Recent interest has emerged in microsensor platforms that are capable of supporting reconnaissance, surveillance and target acquisition operations. These devices typically consist of one or more sensors, signal conditioning and processing subsystems, a radio link and a power source. Sensors employed can range from acoustic, to seismic, to magnetic, to visible/infrared imagers. A notable shortcoming of these systems is the fact that they are battery powered. The use of a finite power source places an upper limit on the lifespan of such a system. Thus, a major thrust in the development and usage of these microsensor platforms lies in the conservation of their limited energy resources. In attempt to reduce power consumption and hence extend the system's lifespan, communication bandwidths are often limited. In order to reduce the required bandwidth, much of the signal processing necessary to achieve a desired functionality must be performed within the microsensor platform itself. This thesis effort provides this crucial bandwidth reduction by implementing in hardware an algorithm developed by the University of Maryland, which limits transmissions to the best view Regions-of-Interest (ROI) data, on the CAuS platform by BAE Systems. The hardware implementation was verified with a Matlab script that compared its results with those of the original algorithm. It was shown that these implementations were consistent for all of the data sets tested. Moreover, a subjective analysis, in which the detected ROIs were visually inspected, was performed to corroborate the former quantitative results. / Master of Science

Social anxiety is a disorder where people fear social interactions and is associated with physiological changes. Eye tracking studies have shown that people with social anxiety spent more time gazing at emotional faces presented on a computer screen and spent more time gazing at the eye region. There has been limited studies on tracking eye gaze in a real-life setting interacting with another person. We used a wearable eye tracker during a brief one-on-one interview about participants’ challenges faced at work or school. Along with self-report psychological measures about social anxiety and shyness, we also measured participants’ salivary cortisol as a metric for physiological stress. We hypothesized that socially anxious individuals would have higher cortisol levels and spent more time gazing at the face. However, there was no change in cortisol levels before and after the interview. In addition, socially anxious individuals had lower cortisol levels than less anxious people. Furthermore, the time spent fixating on the region of interest (ROI), which was the face, was not correlated with social anxiety, anxiety or shyness. Paradoxically, the more socially anxious participants seem to have had a lower physiological stress response than less socially anxious participants.

Volvo Construction Equipment (CE) are facing the challenge of vibrations in their wheel loaders that generate disruptive noise and impact the driver's experience. These vibrations have been linked to the contact surface between the crown wheel and pinion gear in the vehicles drive-axles. In response, this thesis was created to develop an Artificial Intelligence (AI) system, which can identify outliers in a dataset containing images of the contact surfaces between the crown wheel and pinion gear. However, the dataset exhibits variations in image sharpness, exposure and centering of the crown wheel, which hinders its suitability for machine vision tasks. The varying quality of the images poses the challenge of accurately extracting relevant features required to analyze the images through machine learning algorithms. This research aims to address these challenges by investigating two research questions. (1) what method can be employed to extract the Region of Interest (ROI) in images of crown wheels? And (2) which method is suitable for detection of outliers within the ROI? To find answers to these questions, a literature study was conducted leading up to the implementation of two architectures: You Only Look Once (YOLO) v5 Oriented Bounding Boxes (OBB) and a Hybrid Autoencoder (BAE). Visual evaluation of the results showed promising outcomes particularly for the extraction of ROIs, where the relevant areas were accurately identified despite the large variations in image quality. The BAE successfully identified outliers that deviated from the majority, however, the results of the model were influenced by the differences in image quality, rather than the geometrical shape of the contact patterns. These findings suggest that using the same feature extraction method on a higher-quality dataset or employing a more robust segmentation method, could increase the likelihood of identifying the contact patterns responsible for the vibrations.

