O LiDAR (Light Detection And Ranging) vem-se consolidando como tecnologia de mapeamento, contribuindo com a ciência da informação geográfica. Este trabalho fez uma revisão do estado da arte da tecnologia LiDAR aerotransportado ou ALS (Airborne Laser Scanner), em constante mudança e aperfeiçoamento, no que diz respeito aos sistemas sensores e a estrutura de armazenamento das informações adquiridas. Inicialmente foi apresentado um panorama da utilização do LiDAR aerotransportado na produção de modelos digitais de elevação, em levantamentos de linhas de transmissão, no setor de transportes, e foi dada ênfase à tarefa de extração da vegetação e de edificações, detectando também o solo exposto. Para a extração de edificações, foram apresentados diversos conceitos desenvolvidos nos últimos quatro anos. Na parte prática foi utilizada uma região de teste para comparar feições urbanas obtidas pela classificação automática, realizada pelo software TerraScan, com feições homólogas provenientes de uma base cartográfica de referência, mostrando convergências e divergências entre os dois produtos. Foi realizada uma análise de declividade para determinação de bordas das edificações e, com isso, realizar a segmentação dessas feições. Foi realizado um controle de qualidade cartográfica do produto LiDAR que pudesse classificar esse produto quanto ao padrão de exatidão cartográfica digital. O produto obtido pelo LiDAR atendeu às classes B, C e D da nova norma brasileira a partir da escala 1:10.000. Também foi proposto e realizado o controle de qualidade altimétrico a partir das curvas de nível do produto cartográfico de referência. Recomenda-se a utilização cuidadosa desse produto em função da escala do mapeamento e das necessidades do usuário. / LiDAR (Light Detection And Ranging) has been consolidated as a mapping technology, contributing to the science of geographic information. This paper reviewed the state of the art of the LiDAR airborne technology or ALS (Airborne Laser Scanner), in constant change and improvement, with respect to the sensors and systems structure for storing acquired information. Initially, an overview was presented regarding the use of airborne LiDAR in producing digital elevation models, in surveys of transmission lines and the transportation sector. Emphasis was given to the task of extracting vegetation and buildings, also detecting the exposed soil. For the extraction of buildings, many concepts developed over the past four years were presented. In the practical part, a region test was used to compare the urban features obtained by the automatic classification performed by TerraScan software, with corresponding features from a cartographic reference product, showing similarities and differences between them. An analysis to determine the slope of the edges of the buildings was accomplished and, therefore, the segmentation of these features. The quality control of cartographic LiDAR product was performed in order to classify this product as the standard for digital cartographic accuracy. The product obtained by LiDAR met classes B, C and D of the new Brazilian standard in the 1:10,000 scale. Quality control of altimetry from the curves of the cartographic reference product level was also proposed and performed. We recommend the careful use of the product depending on the scale of the mapping and on users needs.

With the advance of the modern information society, the importance of reliable identity authentication has increased dramatically. Using biometrics as a means for verifying the identity of a person increases both the security and the convenience of the systems. By using yourself to verify your identity such risks as lost keys and misplaced passwords are removed and by virtue of this, convenience is also increased. The most mature and well-developed biometric technique is fingerprint recognition. Fingerprints are unique for each individual and they do not change over time, which is very desirable in this application. There are multitudes of approaches to fingerprint recognition, most of which work by identifying so called minutiae and match fingerprints based on these. In this diploma work, two alternative methods for locating distinct features in fingerprint images have been evaluated. The Template Correlation Method is based on the correlation between the image and templates created to approximate the homogenous ridge/valley areas in the fingerprint. The high-dimension of the feature vectors from correlation is reduced through principal component analysis. By visualising the dimension reduced data by ordinary plotting and observing the result classification is performed by locating anomalies in feature space, where distinct features are located away from the non-distinct. The Circular Sampling Method works by sampling in concentric circles around selected points in the image and evaluating the frequency content of the resulting functions. Each images used here contains 30400 pixels which leads to sampling in many points that are of no interest. By selecting the sampling points this number can be reduced. Two approaches to sampling points selection has been evaluated. The first restricts sampling to occur only along valley bottoms of the image, whereas the second uses orientation histograms to select regions where there is no single dominant direction as sampling positions. For each sampling position an intensity function is achieved by circular sampling and a frequency spectrum of this function is achieved through the Fast Fourier Transform. Applying criteria to the relationships of the frequency components classifies each sampling location as either distinct or non-distinct. Using a cyclic approach to evaluate the methods and their potential makes selection at various stages possible. Only the Circular Sampling Method survived the first cycle, and therefore all tests from that point on are performed on thismethod alone. Two main errors arise from the tests, where the most prominent being the number of spurious points located by the method. The second, which is equally serious but not as common, is when the method misclassifies visually distinct features as non-distinct. Regardless of the problems, these tests indicate that the method holds potential but that it needs to be subject to further testing and optimisation. These tests should focus on the three main properties of the method: noise sensitivity, radial dependency and translation sensitivity.

