This thesis presents a method of object classification using the idea of deformable shape matching. Three types of visual features, geometric blur, C1 and SIFT, are used to generate feature descriptors. These feature descriptors are then used to find point correspondences between pairs of images. Various morphable models are created by small subsets of these correspondences using thin-plate spline. Given these morphs, a simple algorithm, least median of squares (LMEDS), is used to find the best morph. A scoring metric, using both LMEDS and distance transform, is used to classify test images based on a nearest neighbor algorithm. We perform the experiments on the Caltech 101 dataset [5]. To ease computation, for each test image, a shortlist is created containing 10 of the most likely candidates. We were unable to duplicate the performance of [1] in the shortlist stage because we did not use hand-segmentation to extract objects for our training images. However, our gain from the shortlist to correspondence stage is comparable to theirs. In our experiments, we improved from 21% to 28% (gain of 33%), while [1] improved from 41% to 48% (gain of 17%). We find that using a non-shape based approach, C2 [14], the overall classification rate of 33.61% is higher than all of the shaped based methods tested in our experiments.

Pedestrian detection has been an active research area for computer vision in recently years. It has many applications that could improve our lives, such as video surveillance security, auto-driving assistance systems, etc. The approaches of pedestrian detection could be roughly categorized into two categories, shape-based approaches and appearance-based approaches. In the literature, most of approaches are appearance-based. Shape-based approaches are usually integrated with an appearance-based approach to speed up a detection process. In this thesis, I propose a shape-based pedestrian detection framework using the geometric features of human to detect pedestrians. This framework includes three main steps. Give a static image, i) generating the edge image of the given image, ii) according to the edge image, extracting the basic polylines, and iii) using the geometric relationships among the polylines to detect pedestrians. The detection result obtained by the proposed framework is promising. There was a comparison made of this proposed framework with the algorithm which introduced by Dalal and Triggs [7]. This proposed algorithm increased the true-positive detection result by 47.67%, and reduced the false-positive detection number by 41.42%.

In this thesis, real time detection of traffic signs using FPGA hardware is presented. Traffic signs have distinctive color and shape properties. Therefore, color and shape based algorithms are chosen to implemented on FPGA. FPGA supports sufficient logic to implement complete systems and sub-systems. Color information of images/frames is used to minimize the search domain of detection process. Using FPGA, real time conversion of YUV space to RGB space is performed. Furthermore, color thresholding algorithm is used to localize the sign in the image/video depending on the color. Edges are the most important image/frame attributes that provide valuable information about the shape of the objects. Sobel edge detection algorithm is implemented on FPGA. After color segmentation, FPGA implementation of Sobel algorithm is used to find the edges of candidate traffic signs in real time. Later, radial symmetry based shape detection algorithm is used to determine circular traffic signs. Each FPGA implemented algorithm is tested by using video sequences and static images. In addition, combined implementation of color based and shape based algorithms are tested. Joint application of color and shape based algorithms are used in order to reduce search domain and the processing time of detection process. Designing architecture on FPGA makes traffic sign detection system portable as a final product and relatively more efficient than the computer based detection systems. The resulting hardware is suitable where cost and compactness constraints are important.

The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented.

No description available.

Optimization and planning of radiation therapy is performed in a treatment planning system. This includes the definition of target structures to be irradiated and organs at risk to be protected, typically performed by contouring structures slice by slice in the image data. Conversions between contours and their volume representations are needed for visualizations and computations, but will however introduce a loss of information due to the sampling to a uniform voxel grid. The number of conversions performed can be large, causing errors to accumulate. The aim of this thesis is to examine volume reconstruction methods and sparse voxel representations for the purpose of volume reconstruction and representation with better accuracy than currently used algorithms in treatment planning systems. A prototype has been shown to be more accurate on contours and potentially cheaper in memory compared to the current method in RayStation in the case where contours represent non-smooth objects.

This thesis addresses the generation of behaviourally useful, robust representations of the sensory world in the context of machine vision and behaviour. The goals of the work presented in this thesis are to investigate strategies for representing the visual world in a way which is behaviourally useful, to investigate the use of a neurally inspired early perceptual organisation system upon high-level processing in an object recognition system and to investigate the use of a perceptual organisation system on driving an object-based selection process. To address these problems, a biologically inspired framework for machine attention has been developed at a high level of neural abstraction, which has been heavily inspired by the psychological and physiological literature. The framework is described in this thesis, and three system implementations, which investigate the above issues, are described and analysed in detail. The primate brain has access to a coherent representation of the external world, which appears as objects at different spatial locations. It is through these representations that appropriate behavioural responses may be generated. For example, we do not become confused by cluttered scenes or by occluded objects. The representation of the visual scene is generated in a hierarchical computing structure in the primate brain: while shape and position information are able to drive attentional selection rapidly, high-level processes such as object recognition must be performed serially, passing through an attentional bottleneck. Through the process of attentional selection, the primate visual system identifies behaviourally relevant regions of the visual scene, which allows it to prioritise serial attentional shifts towards certain locations. In primates, the process of attentional selection is complex, operating upon surface representations which are robust to occlusion. Attention itself suppresses neural activity related to distractor objects, while sustaining activity relating to the target, allowing the target object to have a clear neural representation upon which the recognition process can operate. This thesis concludes that dynamic representations that are both early and robust against occlusion have the potential to be highly useful in machine vision and behaviour applications.

