Anomaly detection & object classification using multi-spectral LiDAR and sonar

In this thesis, we present the theory of high-dimensional signal approximation of multifrequency signals. We also present both linear and non-linear compressive sensing (CS) algorithms that generate encoded representations of time-correlated single photon counting (TCSPC) light detection and ranging (LiDAR) data, side-scan sonar (SSS) and synthetic aperture sonar (SAS). The main contributions of this thesis are summarised as follows: 1. Research is carried out studying full-waveform (FW) LiDARs, in particular, the TCSPC data, capture, storage and processing. 2. FW-LiDARs are capable of capturing large quantities of photon-counting data in real-time. However, the real-time processing of the raw LiDAR waveforms hasn’t been widely exploited. This thesis answers some of the fundamental questions: • can semantic information be extracted and encoded from raw multi-spectral FW-LiDAR signals? • can these encoded representations then be used for object segmentation and classification? 3. Research is carried out into signal approximation and compressive sensing techniques, its limitations and the application domains. 4. Research is also carried out in 3D point cloud processing, combining geometric features with material spectra (spectral-depth representation), for object segmentation and classification. 5. Extensive experiments have been carried out with publicly available datasets, e.g. the Washington RGB Image and Depth (RGB-D) dataset [108], YaleB face dataset1 [110], real-world multi-frequency aerial laser scans (ALS)2 and an underwater multifrequency (16 wavelengths) TCSPC dataset collected using custom-build targets especially for this thesis. 6. The multi-spectral measurements were made underwater on targets with different shapes and materials. A novel spectral-depth representation is presented with strong discrimination characteristics on target signatures. Several custom-made and realistically scaled exemplars with known and unknown targets have been investigated using a multi-spectral single photon counting LiDAR system. 7. In this work, we also present a new approach to peak modelling and classification for waveform enabled LiDAR systems. Not all existing approaches perform peak modelling and classification simultaneously in real-time. This was tested on both simulated waveform enabled LiDAR data and real ALS data2 . This PhD also led to an industrial secondment at Carbomap, Edinburgh, where some of the waveform modelling algorithms were implemented in C++ and CUDA for Nvidia TX1 boards for real-time performance. 1http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ 2This dataset was captured in collaboration with Carbomap Ltd. Edinburgh, UK. The data was collected during one of the trials in Austria using commercial-off-the-shelf (COTS) sensors

Automatic colonic polyp detection using curvature analysis for standard and low dose CT data

Colon cancer is the second leading cause of cancer related deaths in the developed nations. Early detection and removal of colorectal polyps via screening is the most effective way to reduce colorectal cancer (CRC) mortality. Computed Tomography Colonography (CTC) or Virtual Colonoscopy (VC) is a rapidly evolving non-invasive technique and the medical community view this medical procedure as an alternative to the standard colonoscopy for the detection of colonic polyps. In CTC the first step for automatic polyp detection for 3D visualization of the colon structure and automatic polyp detection addresses the segmentation of the colon lumen. The segmentation of colon lumen is far from a trivial task as in practice many datasets are collapsed due to incorrect patient preparation or blockages caused by residual water/materials left in the colon. In this thesis a robust multi-stage technique for automatic segmentation of the colon is proposed tha t maximally uses the anatomical model of a generic colon. In this regard, the colon is reconstructed using volume by length analysis, orientation, length, end points, geometrical position in the volumetric data, and gradient of the centreline of each candidate air region detected in the CT data. The proposed method was validated using a total of 151 standard dose (lOOmAs) and 13 low-dose (13mAs-40mAs) datasets and the collapsed colon surface detection was always higher than 95% with an average of 1.58% extra colonic surface inclusion. The second major step of automated CTC attempts the identification of colorectal polyps. In this thesis a robust method for polyp detection based on surface curvature analysis has been developed and evaluated. The convexity of the segmented colon surface is sampled using the surface normal intersection, Hough transform, 3D histogram, Gaussian distribution, convexity constraint and 3D region growing. For each polyp candidate surface the morphological and statistical features are extracted and the candidate surface is classified as a polyp/fold structure using a Feature Normalized Nearest Neighbourhood classifier. The devised polyp detection scheme entails a low computational overhead (typically takes 3.60 minute per dataset) and shows 100% sensitivity for polyps larger than 10mm, 92% sensitivity for polyps in the range 5 to 10mm and 64.28% sensitivity for polyp smaller than 5mm. The developed technique returns in average 4.01 false positives per dataset. The patient exposure to ionising radiation is the major concern in using CTC as a mass screening technique for colonic polyp detection. A reduction of the radiation dose will increase the level of noise during the acquisition process and as a result the quality of the CT d a ta is degraded. To fully investigate the effect of the low-dose radiation on the performance of automated polyp detection, a phantom has been developed and scanned using different radiation doses. The phantom polyps have realistic shapes (sessile, pedunculated, and flat) and sizes (3 to 20mm) and were designed to closely approximate the real polyps encountered in clinical CT data. Automatic polyp detection shows 100% sensitivity for polyps larger than 10mm and shows 95% sensitivity for polyps in the range 5 to 10mm. The developed method was applied to CT data acquired at radiation doses between 13 to 40mAs and the experimental results indicate th a t robust polyp detection can be obtained even at radiation doses as low as 13mAs