Quantitative cancer imaging is an emerging field that develops computational techniques to acquire a deep understanding of cancer characteristics for cancer diagnosis and clinical decision making. The recent emergence of growing clinical imaging data provides a wealth of opportunity to systematically explore quantitative information to advance cancer diagnosis. Crucial questions arise as to how we can develop specific computational models that are capable of mining meaningful knowledge from a vast quantity of imaging data and how to transform such findings into improved personalized health care? This dissertation presents a set of computational models in the context of malignant brain tumors— Giloblastoma Multiforme (GBM), which is notoriously aggressive with a poor survival rate. In particular, this dissertation developed quantitative feature extraction approaches for tumor diagnosis from magnetic resonance imaging (MRI), including a multi-scale local computational feature and a novel regional habitat quantification analysis of tumors. In addition, we proposed a histogram-based representation to investigate biological features to characterize ecological dynamics, which is of great clinical interest in evaluating tumor cellular distributions. Furthermore, in regards to clinical systems, generic machine learning techniques are typically incapable of generalizing well to specific diagnostic problems. Therefore, quantitative analysis from a data-driven perspective is becoming critical. In this dissertation, we propose two specific data-driven models to tackle different types of clinical MRI data. First, we inspected cancer systems from a time-domain perspective. We propose a quantitative histogram-based approach that builds a prediction model, measuring the differences from pre- and post-treatment diagnostic MRI data. Second, we investigated the problem of mining knowledge from a skewed distribution—data samples of each survival group are unequally distributed. We proposed an algorithmic framework to effectively predict survival groups by jointly considering imbalanced distributions and classifier design. Our approach achieved an accuracy of 95.24%, suggesting it captures class-specific information in a challenging clinical setting.

Abstract Communicating with mobile devices has become an unavoidable part of our daily life. Unfortunately, the current user interface designs are mostly taken directly from desktop computers. This has resulted in devices that are sometimes hard to use. Since more processing power and new sensing technologies are already available, there is a possibility to develop systems to communicate through different modalities. This thesis proposes some novel computer vision approaches, including head tracking, object motion analysis and device ego-motion estimation, to allow efficient interaction with mobile devices. For head tracking, two new methods have been developed. The first method detects a face region and facial features by employing skin detection, morphology, and a geometrical face model. The second method, designed especially for mobile use, detects the face and eyes using local texture features. In both cases, Kalman filtering is applied to estimate the 3-D pose of the head. Experiments indicate that the methods introduced can be applied on platforms with limited computational resources. A novel object tracking method is also presented. The idea is to combine Kalman filtering and EM-algorithms to track an object, such as a finger, using motion features. This technique is also applicable when some conventional methods such as colour segmentation and background subtraction cannot be used. In addition, a new feature based camera ego-motion estimation framework is proposed. The method introduced exploits gradient measures for feature selection and feature displacement uncertainty analysis. Experiments with a fixed point implementation testify to the effectiveness of the approach on a camera-equipped mobile phone. The feasibility of the methods developed is demonstrated in three new mobile interface solutions. One of them estimates the ego-motion of the device with respect to the user's face and utilises that information for browsing large documents or bitmaps on small displays. The second solution is to use device or finger motion to recognize simple gestures. In addition to these applications, a novel interactive system to build document panorama images is presented. The motion estimation and recognition techniques presented in this thesis have clear potential to become practical means for interacting with mobile devices. In fact, cameras in future mobile devices may, for the most of time, be used as sensors for self intuitive user interfaces rather than using them for digital photography.

