assim como as medidas de similaridades para buscar investigar, de maneira quantitativa, sobre a eficiência de todos os recursos empregados para o mesmo propósito. Resultados: Com isto, foi obtida, após a análise dos experimentos, a taxa de acertos para o pulmão direito em 75,61% e para o esquerdo em 63,41%, em que o nsAlterar promoveu o melhoramento nas distribuições, mesmo as que não foram segmentadas corretamente, através de aproximações de maneira adequada. Conclusões: A partir do Active Shape Model associado ao nsAlterar e demais recursos, foi possível complementar a funcionalidade do sistema PneumoCAD, através do uso da segmentação em situações reais, contribuindo, assim, para a obtenção de maior eficiência e de melhores resultados.

The four-dimensional (4-D) cardiac MR images contain rich information about the static and dynamic properties of the heart, which were not fully utilized in clinical practice for quantitative analysis -- a difficult task for humans, which can be achieved by computer-aided image analysis and diagnosis. In this thesis, the 4-D Active Appearance Model (AAM) was used to achieve highly automated computer segmentation of the left and right ventricles (LV and RV) and the diagnosis of normal and tetralogy of Fallot (TOF) patients. The whole process was implemented in four stages: data construction, model construction, computer segmentation, and computer-aided diagnosis. The data construction stage overcame most inherent limitations of cardiac MR imaging and produced high-quality 4-D ventricular image with isotropic voxels, complete coverage and no respiratory motion artifacts. A manual tracing application was developed to trace the ventricular surfaces in a true 4-D context and produced accurate independent standard for model construction and segmentation validation. In the model construction stage, the 4-D AAMs were constructed using a custom designed automatic landmarking and texture mapping procedure with high efficiency. In the computer segmentation stage, the 4-D AAMs were applied to segment the left and right ventricles of 25 normal and 25 TOF patient scans. The segmentation achieved accurate results measured by signed surface positioning errors. On normal hearts, the average signed errors were 0.3±2.3 mm for LV and 0.1±3.4 mm for RV. On TOF hearts with large shape variability, the errors were -1.5±3.2 mm for LV and -0.9±4.3 mm for RV. Other error metrics such as relative overlapping also indicated good segmentation accuracies. In the computer-aided diagnosis stage, 100% normal/TOF classification was achieved using the novel 4-D ventricular function indices -- the shape modal indices. The longitudinal analysis performed on subjects with multiple annual scans showed that the normal subjects exhibited smaller variances of these 4-D indices than TOF patients, which demonstrated the potential of using them as disease status determinants. In addition, the quantitative 4-D indices provided more information about the dynamic properties of the heart and identified patient-specific features that were not sensed by human expert observers.

The development of predictive appearance models for organic tissues is a challenging task due to the inherent complexity of these materials. In this thesis, we closely examine the biophysical processes responsible for the appearance attributes of whole blood, one the most fundamental of these materials. We describe a new appearance model that simulates the mechanisms of light propagation and absorption within the cellular and fluid portions of this specialized tissue. The proposed model employs a comprehensive, and yet flexible first principles approach based on the morphological, optical and biochemical properties of blood cells. This approach allows for environment driven changes in the cells' anatomy and orientation to be appropriately included into the light transport simulations. The correctness and predictive capabilities of the proposed model are quantitatively and qualitatively evaluated through comparisons of modeled results with actual measured data and experimental observations reported in the scientific literature. Its incorporation into rendering systems is illustrated through images of blood samples depicting appearance variations controlled by physiologically meaningful parameters. Besides the contributions to the modeling of material appearance, the research presented in this thesis is also expected to have applications in a wide range of biomedical areas, from optical diagnostics to the visualization and noninvasive imaging of blood-perfused tissues.

No description available.

Object appearance models are a consequence of illumination, viewing direction, camera intrinsics, and other conditions that are specific to a particular camera. As a result, a model acquired in one view is often inappropriate for use in other viewpoints. In this work we treat this appearance model distortion between two non-overlapping cameras as one in which some unknown color transfer function warps a known appearance model from one view to another. We demonstrate how to recover this function in the case where the distortion function is approximated as general affine and object appearance is represented as a mixture of Gaussians. Appearance models are brought into correspondence by searching for a bijection function that best minimizes an entropic metric for model dissimilarity. These correspondences lead to a solution for the transfer function that brings the parameters of the models into alignment in the UV chromaticity plane. Finally, a set of these transfer functions acquired from a collection of object pairs are generalized to a single camera-pair-specific transfer function via robust fitting. We demonstrate the method in the context of a video surveillance network and show that recognition of subjects in disjoint views can be significantly improved using the new color transfer approach.

