This study is centred on an analysis of pupils' lived experience while engaged in the generalisation of linear sequences/progressions presented in a pictorial context. The study is oriented within the conceptual framework of qualitative research, and is anchored within an interpretive paradigm. A case study methodological strategy was adopted, the research participants being the members of a mixed gender, high ability Grade 9 class of 23 pupils at an independent school in South Africa. The analytical framework is structured around a combination of complementary multiple perspectives provided by three theoretical ideas, enactivism, figural apprehension, and knowledge objectification. An important aspect of this analytical framework is the sensitivity it shows to the visual, phenomenological and semiotic aspects of figural pattern generalisation. It is the central thesis of this study that the combined complementary multiple perspectives of enactivism, figural apprehension and knowledge objectification provide a powerful depth of analysis to the exploration of the inter-relationship between the embodied processes of pattern generalisation and the visualisation of pictorial cues. The richly textured tapestry of activity captured through a multi-systemic semiotic analysis of participants' generalisation activity stands testament to this central thesis. Insights gleaned from this study are presented as practical strategies which support and encourage a multiple representational approach to pattern generalisation in the pedagogical context of the classroom.

The presence of ultraviolet patterns on body, as well as perception of ultraviolet spectrum by special photoreceptors, is part of sensory ecology of many animal species, including reptiles. Most current research discusses the importance of ultraviolet signs in coloration of diurnal species. The aim of our study was to find out what character have the reflective signs in ultraviolet spectrum in overall coloration of common leopard gecko (Eubplepharis macularius) through a digital photography. The reflective pattern is present in both adults and juveniles and passes as well as the rest of the coloration by significant ontogenetic changes. Another aim was to evaluate the role of ultraviolet reflecting signs in the biology of this crepuscular-nocturnal species. We expect that the pattern contributes to their antipredatory strategies during their first few months of life, and also a white reflecting surface is preserved in adulthood, especially on their tail, which is differently coloured than the rest of the body.

A busca por métodos de leitura biométrica tem crescido muito, alimentada pelas necessidades governamentais, militares e comerciais. Pesquisas indicam que o mercado de reconhecimento facial vai movimentar bilhões de dólares nos próximos anos. Dessa forma, encontrar métodos que atendem situações específicas impulsiona novos avanços nessa área. Cada aplicação de reconhecimento de faces precisa de uma solução particular. Há casos que o tempo de resposta é o fator mais importante; outros exigem que a face seja classificada mesmo que de forma parcial. Em todas essas situações, a acurácia e a robustez talvez sejam os atributos mais importantes. Entretanto, na maioria das vezes, tais características se comportam como grandezas inversas: aumentado o grau de confiança dos resultados o desempenho do método será afetado. Por isso, desenvolver uma metodologia que equilibra tais fatores é essencial para a construção de soluções aceitáveis. Este trabalho apresenta um novo algoritmo de reconhecimento facial, baseado em descritores locais e em redes complexas. O método é capaz de concentrar a informação, antes distribuída pelos diversos pontos dos descritores, em um único vetor de características, tornando a classificação mais rápida e eficiente. Além disso, o outro foco da metodologia é reduzir etapas de pré-processamento, evitando que processos sejam executados de forma desnecessária. Os experimentos foram realizados com bancos de faces bem conhecidos na literatura, revelando taxas de acurácia de até 98,5%. A técnica também apresentou bons resultados mesmo quando havia ruídos nas amostras, muitas vezes oriundos de objetos presentes na composição do cenário. Para uma análise complementar, algoritmos clássicos de reconhecimento facial foram submetidos ao mesmo conjunto de dados, gerando assim resultados comparativos entre as metodologias. / The search for biometric scanning methods has grown a lot due to government, military and commercial needs. Researches indicate the face recognition market will move billions of dollars in next years. Thus, finding methods to specific situations drives new advances in this area. Each application face recognition requires a particular solution. There are cases the response time is the most important factor; others require that face must be classified even if partially. In all these situations, accuracy and robustness may be the most important attributes. However, in most cases, these features behave as inverse greatness: increasing the confidence level of the results the method performance will be affected. Therefore, create the method which balances these factors is essential for construction of acceptable solutions. This paper presents a new face recognition algorithm based on local descriptors and complex networks. The method is able to concentrate the information before distributed by various point descriptors, in a unique feature vector. It makes the classification step faster and more efficient. Furthermore, another focus of the method is reduce pre-processing steps, avoiding unnecessary processes. The experiments were conducted with faces datasets well known in the literature, revealing accuracy rates of up to 98.5%. The technique also showed good results when there was noise in the samples, often derived from objects present in the composition of the scene. For additional analysis, classical facial recognition algorithms were subjected to the same data set, generating comparative results between both methodologies.

