Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerised histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each subband provides different information pertaining the texture in the image sample. Our novel method, the Adaptive Discriminant Wavelet Packet Transform (ADWPT), provides a means for ...

Intravascular ultrasound has, over the past decade, significantly changed the clinical diagnosis and therapeutic strategy of coronary and vascular disease assessment, as it not only allows visualisation of the vessel lumen, but gives a unique view of the pathophysiologic structure of the artery wall. This information is currently unavailable from the universally accepted instrument for artery assessment, angiography, which has on several occasions had its diagnostic accuracy questioned. With intravascular ultrasound, there is the potential to categorise diseased arterial tissue belonging to distinct pathological groups which can ultimately aid in the understanding of individual lesions as well as making a significant contribution to treatment choice and management of cardiac patients. The high resolution image information offered by intravascular ultrasound provides excellent crosssectional views of coronary artery disease at the level of the disease process itself. This information can be used ...

The objectives of the research were to develop embedded and non-embedded lossy coding algorithms for images based on lattice vector quantisation and the discrete wavelet transform. We also wanted to develop context-based entropy coding methods (as opposed to simple first order entropy coding). The main objectives can therefore be summarised as follows: (1) To develop algorithms for intra and inter-band formed vectors (vectors with coefficients from the same sub-band or across different sub-bands) which compare favourably with current high performance wavelet based coders both in terms of rate/distortion performance of the decoded image and also subjective quality; (2) To develop new context-based coding methods (based on vector quantisation). The alternative algorithms we have developed fall into two categories: (a) Entropy coded and Binary uncoded successive approximation lattice vector quantisation (SALVQ- E and SA-LVQ-B) based on quantising vectors formed intra-band. This ...

The increasing need for personal authentication in many daily applications has made biometrics a fundamental research area. In particular, handwritten signatures have long been considered one of the most valuable biometric traits. Signatures are the most popular method for identity verification all over the world, and people are familiar with the use of signatures for identity verification purposes in their everyday life. In fact, signatures are widely used in several daily transactions, being recognized as a legal means of verifying an individual’s identity by financial and administrative institutions. In addition, signature verification has the advantage of being a non-invasive biometric technique. Two categories of signature verification systems can be distinguished taking into account the acquisition device, namely, offline systems, where only the static image of the signature is available, and online systems, where dynamic information acquired during the signing process, ...

The interest for the intelligent vehicle field has been increased during the last years, must probably due to an important number of road accidents. Many accidents could be avoided if a device attached to the vehicle would assist the driver with some warnings when dangerous situations are about to appear. In recent years, leading car developers have recorded significant efforts and support research works regarding the intelligent vehicle field where they propose solutions for the existing problems, especially in the vision domain. Road detection and following, pedestrian or vehicle detection, recognition and tracking, night vision, among others are examples of applications which have been developed and improved recently. Still, a lot of challenges and unsolved problems remain in the intelligent vehicle domain. Our purpose in this thesis is to design an Obstacle Recognition system for improving the road security by ...

Current Human-Machine Interfaces (HMI) lack of “emotional intelligence”, i.e. they are not able to identify human emotional states and take this information into account to decide on the proper actions to execute. The goal of affective computing is to fill this lack by detecting emotional cues occurring during Human-Computer Interaction (HCI) and synthesizing emotional responses. In the last decades, most of the studies on emotion assessment have focused on the analysis of facial expressions and speech to determine the emotional state of a person. Physiological activity also includes emotional information that can be used for emotion assessment but has received less attention despite of its advantages (for instance it can be less easily faked than facial expressions). This thesis reports on the use of two types of physiological activities to assess emotions in the context of affective computing: the activity ...

Signature verification deals with the problem of identifying forged signatures of a user from his/her genuine signatures. The difficulty lies in identifying allowed variations in a user’s signatures, in the presence of high intra-class and low inter-class variability (the forgeries may be more similar to a user’s genuine signature, compared to his/her other genuine signatures). The problem can be seen as a non-rigid object matching where classes are very similar. In the field of biometrics, signature is considered a behavioral biometric and the problem possesses further difficulties compared to other modalities (e.g. fingerprints) due to the added issue of skilled forgeries. A novel offline (image-based) signature verification system is proposed in this thesis. In order to capture the signature’s stable parts and alleviate the difficulty of global matching, local features (histogram of oriented gradients, local binary patterns) are used, based ...

The purpose of this thesis is to describe novel signal processing methodologies and analog integrated circuit techniques for low-power biomedical systems. Physiological signals, such as the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG) are mostly non-stationary. The main difficulty in dealing with biomedical signal processing is that the information of interest is often a combination of features that are well localized temporally (e.g., spikes) and others that are more diffuse (e.g., small oscillations). This requires the use of analysis methods sufficiently versatile to handle events that can be at opposite extremes in terms of their time-frequency localization. Wavelet Transform (WT) has been extensively used in biomedical signal processing, mainly due to the versatility of the wavelet tools. The WT has been shown to be a very efficient tool for local analysis of nonstationary and fast transient signals due ...