This thesis addresses authenticity and integrity of medical images using watermarking. Hospital Information Systems (HIS), Radiology Information Systems (RIS) and Picture Archiving and Communication Systems (P ACS) now form the information infrastructure for today's healthcare as these provide new ways to store, access and distribute medical data that also involve some security risk. Watermarking can be seen as an additional tool for security measures. As the medical tradition is very strict with the quality of biomedical images, the watermarking method must be reversible or if not, region of Interest (ROI) needs to be defined and left intact. Watermarking should also serve as an integrity control and should be able to authenticate the medical image. Three watermarking techniques were proposed. First, Strict Authentication Watermarking (SAW) embeds the digital signature of the image in the ROI and the image can be reverted back to its original value bit by bit if required. Second, Strict Authentication Watermarking with JPEG Compression (SAW-JPEG) uses the same principal as SAW, but is able to survive some degree of JPEG compression. Third, Authentication Watermarking with Tamper Detection and Recovery (AW-TDR) is able to localise tampering, whilst simultaneously reconstructing the original image.

Importance prioritised coding is a principle aimed at improving the interpretability (or image content recognition) versus bit-rate performance of image coding systems. This can be achieved by (1) detecting and tracking image content or regions of interest (ROI) that are crucial to the interpretation of an image, and (2)compressing them in such a manner that enables ROIs to be encoded with higher fidelity and prioritised for dissemination or transmission. Traditional image coding systems prioritise image data according to an objective measure of distortion and this measure does not correlate well with image quality or interpretability. Importance prioritised coding, on the other hand, aims to prioritise image contents according to an 'importance map', which provides a means for modelling and quantifying the relative importance of parts of an image. In such a coding scheme the importance in parts of an image containing ROIs would be higher than other parts of the image. The encoding and prioritisation of ROIs means that the interpretability in these regions would be improved at low bit-rates. An importance prioritised image coder incorporated within the JPEG 2000 international standard for image coding, called IMP-J2K, is proposed to encode and prioritise ROIs according to an 'importance map'. The map can be automatically generated using image processing algorithms that result in a limited number of ROIs, or manually constructed by hand-marking OIs using a priori knowledge. The proposed importance prioritised coder coder provides a user of the encoder with great flexibility in defining single or multiple ROIs with arbitrary degrees of importance and prioritising them using IMP-J2K. Furthermore, IMP-J2K codestreams can be reconstructed by generic JPEG 2000 decoders, which is important for interoperability between imaging systems and processes. The interpretability performance of IMP-J2K was quantitatively assessed using the subjective National Imagery Interpretability Rating Scale (NIIRS). The effect of importance prioritisation on image interpretability was investigated, and a methodology to relate the NIIRS ratings, ROI importance scores and bit-rates was proposed to facilitate NIIRS specifications for importance prioritised coding. In addition, a technique is proposed to construct an importance map by allowing a user of the encoder to use gaze patterns to automatically determine and assign importance to fixated regions (or ROIs) in an image. The importance map can be used by IMP-J2K to bias the encoding of the image to these ROIs, and subsequently to allow a user at the receiver to reconstruct the image as desired by the user of the encoder. Ultimately, with the advancement of automated importance mapping techniques that can reliably predict regions of visual attention, IMP-J2K may play a significant role in matching an image coding scheme to the human visual system.