A busca por métodos de leitura biométrica tem crescido muito, alimentada pelas necessidades governamentais, militares e comerciais. Pesquisas indicam que o mercado de reconhecimento facial vai movimentar bilhões de dólares nos próximos anos. Dessa forma, encontrar métodos que atendem situações específicas impulsiona novos avanços nessa área. Cada aplicação de reconhecimento de faces precisa de uma solução particular. Há casos que o tempo de resposta é o fator mais importante; outros exigem que a face seja classificada mesmo que de forma parcial. Em todas essas situações, a acurácia e a robustez talvez sejam os atributos mais importantes. Entretanto, na maioria das vezes, tais características se comportam como grandezas inversas: aumentado o grau de confiança dos resultados o desempenho do método será afetado. Por isso, desenvolver uma metodologia que equilibra tais fatores é essencial para a construção de soluções aceitáveis. Este trabalho apresenta um novo algoritmo de reconhecimento facial, baseado em descritores locais e em redes complexas. O método é capaz de concentrar a informação, antes distribuída pelos diversos pontos dos descritores, em um único vetor de características, tornando a classificação mais rápida e eficiente. Além disso, o outro foco da metodologia é reduzir etapas de pré-processamento, evitando que processos sejam executados de forma desnecessária. Os experimentos foram realizados com bancos de faces bem conhecidos na literatura, revelando taxas de acurácia de até 98,5%. A técnica também apresentou bons resultados mesmo quando havia ruídos nas amostras, muitas vezes oriundos de objetos presentes na composição do cenário. Para uma análise complementar, algoritmos clássicos de reconhecimento facial foram submetidos ao mesmo conjunto de dados, gerando assim resultados comparativos entre as metodologias. / The search for biometric scanning methods has grown a lot due to government, military and commercial needs. Researches indicate the face recognition market will move billions of dollars in next years. Thus, finding methods to specific situations drives new advances in this area. Each application face recognition requires a particular solution. There are cases the response time is the most important factor; others require that face must be classified even if partially. In all these situations, accuracy and robustness may be the most important attributes. However, in most cases, these features behave as inverse greatness: increasing the confidence level of the results the method performance will be affected. Therefore, create the method which balances these factors is essential for construction of acceptable solutions. This paper presents a new face recognition algorithm based on local descriptors and complex networks. The method is able to concentrate the information before distributed by various point descriptors, in a unique feature vector. It makes the classification step faster and more efficient. Furthermore, another focus of the method is reduce pre-processing steps, avoiding unnecessary processes. The experiments were conducted with faces datasets well known in the literature, revealing accuracy rates of up to 98.5%. The technique also showed good results when there was noise in the samples, often derived from objects present in the composition of the scene. For additional analysis, classical facial recognition algorithms were subjected to the same data set, generating comparative results between both methodologies.

The emerging interests in spatial pattern mining leads to the demand for a flexible spatial pattern mining language, on which easy to use and understand visual pattern language could be built. It is worthwhile to define a pattern mining language called LCSPM to allow users to specify complex spatial patterns. I describe a proposed pattern mining language in this paper. A visual interface which allows users to specify the patterns visually is developed. Visual pattern queries are translated into the LCSPM language by a parser and data mining process can be triggered afterwards. The visual language is based on and goes beyond the visual language proposed in literature. I implemented a prototype system based on the open source JUMP framework.

Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I have tested and discussed the choices and roles of parameters. By using correlation of neighboring points directly, statistic distribution of normal vectors at each point has been effectively used to determine the category of the selected point. (II). Segmentation and 3D reconstruction of objects based on LiDAR/MRI. The proposed method includes that the 3D LiDAR data are layered, that different categories are segmented, and that 3D canopy surfaces of individual tree crowns and clusters of trees are reconstructed from LiDAR point data based on a region active contour model. The proposed method allows for delineations of 3D forest canopy naturally from the contours of raw LiDAR point clouds. The proposed model is suitable not only for a series of ideal cone shapes, but also for other kinds of 3D shapes as well as other kinds dataset such as MRI. (III). Novel algorithms for recognition of objects based on LiDAR/MRI. Aimed to the sparse LiDAR data, the feature extraction algorithm has been proposed and applied to classify the building and trees. More importantly, the novel algorithms based on level set methods have been provided and employed to recognize not only the buildings and trees, the different trees (e.g. Oak trees and Douglas firs), but also the subthalamus nuclei (STNs). By using the novel algorithms based on level set method, a 3D model of the subthalamus nuclei (STNs) in the brain has been successfully reconstructed based on the statistical data of previous investigations of an anatomy atlas as reference. The 3D rendering of the subthalamic nuclei and the skull directly from MR imaging is also utilized to determine the 3D coordinates of the STNs in the brain. In summary, the novel methods and algorithms of segmentation, recognition and 3D reconstruction of objects have been proposed. The related experiments have been done to test and confirm the validation of the proposed methods. The experimental results also demonstrate the accuracy, efficiency and effectiveness of the proposed methods. A framework for segmentation, recognition and 3D reconstruction of objects has been established, which has been applied to many research areas.

