AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

SEARCHING HETEROGENEOUS DOCUMENT IMAGE COLLECTIONS

A decrease in data storage costs and widespread use of scanning devices has led to massive quantities of scanned digital documents in corporations, organizations, and governments around the world. Automatically processing these large heterogeneous collections can be difficult due to considerable variation in resolution, quality, font, layout, noise, and content. In order to make this data available to a wide audience, methods for efficient retrieval and analysis from large collections of document images remain an open and important area of research. In this proposal, we present research in three areas that augment the current state of the art in the retrieval and analysis of large heterogeneous document image collections. First, we explore an efficient approach to document image retrieval, which allows users to perform retrieval against large image collections in a query-by-example manner. Our approach is compared to text retrieval of OCR on a collection of 7 million document images collected from lawsuits against tobacco companies. Next, we present research in document verification and change detection, where one may want to quickly determine if two document images contain any differences (document verification) and if so, to determine precisely what and where changes have occurred (change detection). A motivating example is legal contracts, where scanned images are often e-mailed back and forth and small changes can have severe ramifications. Finally, approaches useful for exploiting the biometric properties of handwriting in order to perform writer identification and retrieval in document images are examined

Automatic interpretation of clock drawings for computerised assessment of dementia

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test has potential to improve test accessibility and accuracy. CDT sketch interpretation is one of the first stages in the analysis of the computerised test. It produces a set of recognised digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current interpretation systems perform less well on this kind of data due to its unintelligibility. In this thesis, a novel automatic interpretation system for CDT sketch is proposed and developed. The proposed interpretation system and all the related algorithms developed in this thesis are evaluated using a CDT data set collected for this study. This data consist of two sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from drawings of dementia patients. This thesis has four main contributions. The first is a conceptual model of the proposed CDT sketch interpretation system based on integrating prior knowledge of the expected CDT sketch structure and human reasoning into the drawing interpretation system. The second is a novel CDT sketch segmentation algorithm based on supervised machine learning and a new set of temporal and spatial features automatically extracted from the CDT data. The evaluation of the proposed method shows that it outperforms the current state-of-the-art method for CDT drawing segmentation. The third contribution is a new v handwritten digit recognition algorithm based on a set of static and dynamic features extracted from handwritten data. The algorithm combines two classifiers, fuzzy k-nearest neighbour’s classifier with a Convolutional Neural Network (CNN), which take advantage both of static and dynamic data representation. The proposed digit recognition algorithm is shown to outperform each classifier individually in terms of recognition accuracy. The final contribution of this study is the probabilistic Situational Bayesian Network (SBN), which is a new hierarchical probabilistic model for addressing the problem of fusing diverse data sources, such as CDT sketches created by healthy volunteers and dementia patients, in a probabilistic Bayesian network. The evaluation of the proposed SBN-based CDT sketch interpretation system on CDT data shows highly promising results, with 100% recognition accuracy for heathy CDT drawings and 97.15% for dementia data. To conclude, the proposed automatic CDT sketch interpretation system shows high accuracy in terms of recognising different sketch objects and thus paves the way for further research in dementia and clinical computer-assisted diagnosis of dementia

A Likelihood-Ratio Based Forensic Voice Comparison in Standard Thai

This research uses a likelihood ratio (LR) framework to assess the discriminatory power of a range of acoustic parameters extracted from speech samples produced by male speakers of Standard Thai. The thesis aims to answer two main questions: 1) to what extent the tested linguistic-phonetic segments of Standard Thai perform in forensic voice comparison (FVC); and 2) how such linguistic-phonetic segments are profitably combined through logistic regression using the FoCal Toolkit (Brümmer, 2007). The segments focused on in this study are the four consonants /s, ʨh, n, m/ and the two diphthongs [ɔi, ai]. First of all, using the alveolar fricative /s/, two different sets of features were compared in terms of their performance in FVC. The first comprised the spectrum-based distributional features of four spectral moments, namely mean, variance, skew and kurtosis; the second consisted of the coefficients of the Discrete Cosine Transform (DCTs) applied to a spectrum. As DCTs were found to perform better, they were subsequently used to model the consonant spectrum of the remaining consonants. The consonant spectrum was extracted at the center point of the /s, ʨh, n, m/ consonants with a Hamming window of 31.25 msec. For the diphthongs [ɔi] - [nɔi L] and [ai] - [mai HL], the cubic polynomials fitted to the F2 and F1-F3 formants were tested separately. The quadratic polynomials fitted to the tonal F0 contours of [ɔi] - [nɔi L] and [ai] - [mai HL] were tested as well. Long-term F0 distribution (LTF0) was also trialed. The results show the promising discriminatory power of the Standard Thai acoustic features and segments tested in this thesis. The main findings are as follows. 1. The fricative /s/ performed better with the DCTs (Cllr = 0.70) than with the spectral moments (Cllr = 0.92). 2. The nasals /n, m/ (Cllr = 0.47) performed better than the affricate /tɕh/ (Cllr = 0.54) and the fricative /s/ (Cllr = 0.70) when their DCT coefficients were parameterized. 3. F1-F3 trajectories (Cllr = 0.42 and Cllr = 0.49) outperformed F2 trajectory (Cllr = 0.69 and Cllr = 0.67) for both diphthongs [ɔi] and [ai]. 4. F1-F3 trajectories of the diphthong [ɔi] (Cllr = 0.42) outperformed those of [ai] (Cllr = 0.49). 5. Tonal F0 (Cllr = 0.52) outperformed LTF0 (Cllr = 0.74). 6. Overall, better results were obtained when DCTs of /n/ - [na: HL] and /n/ - [nɔi L] were fused. (Cllr = 0.40 with the largest consistent-with-fact SSLog10LR = 2.53). In light of the findings, we can conclude that Standard Thai is generally amenable to FVC, especially when linguistic-phonetic segments are being combined; it is recommended that the latter procedure be followed when dealing with forensically realistic casework

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

Spatio-Temporal Approaches to Denoising and Feature Extraction in Rapid Image Triage

Ph.DDOCTOR OF PHILOSOPH

Using contour information and segmentation for object registration, modeling and retrieval

This thesis considers different aspects of the utilization of contour information and syntactic and semantic image segmentation for object registration, modeling and retrieval in the context of content-based indexing and retrieval in large collections of images. Target applications include retrieval in collections of closed silhouettes, holistic w ord recognition in handwritten historical manuscripts and shape registration. Also, the thesis explores the feasibility of contour-based syntactic features for improving the correspondence of the output of bottom-up segmentation to semantic objects present in the scene and discusses the feasibility of different strategies for image analysis utilizing contour information, e.g. segmentation driven by visual features versus segmentation driven by shape models or semi-automatic in selected application scenarios. There are three contributions in this thesis. The first contribution considers structure analysis based on the shape and spatial configuration of image regions (socalled syntactic visual features) and their utilization for automatic image segmentation. The second contribution is the study of novel shape features, matching algorithms and similarity measures. Various applications of the proposed solutions are presented throughout the thesis providing the basis for the third contribution which is a discussion of the feasibility of different recognition strategies utilizing contour information. In each case, the performance and generality of the proposed approach has been analyzed based on extensive rigorous experimentation using as large as possible test collections

The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network