Cooperative Navigation for Mixed Human–Robot Teams Using Haptic Feedback

In this paper, we present a novel cooperative navigation control for human–robot teams. Assuming that a human wants to reach a final location in a large environment with the help of a mobile robot, the robot must steer the human from the initial to the target position. The challenges posed by cooperative human–robot navigation are typically addressed by using haptic feedback via physical interaction. In contrast with that, in this paper, we describe a different approach, in which the human–robot interaction is achieved via wearable vibrotactile armbands. In the proposed work, the subject is free to decide her/his own pace. A warning vibrational signal is generated by the haptic armbands when a large deviation with respect to the desired pose is detected by the robot. The proposed method has been evaluated in a large indoor environment, where 15 blindfolded human subjects were asked to follow the haptic cues provided by the robot. The participants had to reach a target area, while avoiding static and dynamic obstacles. Experimental results revealed that the blindfolded subjects were able to avoid the obstacles and safely reach the target in all of the performed trials. A comparison is provided between the results obtained with blindfolded users and experiments performed with sighted people

Aerosol remote sensing from ground-based polarized sky-radiance under cloudy conditions

Für ein besseres Verständnis der Wechselwirkungsprozesse zwischen Aerosolen und Wolken ist es nötig diese in der Umgebung von Wolken zu untersuchen. Polarisationsaufgelöste Messungen haben sich als adäquate Erweiterung klassischer, multispektraler Photometrie bewährt, da sie zusätzliche Information über die Teilen enthalten. In dieser Arbeit wurde ein neuer Algorithmus entwickelt, um mikrophysikalische und optische Aerosoleigenschaften aus bodengebundenen, polarisations- und wellenlängenaufgelösten Messungen der Himmelshelligkeit abzuleiten. Dieser beinhaltet eine Technik zum detektieren und entfernen bewölkter Messpunkte, wodurch die Methode bei teilweiser Bewölkung anwendbar ist. Es wurden numerische Studien mit synthetischen Beobachtungen durchgeführt, die mit 3D Monte-Carlo Strahlungstransportrechnungen erzeugt wurden und unterschiedliche Wolkensituation enthalten, wie Straßen aus Quaderwolken oder realistischere Wolkenfelder aus Large-Eddy-Simulationen (LES). Diese werden zunächst verwendet, um Veränderungen der gemessenen polarisationsaufgelösten Strahldichte zu bestimmen, die durch von Wolken induzierte 3D-Stahlungseffekte entstehen. Die unpolarisierte Strahldichte wird in unmittelbarer Nähe zu Wolken bei 550 nm um bis zu 55% erhöht. In der selben Situation wird die polarisierte Strahldichte verringert, jedoch nur um ungefähr 25%. Als nächstes wurde der Einfluss dieser veränderten Messungen auf die aus ihnen abgeleiteten Aerosoleigenschaften untersucht. In den meisten Fällen konnten Effektivradius und optische Dicke zuverlässig bestimmt werden, sogar wenn ein großer Teil (bis zu 70%) des Himmels mit Wolken bedeckt war. Die optische Dicke des Aerosol wird in der Regel leicht überschätzt, jedoch um nicht mehr als 0,03 oder 10%. Der abgeleitete Effektivradius der Feinpartikel stimmt auf 0,04 µm genau, unabhängig vom Grad der Bewölkung. Für den Effektivradius der Grobpartikel wird der Fehler hin zu größeren Teilchen höher. Der Realteil des Brechungsindex wird in den meisten Fällen überschätzt. Im zweiten Teil wurde der Algorithmus auf Messungen des multispektralen Sonnenphotometers SSARA angewendet. Dieses wurde bereits mit Polarisationsfiltern ausgestattet, um bei 501,5 nm die polarisierte Strahldichte messen zu können. Während der A-LIFE Messkampagne, die im April 2017 in Zypern stattfand, sammelte SSARA an 22 Tagen Messdaten. Hier werden drei Fallstudien gezeigt: Die erste veranschaulicht das Verhalten des Algorithmus bei teilweiser Bewölkung. Im zweiten Fall