This thesis tackles the visual tracking problem as a target contour estimation problem in the face of corrupted measurements. The major aim is to design robust recursive curve filters for accurate contour-based tracking. The state-space representation adopted comprises of a group component and a shape component describing the rigid motion and the non-rigid shape deformation respectively; filtering strategies on each component are then decoupled. The thesis considers two implicit curve descriptors, a classification probability field and the traditional signed distance function, and aims to develop an optimal probabilistic contour observer and locally optimal curve filters. For the former, introducing a novel probabilistic shape description simplifies the filtering problem on the infinite-dimensional space of closed curves to a series of point-wise filtering tasks. The definition and justification of a novel update model suited to the shape space, the derivation of the filtering equations and the relation to Kalman filtering are studied. In addition to the temporal consistency provided by the filtering, extensions involving distributed filtering methods are considered in order to maintain spatial consistency. For the latter, locally optimal closed curve filtering strategies involving curve velocities are explored. The introduction of a local, linear description for planar curve variation and curve uncertainty enables the derivation of a mechanism for estimating the optimal gain associated to the curve filtering process, given quantitative uncertainty levels. Experiments on synthetic and real sequences of images validate the filtering designs.

This thesis deals with image segmentation problems that arise in various computer vision related fields such as medical imaging, satellite imaging, video surveillance, recognition and robotic vision. More specifically, this thesis deals with a special class of image segmentation technique called Snakes or Active Contour Models. In active contour models, image segmentation is posed as an energy minimization problem, where an objective energy function (based on certain image related features) is defined on the segmenting curve (contour). Typically, a gradient descent energy minimization approach is used to drive the initial contour towards a minimum for the defined energy. The drawback associated with this approach is that the contour has a tendency to get stuck at undesired local minima caused by subtle and undesired image features/edges. Thus, active contour based curve evolution approaches are very sensitive to initialization and noise. The central theme of this thesis is to develop techniques that can make active contour models robust against certain classes of local minima by incorporating global information in energy minimization. These techniques lead to energy minimization with global considerations; we call these models -- 'Non-local active contours'. In this thesis, we consider three widely used active contour models: 1) Edge- and region-based segmentation model, 2) Prior shape knowledge based segmentation model, and 3) Motion segmentation model. We analyze the traditional techniques used for these models and establish the need for robust models that avoid local minima. We address the local minima problem for each model by adding global image considerations.

Understanding of the shape and size of different features of human body from the scanned data is necessary for automated design and evaluation of product ergonomics. The traditional method of finding required body dimensions by manual measurements (Anthropometry) has many sociological, logistical and technical drawbacks such as prolonged time, skilled researcher for consistency and accuracy of measurements, undesirable physical contact between the subject and the researcher, required presence of people from different demographic categories or travel of researcher with equipments. If these di- mensions are extracted from the stored digital human models, above drawbacks can be eliminated. With the emergence of laser based 3d scanners, it is now possible generate a large database of surface models of humans from different demographic backgrounds but the automatic processing of 3d meshes is under development. Though some commercial packages are available for extraction of a limited number of dimensions from full body scans, mostly belonging to topologically separable body parts like hands and legs, the dimensions associated with head and face are particularly not available in public domain. The processing of surface models of head and face from the automatic measurement point of view is also not discussed in literature though this type of data has many practical applications like ergonomic design of close-fitting products like respiratory masks,ophthalmic frames (spectacles), helmets and similar head-mounted devices; Creation of a facial feature database for face modeling coding and reconstruction and for use in forensic sciences; Automated anthropological surveys and Medical growth analysis and aesthetic surgery planning. Hence, in this thesis, a computational framework is developed for automatic detection, recognition and measurement of important facial features namely eyes, eyebrows, nose, mouth and moustache (if applicable) from scanned head and shoulder polyhedral models. After preprocessing the scanned mesh manually to fill holes and remove singular vertices, discrete differential geometric operators were implemented to compute surface normals and curvatures. Mean curvature magnitude was used as the primary metric to segment the mesh using morphological watershed algorithms which treat the mesh as a height map and separate the regions according to the water catchment basins. After visualization it was hypothesized that the important facial features consist of relatively high curvature regions and based on this hypothesis a much faster approach was then employed based on mathematical morphology to group the high curvature vertices into regions based on adjacency. The important feature regions isolated this way were then identified and labeled to be belonging to different facial features by a decision tree based on their relative spatial disposition. Adaptive selection of parameters was incorporated later to ensure robustness of this algorithm. Critical points of these identified features are recognized as the standard landmarks associated with those primary facial features. A number of clinically identified landmarks lie on the facial mid-line. An efficient algorithm is proposed for detection and processing of the mid-line using a point sampling technique which is fast and has immunity to noise in the data. An algorithm to find shortest path between two vertices while traveling along the edges is implemented to measure on-surface distances and to isolate the nose. Complete program comprising of curvature and surface normal computations, seg- mentation and identification of 6 important features, facial mid-line processing, detection of total 17 landmarks and shortest path computations to separate nose takes about 2 minutes to work including visualization on a full resolution mesh of typically 2,15,521 Vertices and 4,30,560 Faces. The algorithm was tested successfully on more than 40 faces with minor exceptions. The results match human perception. The computed measurements were also compared with the physical measurements for a few subjects, the measurements were found to be in good agreement and satisfactory for its usage in product ergonomics and clinical applications.

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory.