O ato de executar zoom em imagens é uma tarefa que se aplica em diversas áreas que podem variar desde entretenimento até aplicações científicas. Um dos grandes desafios na área é manter a definição das bordas dos objetos da imagem sem que haja a criação de artefatos tais como aspecto serrilhado ou borramento. Diversos métodos de preservação de borda foram apresentados na literatura. Este trabalho apresenta a proposta de uma nova técnica de interpolação de imagens baseada em clustering que tem como objetivo aumentar a resolução da imagem em tons de cinza preservando as bordas dos objetos nela presentes com um método mais simples e de fácil implementação. Foram realizados testes da técnica proposta com diversas imagens de natureza diferente e seus resultados comparados aos métodos clássicos de interpolação de imagem encontrados na literatura. Para teste da eficácia foram consideradas a medida do PSNR e Correlação Cruzada com cada método comparado. Os resultados obtidos mostraram que a técnica é promissora e que cumpre os objetivos do projeto. / Image zooming is a task applicable to many areas which can vary from entertainment to scientific applications. A big challenge is image edge preserving without creating artifacts like blurring or blocking. Several methods for edge preserving were proposed in literature. This work presents a new technique proposal based on clustering which aims to increase gray scale image resolution preserving objects edges with a simple method and easy to implement. Many different types of images were used to make tests of the proposed technique and results are compared to classical methods of image interpolation found in literature. PSNR and Cross-Correlation measurements were used to compare efficiency between methods. Results showed that the technique is quite competitive and meets the project goals.

Orientadores: Marcos César Ferreira, Édson Luis Bolfe / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-23T13:41:49Z (GMT). No. of bitstreams: 1 Andrade_DanielDiasde_M.pdf: 5558237 bytes, checksum: d148a7a1eabd8b1f1c82c3c7d7641810 (MD5) Previous issue date: 2013 / Resumo: O uso de modelos de estimativa de biomassa através de dados de sensoriamento remoto pode contribuir à geração de conhecimento para a mitigação dos efeitos negativos no clima e na biodiversidade do planeta, oriundos da utilização humana dos recursos naturais. A Área de Proteção Ambiental (APA) Fernão Dias é uma Unidade de Conservação Estadual de Uso Sustentável localizada na Serra da Mantiqueira no Estado de Minas Gerais, próxima à fronteira com o Estado de São Paulo. Por meio da estimativa realizada a partir de dados de campo para a biomassa acima do solo do fragmento denominado "97", coberto por Floresta Ombrófila Madura, e de respostas espectrais detectadas pelo sensor TM do satélite Landsat 5, realizou-se a estimativa do carbono para a cobertura florestal presente no território da APA. O grau de relação entre o Índice de Vegetação ajustado ao solo (SAVI), o Índice de Vegetação pela Razão (SRI), o Índice de Vegetação Melhorado (EVI), o Índice de Vegetação da Diferença Normalizada (NDVI), o Índice de Vegetação Perpendicular (PVI) e os dados de campo foi obtido por meio de equações de regressão linear simples. Foram obtidas correlações distintas entre as variáveis espectrais (índices de vegetação) e os parâmetros biofísicos (carbono): r2 = 0,738; 0,728; 0,721; 0,670; e 0,542, respectivamente. Mapas síntese do estoque de carbono acima do solo (t/ha) foram gerados para as áreas florestadas da APA, delimitados por divisões físicas e político-administrativas. A estimativa se aproximou dos valores amostrados para a biomassa medida em campo, apresentando menor equivalência na medida em que se amplia e se diversifica a área utilizada na estimativa / Abstract: The use of biomass estimation models by means of remote sensing data may contribute to the generation of knowledge for the mitigation of negative effects on climate and biodiversity in the planet, due to the human appropriation of the natural resources. Fernão Dias Environmental Protection Area (APA) is a State Conservation Unit of Sustainable Use located at the Mantiqueira Mountains, Minas Gerais State, nearby the São Paulo State border. Aboveground biomass was estimated for the fragment denominated "97", covered by Mature Evergreen Forest. Landsat 5 imagery was used to map forest cover, so Carbon estimation could be represented for the entire Fernão Dias APA Area. The relationship between Soil Adjusted Vegetation Index (SAVI), Simple Ratio Index (SRI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), and field data was obtained by simple linear regression equations. Distinct correlations for the spectral variables (vegetation indices) and the biophysical parameters (Carbon) were obtained: r2 = 0,738; 0,728; 0,721; 0,670; e 0,542, respectively. Synthetic maps for aboveground Carbon (t/ha) were generated for the Fernão Dias APA's forest areas, delimited by physical and political-administrative divisions. The estimations were similar to the biomass values sampled in the field, presenting smaller equivalences as the area used in the estimation is increased and diversified / Mestrado / Análise Ambiental e Dinâmica Territorial / Mestre em Geografia

Invasive tree species pose a huge problem in the Garden Route and are particularly damaging to aquatic ecosystems, including wetlands, riparian zones, lakes and estuaries. Therefore, this study aimed to determine priority areas for invasive tree species management, with a focus on aquatic ecosystems. This was achieved by using existing literature to identify priority species, based on their impact on aquatic ecosystems and their associated ecosystem services, and then testing the suitability of SPOT-6 and WorldView-3 multispectral data at detecting these focal species. The priority species identified were: Acacia cyclops (rooikrans), Acacia longifolia (long-leaved wattle), Acacia mearnsii (black wattle), Acacia melanoxylon (blackwood), Acacia saligna (Port Jackson willow), Eucalyptus camaldulensis (red river gum), Pinus pinaster (cluster pine) and Pinus radiata (radiata pine). The Random Forest classifier on SPOT-6 data achieved an overall accuracy of 62.5% and this method was consequently deemed ineffective at separating invasive tree species from other tree species in the Garden Route. The overall accuracy of the WorldView-3 classifier was higher (78.9%) but the cost of the data limited the use of more images for the detection of the focal species throughout the Garden Route. Therefore, to identify priority areas for invasive tree management, criteria derived from existing literature were input into spatial conservation planning software. The analysis identified the: Saasveld section of the Garden Route National Park, the Wilderness Lakes, Knysna Forest, Knysna Estuary, Tsitsikamma Forest around Stormsriver and a disturbed area of fynbos southeast of Kareedouw as management priorities. Currently spatial conservation planning software proved to be cost-affordable and useful tool and is recommended for invasive tree management in the Garden Route.

Submitted in fulfillment of the requirements for the Master's Degree in Information and Communication Technology, Durban, University of Technology, Durban, South Africa, 2017. / The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms. / M

The ongoing development of storage devices and technologies for medical image management has led to a growth in the digital archiving of these images. The characteristics of medical x-rays are examined, and a number of digital coding methods are considered. An investigation of several fast cosine transform algorithms is carried out. An adaptive cosine transform coding technique is implemented which produces good quality images using bit rates lower than 0.38 bits per picture element

Pancreatic ductal adenocarcinoma (PDAC) has the poorest five-year survival rate of any cancer. Currently, there are no effective diagnostics or chemotherapeutics. Surgical resection is the only curative therapy. However, most patients experience recurrence due largely to challenges in assessing tumor margin status in the operating room. Molecular probes that selectively highlight pancreatic cancer tissue, having the potential to improve PDAC margin assessment intraoperatively, are urgently needed. In this work, a series of red and near-infrared fluorescent probes is reported. Two were found to distribute to normal pancreas following systemic administration. One selectively accumulates in genetically modified mouse models of PDAC, providing cancer-specific fluorescence. In contrast to the small molecule probes reported previously, it possesses inherent affinity for PDAC cells and tissue, and thus does not require conjugation to targeting agents. Moreover, the probe exhibits intracellular accumulation and enables visualization of four levels of structure including the whole organ, tissue, individual cells and subcellular organelles. It can thus promote new strategies for precision image-guided surgery, pancreatic cancer detection, the monitoring of therapeutic outcomes and basic research.

