Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms

The authors analyze the design of a method for automatized evaluation of parameters in orthopantomographic images capturing pathological tissues developed in human jaw bones. The main problem affecting the applied medical diagnostic procedures consists in low repeatability of the performed evaluation. This condition is caused by two aspects, namely subjective approach of the involved medical specialists and the related exclusion of image processing instruments from the evaluation scheme. The paper contains a description of the utilized database containing images of cystic jaw bones; this description is further complemented with appropriate schematic repre¬sentation. Moreover, the authors present the results of fast automatized segmentation realized via the live-wire method and compare the obtained data with the results provided by other segmentation techniques. The shape parameters and the basic statistical quantities related to the distribution of intensities in the segmented areas are selected. The evaluation results are provided in the final section of the study; the authors correlate these values with the subjective assessment carried out by radiologists. Interestingly, the paper also comprises a discussion presenting the possibility of using selected parameters or their combinations to execute automatic classification of cysts and osteonecrosis. In this context, a comparison of various classifiers is performed, including the Decision Tree, Naive Bayes, Neural Network, k-NN, SVM, and LDA classifica¬tion tools. Within this comparison, the highest degree of accuracy (85% on the average) can be attributed to the Decision Tree, Naive Bayes, and Neural Network classifier

Efficient Hybrid Image Warping for High Frame-Rate Stereoscopic Rendering

Modern virtual reality simulations require a constant high-frame rate from the rendering engine. They may also require very low latency and stereo images. Previous rendering engines for virtual reality applications have exploited spatial and temporal coherence by using image-warping to re-use previous frames or to render a stereo pair at lower cost than running the full render pipeline twice. However these previous approaches have shown artifacts or have not scaled well with image size. We present a new image-warping algorithm that has several novel contributions: an adaptive grid generation algorithm for proxy geometry for image warping; a low-pass hole-filling algorithm to address un-occlusion; and support for transparent surfaces by efficiently ray casting transparent fragments stored in per-pixel linked lists of an A-Buffer. We evaluate our algorithm with a variety of challenging test cases. The results show that it achieves better quality image-warping than state-of-the-art techniques and that it can support transparent surfaces effectively. Finally, we show that our algorithm can achieve image warping at rates suitable for practical use in a variety of applications on modern virtual reality equipment

Lossless compression of images with specific characteristics

Doutoramento em Engenharia ElectrotécnicaA compressão de certos tipos de imagens é um desafio para algumas normas de compressão de imagem. Esta tese investiga a compressão sem perdas de imagens com características especiais, em particular imagens simples, imagens de cor indexada e imagens de microarrays. Estamos interessados no desenvolvimento de métodos de compressão completos e no estudo de técnicas de pré-processamento que possam ser utilizadas em conjunto com as normas de compressão de imagem. A esparsidade do histograma, uma propriedade das imagens simples, é um dos assuntos abordados nesta tese. Desenvolvemos uma técnica de pré-processamento, denominada compactação de histogramas, que explora esta propriedade e que pode ser usada em conjunto com as normas de compressão de imagem para um melhoramento significativo da eficiência de compressão. A compactação de histogramas e os algoritmos de reordenação podem ser usados como préprocessamento para melhorar a compressão sem perdas de imagens de cor indexada. Esta tese apresenta vários algoritmos e um estudo abrangente dos métodos já existentes. Métodos específicos, como é o caso da decomposição em árvores binárias, são também estudados e propostos. O uso de microarrays em biologia encontra-se em franca expansão. Devido ao elevado volume de dados gerados por experiência, são necessárias técnicas de compressão sem perdas. Nesta tese, exploramos a utilização de normas de compressão sem perdas e apresentamos novos algoritmos para codificar eficientemente este tipo de imagens, baseados em modelos de contexto finito e codificação aritmética.The compression of some types of images is a challenge for some standard compression techniques. This thesis investigates the lossless compression of images with specific characteristics, namely simple images, color-indexed images and microarray images. We are interested in the development of complete compression methods and in the study of preprocessing algorithms that could be used together with standard compression methods. The histogram sparseness, a property of simple images, is addressed in this thesis. We developed a preprocessing technique, denoted histogram packing, that explores this property and can be used with standard compression methods for improving significantly their efficiency. Histogram packing and palette reordering algorithms can be used as a preprocessing step for improving the lossless compression of color-indexed images. This thesis presents several algorithms and a comprehensive study of the already existing methods. Specific compression methods, such as binary tree decomposition, are also addressed. The use of microarray expression data in state-of-the-art biology has been well established and due to the significant volume of data generated per experiment, efficient lossless compression methods are needed. In this thesis, we explore the use of standard image coding techniques and we present new algorithms to efficiently compress this type of images, based on finite-context modeling and arithmetic coding