Frequently monitoring the identity of a person connected to a secure system is an important component in a cyber-security system. Identity Assurance (IA) mechanisms which continuously confirm and verify users’ identity after the initial authentication process ensure integrity and security. Such systems prevent unauthorized access and eliminate the need of an authorized user to present credentials repeatedly for verification. Very few cyber-security systems deploy such IA modules. These IA modules are typically based on computer vision and machine learning algorithms. These algorithms work effectively when trained with representative datasets. This thesis describes our effort at collecting a small dataset of multi-view videos of typical work session of several subjects to serve as a resource for other researchers of IA algorithms to evaluate and compare the performance of their algorithms with those of others. We also present a Proof of Concept (POC) face matching algorithm and experimental results with this POC implementation for a subset of collected dataset.

Given the strong association between aberrant nuclear morphology and tumor progression, changes in nuclear structure have remained the gold standard for cancer diagnosis for over 150 years. Recently, the rapid development of imaging hardware and computation power creates the opportunity for automated computer-aided diagnosis (CAD). Developing a robust and reliable pattern recognition pipeline is a pressing need to mine and analyze tons of nuclei data being captured. Among the rich studies on pattern recognition problems in pathology, automated nuclei detection, segmentation and cancer detection are the recurring tasks due to the importance and challenges of nuclei analysis. In this thesis, we propose and investigate the state-of-art methods in the CAD modules for maximizing the overall amount of information from images for decision making. We focus on nuclei segmentation and patient cancer detection in the nuclei image analysis pipeline. As the first step in nuclei analysis, we develop an unsupervised nuclei detection and segmentation approach for pathology images. Different from many supervised segmentation methods whose performances rely on the quality and quantity of training samples, the proposed method is able to automatically search for the nucleus contour by solving the shortest path problem with little user effort. We consider the cancer detection task as a set classification problem and propose a highly discriminative predictive model in the sense that it not only optimizes the classifier decision boundary but also transfers discriminative information to set representation learning. The innovation of the model is the integration of set representation learning and classifier training into one objective function for boosting the cancer detection performance. Experimental results showed that the new model provides significant improvements compared with state-of-art methods in the diagnostic challenges. In addition, we showed that the predictive model enables visual interpretation of discriminative nuclear characteristics representing the whole nuclei set. We believe the proposed model is quite general and provide experimental validations in several extended pattern recognition problems.

This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.

Crack initiation and propagation in the bladed disks of aero-engines caused by high-cycle fatigue under cyclic loads could result in the breakdown of the engines if not detected at an early stage. Although a number of fault detection methods have been reported in the literature, it still remains very challenging to develop a reliable online technique to accurately diagnose defects in bladed disks. One of the main challenges is to characterize signals contaminated by noises. These noises caused by very dynamic engine operation environment. This work presents a new technique for engine bladed disk crack detection, which utilizes advanced analysis of clearance and time-of-arrival signals acquired from blade tip sensors. This technique involves two stages of signal processing: 1) signal pre-processing for noise elimination from predetermined causes; and 2) signal post-processing for characterizing crack initiation and location. Experimental results from the spin rig test were used to validate technique predictions.

We estimate the velocity field of the blood flow in a human face from videos. Our approach first performs spatial preprocessing to improve the signal-to-noise ratio (SNR) and the computational efficiency. The discrete Fourier transform (DFT) and a temporal band-pass filter are then applied to extract the frequency corresponding to the subjects heart rate. We propose multiple kernel based k-NN classification for removing the noise positions from the resulting phase and amplitude maps. The 2D blood flow field is then estimated from the relative phase shift between the pixels. We evaluate our approach about segmentation as well as velocity field on real and synthetic face videos. Our method produces the recall and precision as well as a velocity field with an angular error and magnitude error on the average.

Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semi-automatic road network extraction techniques have significantly increased the extraction rate of road networks. Automated processes still yield some erroneous and incomplete results and costly human intervention is still required to evaluate results and correct errors. With the aim of improving the accuracy of road extraction systems, three objectives are defined in this thesis: Firstly, the study seeks to develop a flexible semi-automated road extraction system, capable of extracting roads from QuickBird satellite imagery. The second objective is to integrate a variety of algorithms within the road network extraction system. The benefits of using each of these algorithms within the proposed road extraction system, is illustrated. Finally, a fully automated system is proposed by incorporating a number of the algorithms investigated throughout the thesis. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Computer Science / unrestricted