Ground penetrating radar (GPR) is a promising technique for humanitarian demining applications as it permits providing useful information about the subsurface based on wave reflections produced by electromagnetic (EM) contrasts. Yet, landmine detection using GPR can suffer from: (1) clutter, i.e, undesirable effects from antenna coupling, system ringing and soil surface and subsurface reflections; (2) false alarms, e.g., reflections from buried mine-like objects such as stones or metallic debris; (3) effects of soil properties on the GPR performance, such as attenuation. This thesis addresses these topics in an integrated approach aiming at reducing clutter, identifying landmines from false alarms and analysing GPR performance. For subtracting undesirable reflections, a new physically-based filtering algorithm is developed, which takes into account major antenna effects and soil surface reflection. It is applied in conjunction with a change detection algorithm for enhancing landmine detection. Landmine identification is performed using discriminant characteristics extracted from the pre-filtered data by a novel feature extraction approach in the time-frequency domain. For analysing the effects of soil properties, in particular soil dielectric permittivity, an EM model is coupled to pedotransfer functions for estimating the GPR performance on a given soil. The developed algorithms are validated using data acquired by two different hand-held GPR systems. Promising results are obtained under laboratory and outdoor conditions, where different types of soil (including real mine-affected soils) and landmines (including improvised explosive devices) are considered.

Junctions are the intersection points of three or more intensity surfaces in an image. An analysis of zero crossings and the gradient near junctions demonstrates that gradient-based edge detection schemes fragment edges at junctions. This fragmentation is caused by the intrinsic pairing of zero crossings and a destructive interference of edge gradients at junctions. Using the previous gradient analysis, we propose a junction detector that finds junctions in edge maps by following gradient ridges and using the minimum direction of saddle points in the gradient. The junction detector is demonstrated on real imagery and previous approaches to junction detection are discussed.

Ground penetrating radar (GPR) is a promising technique for humanitarian demining applications as it permits providing useful information about the subsurface based on wave reflections produced by electromagnetic (EM) contrasts. Yet, landmine detection using GPR can suffer from: (1) clutter, i.e, undesirable effects from antenna coupling, system ringing and soil surface and subsurface reflections; (2) false alarms, e.g., reflections from buried mine-like objects such as stones or metallic debris; (3) effects of soil properties on the GPR performance, such as attenuation. This thesis addresses these topics in an integrated approach aiming at reducing clutter, identifying landmines from false alarms and analysing GPR performance. For subtracting undesirable reflections, a new physically-based filtering algorithm is developed, which takes into account major antenna effects and soil surface reflection. It is applied in conjunction with a change detection algorithm for enhancing landmine detection. Landmine identification is performed using discriminant characteristics extracted from the pre-filtered data by a novel feature extraction approach in the time-frequency domain. For analysing the effects of soil properties, in particular soil dielectric permittivity, an EM model is coupled to pedotransfer functions for estimating the GPR performance on a given soil. The developed algorithms are validated using data acquired by two different hand-held GPR systems. Promising results are obtained under laboratory and outdoor conditions, where different types of soil (including real mine-affected soils) and landmines (including improvised explosive devices) are considered.

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One of the fields where image processing finds its application but that remains as anunexplored territory is the analysis of cell morphology. This master thesis proposes a systemto carry out this research and sets the necessary technical basis to make it feasible, rangingfrom the processing of time-lapse sequences using image segmentation to the representation,description and classification of cells in terms of morphology. Due to the highly variability of cell morphological characteristics several segmentationmethods have been implemented to face each of the problems encountered: Edge-detection,region-growing and marked watershed were found to be successful processing algorithms.This variability inherent to cells and the fact that human eye has a natural disposition to solvesegmentation problems finally lead to the development of a user-friendly interactiveapplication, the Time Lapse Sequence Processor (TLSP). Although it was initially consideredas a mere interface to perform cell segmentation, TLSP concept has evolved into theconstruction of a complete multifunction tool to perform cell morphology analysis:segmentation, morphological data extraction, analysis and management, cell tracking andrecognition system, etc. In its last version, TLSP v0.2 Alpha contains several segmentationtools, improved user interface and, data extraction and management capabilities. Finally, a wide set of recommendations and improvements have been discussed, pointing the path for future development in this area.

Simultaneous Localization And Mapping (SLAM) is a process of mapping an unknown environment and at the same time keeping track of the position within this map. In this theses, SLAM is performed in a marine environent using radar images only. A SLAM solution is presented. It uses SIFT to compare pairs of radar images. From these comparisons, measurements of the boat movements are obtained. A type of Kalman filter (Exactly Sparse Delayed-state Filter, ESDF) uses these measurements to estimate the trajectory of the boat. Once the trajectory is estimated, the radar images are joined together in order to create a map. The presented solution is tested and the estimated trajectory is compared to GPS data. Results show that the method performs well for at least shorter periods of time.

Diagnosis of stress is important because it can cause many diseases e.g., heart disease, headache, migraine, sleep problems, irritability etc. Diagnosis of stress in patients often involves acquisition of biological signals for example heart rate, finger temperature, electrocardiogram (ECG), electromyography signal (EMG), skin conductance signal (SC) etc. followed up by a careful analysis of the acquired signals. The accuracy is totally dependent on the experience of an expert. Again the number of such experts is also very limited. Heart rate is considered as an important parameter in determining stress. It reflects status of the autonomic nervous system (ANS) and thus is very effective in monitoring any imbalance in patient’s stress level. Therefore, a computer-aided system is useful to determine stress level based on various features that can be extracted from a patient’s heart rate signals. Stress diagnosis using biomedical signals is difficult and since the biomedical signals are too complex to generate any rule an experienced person or expert is needed to determine stress levels. Also, it is not feasible to use all the features that are available or possible to extract from the signal. So, relevant features should be chosen from the extracted features that are capable to diagnose stress. Again, ECG signal is frequently contaminated by outliers produced by the loose conduction of the electrode due to sneezing, itching etcetera that hampers the value of the features. A Case-Based Reasoning (CBR) System is helpful when it is really hard to formulate rule and the knowledge on the domain is also weak. A CBR system is developed to evaluate how closely it can diagnose stress levels compare to an expert. A study is done to find out mostly used features to reduce the number of features used in the system and in case library. A software prototype is developed that can collect ECG signal from a patient through ECG sensor and calculate Inter Beat Interval (IBI) signal and features from it. Instead of doing manual visual inspection a new way to remove outliers from the IBI signal is also proposed and implemented here. The case base has been initiated with 22 reference cases classified by an expert. A performance analysis has been done and the result considering how close the system can perform compare to the expert is presented. On the basis of the evaluations an accuracy of 86% is obtained compare to an expert. However, the correctly classified case for stressed group (Sensitivity) was 57% and it is quite important to increase as it is related to the safety issue of health. The reasons of relatively lower sensitivity and possible ways to improve it are also investigated and explained.

A speech visualization system is proposed thatcould be used by a deaf person for understanding speech.Several novel techniques are proposed, including: (1) Minimizing spectral leakage in the Fourier transform by using avariable-length window. (2) Making use of the fact that there is no spectral leakage in order to calculate how much of the energy of the speech signal is due to its periodic component vs. its nonperiodic component. (3) Modelling the mouth and lips as a band-pass filter and estimating the central frequency and bandwidth of this filter in order to assign colours tounvoiced speech sounds.