Anuário Científico 2001

O Conselho Científico do Instituto Superior de Engenharia de Lisboa inicia neste ano de 2002 a publicação do Anuário Científico do ISEL, acção que, estamos certos, terá continuidade. Integram este Anuário Científico os resumos dos artigos e comunicações publicados pelos nossos docentes em livros, revistas, actas de congressos ou apresentados noutros eventos científicos. Inserem-se também nesta publicação os resumos das teses orientadas ou coorientadas por docentes do ISEL. Os resumos apresentados são prova da aceitação pela comunidade científica, nacional e internacional, do trabalho científico desenvolvido pelo nosso corpo docente, de per si, ou de parceria com a restante comunidade académica e de investigação científica. Nas sociedades modernas o conhecimento é uma mais valia inquestionável, motor do seu desenvolvimento e gerador de riqueza. No nosso entendimento é um dever e uma obrigação apoiar todos os esforços conducentes ao progresso do conhecimento científico, bem como à sua transferência para a sociedade. O ensino da engenharia é indissociável à realização de teses de mestrado e de doutoramento. A actual legislação espartilha o ISEL nesta componente de pós-graduação em engenharia, situação que urge resolver. Não obstante, o ISEL tem conseguido, em articulação com instituições universitárias, encontrar mecanismos para ultrapassar parte destas limitações, contribuindo para o desenvolvimento científico e tecnológico do país

Novel Markovian Change Detection Models in Computer Vision

In this thesis novel probabilistic models are proposed for three different change detection tasks of computer vision, primarily focusing on applications from video surveillance and aerial exploitation. The surveys are performed in a coherent Markov Random Field (MRF) segmentation framework, but the introduced models face different practical challenges such as shadow effects, image registration errors or presence of noisy and incomplete concept descriptors. Contributions are presented in efficient feature extraction, probabilistic modeling of natural processes and feature integration via local innovations in the model structures. We show by several experiments that the proposed novelties embedded into a strict mathematical toolkit can significantly improve the results in real world test images and videos

Multi-modal on-body sensing of human activities

Increased usage and integration of state-of-the-art information technology in our everyday work life aims at increasing the working efficiency. Due to unhandy human-computer-interaction methods this progress does not always result in increased efficiency, for mobile workers in particular. Activity recognition based contextual computing attempts to balance this interaction deficiency. This work investigates wearable, on-body sensing techniques on their applicability in the field of human activity recognition. More precisely we are interested in the spotting and recognition of so-called manipulative hand gestures. In particular the thesis focuses on the question whether the widely used motion sensing based approach can be enhanced through additional information sources. The set of gestures a person usually performs on a specific place is limited -- in the contemplated production and maintenance scenarios in particular. As a consequence this thesis investigates whether the knowledge about the user's hand location provides essential hints for the activity recognition process. In addition, manipulative hand gestures -- due to their object manipulating character -- typically start in the moment the user's hand reaches a specific place, e.g. a specific part of a machinery. And the gestures most likely stop in the moment the hand leaves the position again. Hence this thesis investigates whether hand location can help solving the spotting problem. Moreover, as user-independence is still a major challenge in activity recognition, this thesis investigates location context as a possible key component in a user-independent recognition system. We test a Kalman filter based method to blend absolute position readings with orientation readings based on inertial measurements. A filter structure is suggested which allows up-sampling of slow absolute position readings, and thus introduces higher dynamics to the position estimations. In such a way the position measurement series is made aware of wrist motions in addition to the wrist position. We suggest location based gesture spotting and recognition approaches. Various methods to model the location classes used in the spotting and recognition stages as well as different location distance measures are suggested and evaluated. In addition a rather novel sensing approach in the field of human activity recognition is studied. This aims at compensating drawbacks of the mere motion sensing based approach. To this end we develop a wearable hardware architecture for lower arm muscular activity measurements. The sensing hardware based on force sensing resistors is designed to have a high dynamic range. In contrast to preliminary attempts the proposed new design makes hardware calibration unnecessary. Finally we suggest a modular and multi-modal recognition system; modular with respect to sensors, algorithms, and gesture classes. This means that adding or removing a sensor modality or an additional algorithm has little impact on the rest of the recognition system. Sensors and algorithms used for spotting and recognition can be selected and fine-tuned separately for each single activity. New activities can be added without impact on the recognition rates of the other activities