Cellular Neural Networks are characterized by simplicity of operation. The network consists of a large number of nonlinear processing units; called cells; that are equally spread in the space. Each cell has a simple function (sequence of multiply-add followed by a single discrimination) that takes an element of a topographic map and then interacts with all cells within a specified sphere of interest through direct connections. Due to their intrinsic parallel computing power, CNNs have attracted the attention of a wide variety of scientists in, e.g., the fields of image and video processing, robotics and higher brain functions. Simplicity of operation together with the local connectivity gives CNNs first-hand advantages for tiled VLSI implementations with very high speed and complexity. The first VLSI implementation has been based on analogue technology but was small and suffered from parasitic capacitances and resistances ...

In this work, we study the application of wavelet analysis for robust speech processing. Reliable time-scale features (TS) which characterize the relevant phonetic classes such as voiced (V), unvoiced (UV), silence (S), mixed-excitation, and stop sounds are extracted. By training neural and Bayesian networks, the classification rates provided by only 7 TS features are mostly similar to the ones obtained by 13 MFCC features. The TS features are further enhanced to design a reliable and low-complexity V/UV/S classifier. Quantile filtering and slope tracking are used for deriving adaptive thresholds. A robust voice activity detector is then built and used as a pre-processing stage to improve the performance of a speaker verification system. Based on wavelet shrinkage, a statistical wavelet filtering (SWF) method is designed for speech enhancement. Non-stationary and colored noise is handled by employing quantile filtering and time-frequency adaptive ...

Medical and holographic imaging modalities produce large datasets that require efficient compression mechanisms for storage and transmission. This PhD dissertation proposes state-of-the-art technology extensions for JPEG coding standards to improve their performance in the aforementioned application domains. Modern hospitals rely heavily on volumetric images, such as produced by CT and MRI scanners. In fact, the completely digitized medical work flow, the improved imaging scanner technologies and the importance of volumetric image data sets have led to an exponentially increasing amount of data, raising the necessity for more efficient compression techniques with support for progressive quality and resolution scalability. For this type of imagery, a volumetric extension of the JPEG 2000 standard was created, called JP3D. In addition, improvements to JP3D, being alternative wavelet filters, directional wavelets and an intra-band prediction mode, were proposed and their applicability was evaluated. Holographic imaging, ...

The work presented in this thesis covers topics that extend the scalability functionalities in video coding and improve the compression performance. Two main novel approaches are presented, each targeting a different part of the scalable video coding (SVC) architecture: motion adaptive wavelet transform based on the wavelet transform in lifting implementation, and a design of a flexible framework for generalised spatio-temporal decomposition. Motion adaptive wavelet transform is based on the newly introduced concept of connectivity-map. The connectivity-map describes the underlying irregular structure of regularly sampled data. To enable a scalable representation of the connectivity-map, the corresponding analysis and synthesis operations have been derived. These are then employed to define a joint wavelet connectivity-map decomposition that serves as an adaptive alternative to the conventional wavelet decomposition. To demonstrate its applicability, the presented decomposition scheme is used in the proposed SVC framework, ...

The development of a fully unsupervised algorithm to achieve image segmentation is the central theme of this dissertation. Existing literature falls short of such a goal providing many algorithms capable of solving a subset of this highly challenging problem. Unsupervised segmentation is the process of identifying and locating the constituent regions of an observed image, while having no prior knowledge of the number of regions. The problem can be formulated in a Bayesian framework and through the use of an assumed model unsupervised segmentation can be posed as a problem of optimisation. This is the approach pursued throughout this dissertation. Throughout the literature, the commonly adopted model is an hierarchical image model whose underlying components are various forms of Markov Random Fields Gaussian. Markov Random Field models are used to model the textural content of the observed images regions, while ...

The present doctoral thesis aims towards the development of new long-term, multi-channel, audio-visual processing techniques for the analysis of bioacoustics phenomena. The effort is focused on the study of the physiology of the gastrointestinal system, aiming at the support of medical research for the discovery of gastrointestinal motility patterns and the diagnosis of functional disorders. The term "processing" in this case is quite broad, incorporating the procedures of signal processing, content description, manipulation and analysis, that are applied to all the recorded bioacoustics signals, the auxiliary audio-visual surveillance information (for the monitoring of experiments and the subjects' status), and the extracted audio-video sequences describing the abdominal sound-field alterations. The thesis outline is as follows. The main objective of the thesis, which is the technological support of medical research, is presented in the first chapter. A quick problem definition is initially ...

This thesis contributes to motion characterization through inertial and physiological signals captured by wearable devices and analyzed using signal processing and deep learning techniques. This research leverages the possibilities of motion analysis for three main applications: to know what physical activity a person is performing (Human Activity Recognition), to identify who is performing that motion (user identification) or know how the movement is being performed (motor anomaly detection). Most previous research has addressed human motion modeling using invasive sensors in contact with the user or intrusive sensors that modify the user’s behavior while performing an action (cameras or microphones). In this sense, wearable devices such as smartphones and smartwatches can collect motion signals from users during their daily lives in a less invasive or intrusive way. Recently, there has been an exponential increase in research focused on inertial-signal processing to ...