herrschte aufgrund einer Saharastaubschicht eine hohe Aerosolbelastung bei ansonsten klarem Himmel. Der dritte Fall beschreibt das Aufziehen von Feinpartikel-Aerosolen aus Waldbrandgebieten. Während der Vorbereitung des Instruments wurde zudem eine neuartige radiometrische und polarimetrische Kalibriermethode entwickelt, die es erlaubt gleichzeitig die Güte und die Winkel der Polarisationsfilter mit hoher Genauigkeit zu bestimmen (entsprechend auf 0,002 und unter 0,1°). Des weiteren wurde eine neue Methode für die Kalibrierung unserer altazimuthalen Montierung verwendet, die eine Korrektur der Positionierung des Messkopfs auf unter 32 arcmin ermöglicht. Dies ist momentan durch die Genauigkeit des verwendeten Sonnensuchers beschränkt. Diese beiden Kalibriermethoden sind auch auf andere Sonnenphotometer anwendbar, wie zum Beispiel die Cimel CE318-DP Instrumente, die in AERONET verwendet werden.To study aerosol-cloud interactions, observations in the vicinity of clouds are necessary. Polarimetry has proven to be a useful enhancement to classical multispectral photometry to infer aerosol optical properties, as polarized radiation contains additional information about the particles. In this thesis, a new retrieval algorithm for the retrieval of microphysical and optical aerosol properties from ground-based polarized and multispectral sky radiance measurements was developed. It includes a cloud screening mechanism that makes the method applicable to partly cloudy situations. Numerical studies have been conduced with synthetic observations generated using 3D Monte-Carlo radiative transfer simulations of different cloud situations, including cuboid cloud streets and more realistic Large-Eddy simulation (LES) generated cloud fields. These are used to first determine the 3D radiative cloud effects observable in the measured polarized radiances as a function of cloud distance. Total radiance is increased by up to 55% on average close to clouds at 550 nm, while linear polarized radiance is reduced, but only by about 25% in the same case. The influence of these altered measurements on the aerosol properties retrieved from them was investigated next. For most cases, effective radius and optical depth of the aerosol can be retrieved well, even if a significant portion (up to 70%) of the sky is covered by clouds. The aerosol optical depth is typically slightly overestimated (not more than 0.03 or 10%). The retrieval of fine mode particle effective radius is accurate to within 0.04 µm regardless of the cloud contamination. For the retrieved coarse mode effective radius the error becomes larger towards bigger particles. A positive bias in the retrieved index of refraction has been observed in most cases. In a second step, the retrieval was applied to measurements made with the SSARA multispectral sun and sky photometer, which has previously been equipped with polarizer filters to measure polarized radiance at 501.5 nm. During the A-LIFE field campaign in Cyprus in April 2017, SSARA collected 22 days of data. Here, three case studies are presented: The first demonstrates the performance of the retrieval under partially cloudy conditions. In the second case, a high aerosol load due to a Saharan dust layer was present during otherwise perfect clear sky conditions. Fine mode dominated Biomass burning aerosol was observed in the third case. During the preparation of the instrument, a novel radiometric and polarimetric calibration method has been developed, which simultaneously determines the linear polarizers' diattenuation and relative orientation with high accuracy (0.002 and below 0.1°, respectively). Furthermore, a new calibration method for the alt-azimuthal mount capable of correcting the instrument's pointing to within 32 arcmin was implemented. So far, this is limited by the accuracy of the sun-tracker. Both these methods are applicable to other sun and sky radiometers, such as Cimel CE318-DP instruments used in AERONET