Context. Automatic placement of surveillance cameras' 3D models in an arbitrary floor plan containing obstacles is a challenging task. The problem becomes more complex when different types of region of interest (RoI) and minimum resolution are considered. An automatic camera placement decision support system (ACP-DSS) integrated into a 3D CAD environment could assist the surveillance system designers with the process of finding good camera settings considering multiple constraints. Objectives. In this study we designed and implemented two subsystems: a camera toolset in SketchUp (CTSS) and a decision support system using an enhanced Particle Swarm Optimization (PSO) algorithm (HPSO-DSS). The objective for the proposed algorithm was to have a good computational performance in order to quickly generate a solution for the automatic camera placement (ACP) problem. The new algorithm benefited from different aspects of other heuristics such as hill-climbing and greedy algorithms as well as a number of new enhancements. Methods. Both CTSS and ACP-DSS were designed and constructed using the information technology (IT) research framework. A state-of-the-art evolutionary optimization method, Hybrid PSO (HPSO), implemented to solve the ACP problem, was the core of our decision support system. Results. The CTSS is evaluated by some of its potential users after employing it and later answering a conducted survey. The evaluation of CTSS confirmed an outstanding satisfactory level of the respondents. Various aspects of the HPSO algorithm were compared to two other algorithms (PSO and Genetic Algorithm), all implemented to solve our ACP problem. Conclusions. The HPSO algorithm provided an efficient mechanism to solve the ACP problem in a timely manner. The integration of ACP-DSS into CTSS might aid the surveillance designers to adequately and more easily plan and validate the design of their security systems. The quality of CTSS as well as the solutions offered by ACP-DSS were confirmed by a number of field experts. / Sarmad Rohani: 004670606805 Reza Shams: 0046704030897

An image is a set of quantized intensity values that are sampled at a finite set of sample points on a two-dimensional plane. Images are crucial to many application areas, such as computer graphics and pattern recognition, because they discretely represent the information that the human eyes interpret. This thesis considers the use of triangular meshes for approximating intensity images. With the help of the wavelet-based analysis, triangular meshes can be efficiently constructed to approximate the image data. In this thesis, this study will focus on local image enhancement and mesh simplification operations, which try to minimize the total error of the reconstructed image as well as the number of triangles used to represent the image. The study will also present an optimal procedure for selecting triangle types used to represent the intensity image. Besides its applications to image and video compression, this triangular representation is potentially very useful for data storage and retrieval, and for processing such as image segmentation and object recognition. / Master of Science

HINTERGRUND/ ZIELSETZUNG: Vom idiopathischen Parkinsonsyndrom (IPS) können so genannte „atypische“ Parkinsonsyndrome (APS) mit einem Anteil von ca. 20% bezogen auf die Gesamtinzidenz unterschieden werden. Neben zusätzlichen Krankheitssymptomen und einem progredienteren Verlauf zeichnen sie sich durch eine schlechtere Prognose aus, die häufig auf einem Nichtansprechen auf eine dopaminerge Therapie beruht. Eine frühzeitige, korrekte Diagnose ist daher sehr entscheidend, aber im Einzelfall auch für Spezialisten äußerst schwierig. Trotz anerkannter klinischer Diagnosekriterien gibt es besonders im Frühstadium eine hohe Rate an Fehldiagnosen. Das zur Zeit vorherrschende Verfahren in der bildgebenden Diagnostik ist die Magnetresonanztomographie, wobei die konventionelle, qualitative MRT bislang keine zufriedenstellenden Ergebnisse bezüglich ihrer Spezifität und Sensitivität gezeigt hat. Die vorliegende Arbeit untersucht in einer direkten Vergleichsstudie das differenzialdiagnostische Potential der sogenannten „erweiterten“ quantitativen MRT-Verfahren. MATERIAL UND METHODEN: Ein Gesamtkollektiv von insgesamt 44 Probanden (IPS/ APS/ gesunde Kontrollen) durchlief ein umfassendes quantitatives MRT- Protokoll (R1/R2(*)-, DTI-, MTR- Mapping) um in manuell bilateral markierten, definierten Regionen (ROIs) in den Basalganglienkernen quantitative Parameter zu erheben. ERGEBNISSE: Die beste hochsignifikante Trennung der MSA-P- Patienten sowohl von IPS- Patienten (p = 0,001) als auch von Kontrollen (p = 0,004) konnte anhand des R2 * - Mappings im Putamen erreicht werden. Es zeigte sich eine Vorhersagekraft AUC von > / = 0,96 mit einer Sensitivität von 77,8 % (bei einer Spezifität von 100 %). Dies bestätigt die große Bedeutung der Eisensensitivität des R2*-Mappings bei der Identifizierung von MSA-P- Patienten. Auch anhand des MTR-Mappings konnte eine MSA-P anhand der putaminalen (p = 0,005) und nigralen (p = 0,003) Signalveränderungen signifikant vorhergesagt werden. Die beste signifikante Abgrenzung der PSP- Patienten von den Kontrollen gelang anhand der DTI- Messungen in der Substantia nigra (p = 0,001) sowie im Globus pallidus (p = 0,004). Für die diagnostische Vorhersage eines IPS konnten keine nutzbaren Signalunterschiede festgestellt werden. Insbesondere in der Substantia nigra zeigten sich gegenüber Kontrollen keine signifikanten Gruppenunterschiede. FAZIT: Unter den angewandten MRT- Verfahren zeigt das R2*-Mapping die beste Vorhersagekraft zur Differenzierung der MSA von IPS- Patienten und das DTI- Mapping zur Identifizierung der PSP- Patienten. Das Besondere unseres Arbeitsansatzes war, im Gegensatz zu vorherigen Studien, die Durchführung der Untersuchung an nur einer Kohorte. Dadurch konnte die Güte der verschiedenen MRT-Verfahren direkt und quantitativ miteinander verglichen werden. Insgesamt unterstreichen die Erkenntnisse dieser Arbeit den Stellenwert und die mögliche klinische Relevanz der quantitativen MRT, insbesondere bei der Identifizierung atypischer Parkinsonsyndrome.