The development of predictive appearance models for organic tissues is a challenging task due to the inherent complexity of these materials. In this thesis, we closely examine the biophysical processes responsible for the appearance attributes of whole blood, one the most fundamental of these materials. We describe a new appearance model that simulates the mechanisms of light propagation and absorption within the cellular and fluid portions of this specialized tissue. The proposed model employs a comprehensive, and yet flexible first principles approach based on the morphological, optical and biochemical properties of blood cells. This approach allows for environment driven changes in the cells' anatomy and orientation to be appropriately included into the light transport simulations. The correctness and predictive capabilities of the proposed model are quantitatively and qualitatively evaluated through comparisons of modeled results with actual measured data and experimental observations reported in the scientific literature. Its incorporation into rendering systems is illustrated through images of blood samples depicting appearance variations controlled by physiologically meaningful parameters. Besides the contributions to the modeling of material appearance, the research presented in this thesis is also expected to have applications in a wide range of biomedical areas, from optical diagnostics to the visualization and noninvasive imaging of blood-perfused tissues.

The need for real-time video analysis is rapidly increasing in today's world. The decreasing cost of powerful processors and the proliferation of affordable cameras, combined with needs for security, methods for searching the growing collection of video data, and an appetite for high-tech entertainment, have produced an environment where video processing is utilized for a wide variety of applications. Tracking is an element in many of these applications, for purposes like detecting anomalous behavior, classifying video clips, and measuring athletic performance. In this dissertation we focus on augmented reality, but the methods and conclusions are applicable to a wide variety of other areas. In particular, our work deals with achieving real-time performance while tracking with augmented reality systems using a minimum set of commercial hardware. We have built prototypes that use both existing technologies and new algorithms we have developed. While performance improvements would be possible with additional hardware, such as multiple cameras or parallel processors, we have concentrated on getting the most performance with the least equipment. Tracking is a broad research area, but an essential component of an augmented reality system. Tracking of some sort is needed to determine the location of scene augmentation. First, we investigated the effects of illumination on the pixel values recorded by a color video camera. We used the results to track a simple solid-colored object in our first augmented reality application. Our second augmented reality application tracks complex non-rigid objects, namely human faces. In the color experiment, we studied the effects of illumination on the color values recorded by a real camera. Human perception is important for many applications, but our focus is on the RGB values available to tracking algorithms. Since the lighting in most environments where video monitoring is done is close to white, (e.g., fluorescent lights in an office, incandescent lights in a home, or direct and indirect sunlight outside,) we looked at the response to "white" light sources as the intensity varied. The red, green, and blue values recorded by the camera can be converted to a number of other color spaces which have been shown to be invariant to various lighting conditions, including view angle, light angle, light intensity, or light color, using models of the physical properties of reflection. Our experiments show how well these derived quantities actually remained constant with real materials, real lights, and real cameras, while still retaining the ability to discriminate between different colors. This color experiment enabled us to find color spaces that were more invariant to changes in illumination intensity than the ones traditionally used. The first augmented reality application tracks a solid colored rectangle and replaces the rectangle with an image, so it appears that the subject is holding a picture instead. Tracking this simple shape is both easy and hard; easy because of the single color and the shape that can be represented by four points or four lines, and hard because there are fewer features available and the color is affected by illumination changes. Many algorithms for tracking fixed shapes do not run in real time or require rich feature sets. We have created a tracking method for simple solid colored objects that uses color and edge information and is fast enough for real-time operation. We also demonstrate a fast deinterlacing method to avoid "tearing" of fast moving edges when recorded by an interlaced camera, and optimization techniques that usually achieved a speedup of about 10 from an implementation that already used optimized image processing library routines. Human faces are complex objects that differ between individuals and undergo non-rigid transformations. Our second augmented reality application detects faces, determines their initial pose, and then tracks changes in real time. The results are displayed as virtual objects overlaid on the real video image. We used existing algorithms for motion detection and face detection. We present a novel method for determining the initial face pose in real time using symmetry. Our face tracking uses existing point tracking methods as well as extensions to Active Appearance Models (AAMs). We also give a new method for integrating detection and tracking data and leveraging the temporal coherence in video data to mitigate the false positive detections. While many face tracking applications assume exactly one face is in the image, our techniques can handle any number of faces. The color experiment along with the two augmented reality applications provide improvements in understanding the effects of illumination intensity changes on recorded colors, as well as better real-time methods for detection and tracking of solid shapes and human faces for augmented reality. These techniques can be applied to other real-time video analysis tasks, such as surveillance and video analysis.

No / Age progression that involves the reconstruction of facial appearance with a natural ageing effect has several applications. These include the search for missing people and identification of fugitives. The majority of age progression methods reported in the literature are data driven. Hence, such methods learn from training data and utilise statistical models such as 3D morphable models and active appearance models (AAM). Principal component analysis (PCA) which is a vital part of these models has an unfortunate drawback of averaging out texture details. Therefore, they work as a low pass filter and as such many of the face skin deformations and minor details become faded. Interestingly, recent work in 2D and 3D animation has shown that patches of the human face are somewhat similar when compared in isolation. Thus, researchers have proposed generating novel faces by compositing small face patches, usually from large image databases. Following these ideas, we propose a novel age progression model which synthesises aged faces using a hybrid of these two techniques. First, an invertible model of age synthesis is developed using AAM and sparse partial least squares regression (sPLS). Then the texture details of the face are enhanced using the patch-based synthesis approach. Our results show that the hybrid algorithm produces both unique and realistic images. Furthermore, our method demonstrates that the identity and ageing effects of subjects can be more emphasised.

No description available.

Yes / Age and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.