Orientador: Carlos Roberto de Souza Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-27T02:30:54Z (GMT). No. of bitstreams: 1 Locatelli_PauloEduardo_M.pdf: 8425905 bytes, checksum: a57947ba3b089a77dc310bac5fc58f26 (MD5) Previous issue date: 2014 / Resumo: A extração automática de feições lineares a partir de técnicas de processamento digital de imagens atualmente é uma ampla possibilidade em Geociências, permitindo a interpretação de lineamentos e reconhecimento de padrões em fotos aéreas e imagens de satélite. Entretanto, o que ainda predomina é a extração dessas feições de forma manual, o que geralmente se traduz como um trabalho demorado, sujeito à experiência do usuário e critérios subjetivos. A extração e interpretação de feições lineares auxiliada por algoritmos e métodos computacionais permite acelerar o processo e reduzir a subjetividade inerente ao método manual. Nesse sentido, diferentes algoritmos têm sido propostos com a finalidade de otimizar o processo de detecção destas feições. Em Geociências, feições lineares geralmente estão associadas a elementos da estrutura geológica, como zonas de falha, famílias de fraturas, enxames de diques e cinturões de cisalhamento. A identificação dessas estruturas é essencial na exploração mineral e de hidrocarbonetos, entre outras áreas. No entanto, a identificação de lineamentos em superfície não garante a existência de correlação com estruturas geológicas em profundidade. Neste contexto, este trabalho utiliza a região de Serra Leste, Carajás (PA), como área teste para verificar a recorrência de um lineamento, em diferentes fontes de dados, no sentido de ponderar a sua relevância geológica em níveis crustais diversos. Os dados empregados no trabalho incluem: (i) modelos digitais de elevação (MDEs) extraídos de dados óticos (ASTER) e de micro-ondas na banda-C (SRTM), (ii) imagens multiespectrais orbitais, e (iii) produtos gerados a partir de dados aerogeofísicos (magnetometria). Aos MDEs foram aplicados algoritmos de extração automática de lineamentos e técnica de realce das feições morfoestruturais. Aos dados aeromagnéticos foram aplicados (i) métodos de realce das anomalias magnéticas; (ii) deconvolução de Euler e (iii) métodos de inversão para se obter o modelo de susceptibilidade magnética. Os resultados obtidos indicam que mapas de declividade, gerados a partir dos MDEs, são produtos singulares para extração otimizada de lineamentos com base em programas semiautomáticos. Os produtos aeromagnéticos indicam uma grande coerência entre anomalias magnéticas realçadas, independente de uso de métodos direto ou inverso de processamento. Entretanto, a comparação entre as anomalias magnéticas em profundidade e lineamentos superficiais marcados por feições do relevo mostra significativa decorrelação. Essa constatação tem impacto importante sobre o uso de mapas estruturais foto-interpretados ou gerados automaticamente na exploração mineral e petrolífera / Abstract: The automatic extraction of linear features by digital image processing techniques has been showing a wide range of possibility in Geosciences allowing the interpretation of lineaments and pattern recognition in aerial photos and satellite images. However, there is still a predominance of manual extraction of these features, which usually is a time consuming job and subject to expertise and subjective criteria. The extraction and interpretation of linear features when aided by computational methods and algorithms can speed up the process and reduce the subjectivity inherent to the manual process. In this sense, several algorithms have been proposed in order to optimize the detection process of these features. In geosciences, linear features are commonly related with geological structure elements, such as fault or shear zones, fractures families and dyke swarms. The identification of these structures is essential to mineral and hydrocarbons exploration, among other areas. However, the identification of surface lineaments does not ensure correlation with in-depth geological structures. In this context, this work uses Serra Leste region, in Carajás Province, Pará State, Brazil, as a testing area to check the recurrence of lineaments in different data sources in order to assess its geological significance in several crustal levels. The data used in this study include: (i) digital elevation models (DEMs) extracted from the optical data (ASTER) and in the microwave C-band (SRTM), (ii) orbital multispectral images, and (iii) products generated from airborne geophysics (magnetometry). Algorithms for automatic extraction of lineaments and technical enhancement of morphostructural features were applied to the DEMs. With regard to the magnetic data, the flowing techniques: (i) methods for enhancement of magnetic anomalies, (ii) Euler deconvolution and (iii) inversion methods to recover susceptibility model. The results indicate that slope maps, generated from the DEMs, are unique products for an optimized lineament extraction using semi-automated methods. The magnetic products display a wide coherence between enhanced magnetic anomalies, independent of the use of direct or inverse processing methods. Nonetheless, the comparison between the magnetic anomalies at depth and surface lineament, marked by relief features, shows no significant correlation. This finding has important impact on the use of photo-interpreted structural maps or automatically generated in mineral and oil exploration / Mestrado / Geologia e Recursos Naturais / Mestre em Geociências

This study compares independent use of two known algorithms (Kalmar filter with background subtraction and Particle Filter) that are commonly deployed in object tracking applications. Object tracking in general is very challenging; it presents numerous problems that need to be addressed by the application in order to facilitate its successful deployment. Such problems range from abrupt object motion, during tracking, to a change in appearance of the scene and the object, as well as object to scene occlusions, and camera motion among others. It is important to take into consideration some issues, such as, accounting for noise associated with the image in question, ability to predict to an acceptable statistical accuracy, the position of the object at a particular time given its current position. This study tackles some of the issues raised above prior to addressing how the use of either of the aforementioned algorithm, minimize or in some cases eliminate the negative effects

Gesture recognition plays an important role in human computer Interaction for intelligent computing. Major applications like Gaming, Robotics and Automated Homes uses gesture recognition techniques which diminishes the usage of mechanical devices. The main goal of my thesis is to interpret SWAT team gestures using different types of sensors. Accelerometer and flex sensors were explored extensively to build a prototype for soldiers to communicate in the absence of line of sight. Arm movements were recognized by flex sensors and motion gestures by Accelerometers. Accelerometers are used to measure acceleration in respect to movement of the sensor in 3D. Flex sensors changes its resistance based on the amount of bend in the sensor. SVM is the classification algorithm used for classification of the samples. LIBSVM (Library for Support Vector Machines) is integrated software for support vector classification, regression and distribution estimation which supports multi class classification. Sensors data is connected to the WI micro dig to digitize the signal and to transmit it wirelessly to the computing device. Feature extraction and Signal windowing were the two major factors which contribute for the accuracy of the system. Mean Average value and Standard Deviation are the two features considered for accelerometer sensor data classification and Standard deviation is used for the flex sensor analysis for optimum results. Filtering of the signal is done by identifying the different states of signals which are continuously sampled.

A new method for the general application of quadratic spatial/spatial frequency domain filtering to imagery is presented in this dissertation. The major contribution of this research is the development of an original algorithm for approximating the inverse psuedo Wigner distribution through synthesis of an image in the spatial domain which approximates the result of filtering an original image in the DPWD domain.