High resolution surveillance video cameras are invaluable resources for effective crime prevention and forensic investigations. However, increasing communication bandwidth requirements of high definition surveillance videos are severely limiting the number of cameras that can be deployed. Higher bitrate also increases operating expenses due to higher data communication and storage costs. Hence, it is essential to develop low complexity algorithms which reduce data rate of the compressed video stream without affecting the image fidelity. In this thesis, a computer vision aided H.264 surveillance video encoder and four associated algorithms are proposed to reduce the bitrate. The proposed techniques are (I) Speeded up foreground segmentation, (II) Skip decision, (III) Reference frame selection and (IV) Face Region-of-Interest (ROI) coding. In the first part of the thesis, a modification to the adaptive Gaussian Mixture Model (GMM) based foreground segmentation algorithm is proposed to reduce computational complexity. This is achieved by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we compute periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal. In the second part, we propose a skip decision technique that uses a spatial sampler to sample pixels. The sampled pixels are segmented using the speeded up GMM algorithm. The storage pattern of the GMM parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. In the third part, a reference frame selection algorithm is proposed to maximize the number of background Macroblocks (MB’s) (i.e. MB’s that contain background image content) in the Decoded Picture Buffer. This reduces the cost of coding uncovered background regions. Distortion over foreground pixels is measured to quantify the performance of skip decision and reference frame selection techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence. In the final part of the thesis, face and shadow region detection is combined with the skip decision algorithm to perform ROI coding for pedestrian surveillance videos. Since person identification requires high quality face images, MB’s containing face image content are encoded with a low Quantization Parameter setting (i.e. high quality). Other regions of the body in the image are considered as RORI (Regions of reduced interest) and are encoded at low quality. The shadow regions are marked as Skip. Techniques that use only facial features to detect faces (e.g. Viola Jones face detector) are not robust in real world scenarios. Hence, we propose to initially detect pedestrians using deformable part models. The face region is determined using the deformed part locations. Detected pedestrians are tracked using an optical flow based tracker combined with a Kalman filter. The tracker improves the accuracy and also avoids the need to run the object detector on already detected pedestrians. Shadow and skin detector scores are computed over super pixels. Bilattice based logic inference is used to combine multiple likelihood scores and classify the super pixels as ROI, RORI or RONI. The coding mode and QP values of the MB’s are determined using the super pixel labels. The proposed techniques provide a further reduction in bitrate of up to 50.2%.