The taxonomical identification of freshwater green microalgae is highly relevant problem in Phycology. In particular, the taxonomical identification of samples from the Selenastraceae family of algae is considered particularly problematic with many known inconsistencies. Biologists manually inspect and analyze microscope images of alga strains, and typically carry out several complex and time-consuming procedures that demand considerable expert knowledge. Such practical limitations motivated this investigation on the applicability of image processing, pattern recognition and visual data mining techniques to support the biologists in tasks of species identification. This thesis describes methodologies for the classification of green alga images, considering both traditional automated classification processes and also a user-assisted incremental classification process supported by Neighbor Joining tree visualizations. In this process, users can interact with the visualizations to introduce their knowledge into the classification process, e.g. by selecting suitable training sets and evaluate the results, thus steering the classification process. In order for visualization and classification to be feasible, accurate features must be obtained from the images capable of distinguishing between the different species of algae. As morphological shape properties are a fundamental property in identifying species, suitable segmentation and shape feature extraction strategies have been developed. This was particularly challenging, as different alga species share common morphological characteristics. Two segmentation methodologies are introduced, in which one relies on the level set method and the other is based on the region growing principle. Although the contour-based approach is capable of handling the uneven conditions of green alga images, its computation is time-consuming and not suitable for real time applications. A specialized formulation of the region-based methodology is proposed that considers the specific characteristics of the green alga images handled. This second formulation was shown to be more efficient than the level set approach and generates highly accurate segmentations. Once accurate alga segmentation is achieved, two descriptors are proposed that capture alga shape properties, and also an effective general shape descriptor that computes quantitative measures from two signatures associated to the shape properties. Experimental results are described that indicate that the proposed solutions can be useful to biologists conducting alga identification tasks once it reduces their effort and attains satisfactory discrimination among species. / A identificação taxonômica de algas verdes de água doce é um problema de extrema relevância na Ficologia. Identificar espécies de algas da família Selenastraceae é uma tarefa complexa devido às inconsistências existentes em sua taxonomia, reconhecida como problemática. Os biólogos analisam manualmente imagens de microscópio de cepas de algas e realizam diversos procedimentos demorados que necessitamde conhecimento sólido. Tais limitaçõesmotivaramo estudo da aplicabilidade de técnicas de processamento de imagens, reconhecimento de padrões e mineração visual de dados para apoiar os biólogos em tarefas de identificação de espécies de algas. Esta tese descreve metodologias computacionais para a classificação de imagens de algas verdes, nas abordagens tradicional e baseada em classificação visual incremental com participação do usuário. Nesta última, os usuários interagem com visualizações baseadas em árvores filogenéticas para utilizar seu conhecimento no processo de classificação, como por exemplo, na seleção de instâncias relevantes para o conjunto de treinamento de um classificador, como também na avaliação dos resultados. De forma a viabilizar o uso de classificadores e técnicas de visualização, vetores de características devem ser obtidos das imagens de algas verdes. Neste trabalho, utiliza-se extração de características de forma, uma vez que a taxonomia da família Selenastraceae considera primordialmente as características morfológicas na identificação das espécies. No entanto, a obtenção de características representativas requer que as algas sejam precisamente segmentadas das imagens. Esta é, de fato, uma tarefa altamente desafiadora considerando a baixa qualidade das imagens e a maneira pelas quais as algas se organizam nas imagens. Duas metodologias de segmentação foram introduzidas: uma baseada no método Level Set e outra baseada no algoritmo de crescimento de regiões. A primeira se mostrou robusta e consegue identificar com alta precisão as algas nas imagens, mas seu tempo de execução é alto. A outra apresenta maior precisão e é mais rápida, uma vez que as técnicas de pré-processamento são especializadas para as imagens de algas verdes. Uma vez segmentadas as algas, dois descritores para caracterizar as imagens foram propostos: um baseado em características geométricas básicas e outro que utiliza medidas quantitativas calculadas a partir das assinaturas de forma. Resultados experimentais indicaram que as soluções propostas têm um bom potencial para serem utilizadas em tarefas de identificação taxonômica de algas verdes, uma vez que reduz o esforço nos procedimentos manuais e obtém-se classificações satisfatórias.