‘Next generation’ chromatographic media for biopharmaceutical manufacturing

Novel ways to produce bilayered chromatography adsorbent media featuring a functionalised core surrounded by an inert size exclusion layer for nanoplex purification were explored. Surface functionalisation of anion exchange chromatography media via cold atmospheric pressure plasma etching and oxidation was investigated. Both, a dielectric barrier discharge plasma generation system and a fluidised bed underwater reactor produced bead samples with significantly reduced surface binding, while maintaining their core binding capacities. A 3D visualisation method, allowing the study of the binding topography of the spatial distribution of pDNA and BSA adhered to Q ligands within particles was developed. This CLSM method revealed imperfections on the surface of adsorbents, providing additional binding sites for pDNA due to micro crevices. Implementing beads with a more homogenous surface, restricted access media were produced via an AGE activation-partial bromination method. The thickness and homogeneity of the size exclusion layer was controlled via viscosity enhanced reaction diffusion balancing, yielding a distinct layer devoid of ligands. The particles did not show residual pDNA binding and selectivity of core vs surface binding increased by 100-fold cf. previous studies. The commercial adsorbent Capto Core 700 was successfully implemented as a first capture step for pDNA purification from crude E.coli lysates

Shape/image registration for medical imaging : novel algorithms and applications.

This dissertation looks at two different categories of the registration approaches: Shape registration, and Image registration. It also considers the applications of these approaches into the medical imaging field. Shape registration is an important problem in computer vision, computer graphics and medical imaging. It has been handled in different manners in many applications like shapebased segmentation, shape recognition, and tracking. Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Many image processing applications like remote sensing, fusion of medical images, and computer-aided surgery need image registration. This study deals with two different applications in the field of medical image analysis. The first one is related to shape-based segmentation of the human vertebral bodies (VBs). The vertebra consists of the VB, spinous, and other anatomical regions. Spinous pedicles, and ribs should not be included in the bone mineral density (BMD) measurements. The VB segmentation is not an easy task since the ribs have similar gray level information. This dissertation investigates two different segmentation approaches. Both of them are obeying the variational shape-based segmentation frameworks. The first approach deals with two dimensional (2D) case. This segmentation approach starts with obtaining the initial segmentation using the intensity/spatial interaction models. Then, shape model is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of an energy functional which integrating the shape model with the intensity information. The second one is a 3D simultaneous segmentation and registration approach. The information of the intensity is handled by embedding a Willmore flow into the level set segmentation framework. Then the shape variations are estimated using a new distance probabilistic model. The experimental results show that the segmentation accuracy of the framework are much higher than other alternatives. Applications on BMD measurements of vertebral body are given to illustrate the accuracy of the proposed segmentation approach. The second application is related to the field of computer-aided surgery, specifically on ankle fusion surgery. The long-term goal of this work is to apply this technique to ankle fusion surgery to determine the proper size and orientation of the screws that are used for fusing the bones together. In addition, we try to localize the best bone region to fix these screws. To achieve these goals, the 2D-3D registration is introduced. The role of 2D-3D registration is to enhance the quality of the surgical procedure in terms of time and accuracy, and would greatly reduce the need for repeated surgeries; thus, saving the patients time, expense, and trauma