In this thesis, detection of traffic signs is studied. Since, both color and shape properties of traffic signs are distinctive / these two properties have been employed for detection. Detection using color properties is studied in two different color domains in order to examine and compare the advantages and the disadvantages of these domains for detection purposes. In addition to their color information, shape information is also employed for detection purpose. Edge information (obtained by using the Sobel Operator) of the images/frames is considered as search domain to find triangular, rectangular, octagonal and circular traffic signs. In order to improve the performance of detection process a joint implementation of shape and color based algorithms is utilized. Two different methods have been used v in order to combine these two features. Both of the algorithms help reducing the number of pixels to check whether they belong to a sign or not. This, of course, reduces the processing time of detection process. Each utilized algorithm is tested and compared with the others by using both static images from different sources and video streams. Images having adverse properties are used in order to state algorithms response for some specific conditions such as bad illumination and shadow. After implementation, results show that joint implementation of the color and shape based detection algorithms produces more accurate results. Moreover, joint implementation reduces the processing time of the detection process when compared to application of algorithms individually since it diminishes the search domain.

The taxonomical identification of freshwater green microalgae is highly relevant problem in Phycology. In particular, the taxonomical identification of samples from the Selenastraceae family of algae is considered particularly problematic with many known inconsistencies. Biologists manually inspect and analyze microscope images of alga strains, and typically carry out several complex and time-consuming procedures that demand considerable expert knowledge. Such practical limitations motivated this investigation on the applicability of image processing, pattern recognition and visual data mining techniques to support the biologists in tasks of species identification. This thesis describes methodologies for the classification of green alga images, considering both traditional automated classification processes and also a user-assisted incremental classification process supported by Neighbor Joining tree visualizations. In this process, users can interact with the visualizations to introduce their knowledge into the classification process, e.g. by selecting suitable training sets and evaluate the results, thus steering the classification process. In order for visualization and classification to be feasible, accurate features must be obtained from the images capable of distinguishing between the different species of algae. As morphological shape properties are a fundamental property in identifying species, suitable segmentation and shape feature extraction strategies have been developed. This was particularly challenging, as different alga species share common morphological characteristics. Two segmentation methodologies are introduced, in which one relies on the level set method and the other is based on the region growing principle. Although the contour-based approach is capable of handling the uneven conditions of green alga images, its computation is time-consuming and not suitable for real time applications. A specialized formulation of the region-based methodology is proposed that considers the specific characteristics of the green alga images handled. This second formulation was shown to be more efficient than the level set approach and generates highly accurate segmentations. Once accurate alga segmentation is achieved, two descriptors are proposed that capture alga shape properties, and also an effective general shape descriptor that computes quantitative measures from two signatures associated to the shape properties. Experimental results are described that indicate that the proposed solutions can be useful to biologists conducting alga identification tasks once it reduces their effort and attains satisfactory discrimination among species. / A identificação taxonômica de algas verdes de água doce é um problema de extrema relevância na Ficologia. Identificar espécies de algas da família Selenastraceae é uma tarefa complexa devido às inconsistências existentes em sua taxonomia, reconhecida como problemática. Os biólogos analisam manualmente imagens de microscópio de cepas de algas e realizam diversos procedimentos demorados que necessitamde conhecimento sólido. Tais limitaçõesmotivaramo estudo da aplicabilidade de técnicas de processamento de imagens, reconhecimento de padrões e mineração visual de dados para apoiar os biólogos em tarefas de identificação de espécies de algas. Esta tese descreve metodologias computacionais para a classificação de imagens de algas verdes, nas abordagens tradicional e baseada em classificação visual incremental com participação do usuário. Nesta última, os usuários interagem com visualizações baseadas em árvores filogenéticas para utilizar seu conhecimento no processo de classificação, como por exemplo, na seleção de instâncias relevantes para o conjunto de treinamento de um classificador, como também na avaliação dos resultados. De forma a viabilizar o uso de classificadores e técnicas de visualização, vetores de características devem ser obtidos das imagens de algas verdes. Neste trabalho, utiliza-se extração de características de forma, uma vez que a taxonomia da família Selenastraceae considera primordialmente as características morfológicas na identificação das espécies. No entanto, a obtenção de características representativas requer que as algas sejam precisamente segmentadas das imagens. Esta é, de fato, uma tarefa altamente desafiadora considerando a baixa qualidade das imagens e a maneira pelas quais as algas se organizam nas imagens. Duas metodologias de segmentação foram introduzidas: uma baseada no método Level Set e outra baseada no algoritmo de crescimento de regiões. A primeira se mostrou robusta e consegue identificar com alta precisão as algas nas imagens, mas seu tempo de execução é alto. A outra apresenta maior precisão e é mais rápida, uma vez que as técnicas de pré-processamento são especializadas para as imagens de algas verdes. Uma vez segmentadas as algas, dois descritores para caracterizar as imagens foram propostos: um baseado em características geométricas básicas e outro que utiliza medidas quantitativas calculadas a partir das assinaturas de forma. Resultados experimentais indicaram que as soluções propostas têm um bom potencial para serem utilizadas em tarefas de identificação taxonômica de algas verdes, uma vez que reduz o esforço nos procedimentos manuais e obtém-se classificações satisfatórias.