Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Pedestrian detection and tracking using stereo vision techniques

Automated pedestrian detection, counting and tracking has received significant attention from the computer vision community of late. Many of the person detection techniques described so far in the literature work well in controlled environments, such as laboratory settings with a small number of people. This allows various assumptions to be made that simplify this complex problem. The performance of these techniques, however, tends to deteriorate when presented with unconstrained environments where pedestrian appearances, numbers, orientations, movements, occlusions and lighting conditions violate these convenient assumptions. Recently, 3D stereo information has been proposed as a technique to overcome some of these issues and to guide pedestrian detection. This thesis presents such an approach, whereby after obtaining robust 3D information via a novel disparity estimation technique, pedestrian detection is performed via a 3D point clustering process within a region-growing framework. This clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. This pedestrian detection technique requires no external training and is able to robustly handle challenging real-world unconstrained environments from various camera positions and orientations. In addition, this thesis presents a continuous detect-and-track approach, with additional kinematic constraints and explicit occlusion analysis, to obtain robust temporal tracking of pedestrians over time. These approaches are experimentally validated using challenging datasets consisting of both synthetic data and real-world sequences gathered from a number of environments. In each case, the techniques are evaluated using both 2D and 3D groundtruth methodologies

3D CNN methods in biomedical image segmentation

A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most interesting and looked-forward goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its use as a purely assistive tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedical Image segmentation and scientific interests from a variety of fields, spacing from automotive to natural resource exploration, converge to their development. While most of the applications of CNNs are focused on single-image segmentation, biomedical image data -being it MRI, CT-scans, Microscopy, etc- often benefits from three-dimensional volumetric expression. This work explores a reformulation of the CNN segmentation problem that is native to the 3D nature of the data, with particular interest to the applications to Fluorescence Microscopy volumetric data produced at the European Laboratories for Nonlinear Spectroscopy in the context of two different large international human brain study projects: the Human Brain Project and the White House BRAIN Initiative

Daylight simulation with photon maps

Physically based image synthesis remains one of the most demanding tasks in the computer graphics field, whose applications have evolved along with the techniques in recent years, particularly with the decline in cost of powerful computing hardware. Physically based rendering is essentially a niche since it goes beyond the photorealistic look required by mainstream applications with the goal of computing actual lighting levels in physical quantities within a complex 3D scene. Unlike mainstream applications which merely demand visually convincing images and short rendering times, physically based rendering emphasises accuracy at the cost of increased computational overhead. Among the more specialised applications for physically based rendering is lighting simulation, particularly in conjunction with daylight. The aim of this thesis is to investigate the applicability of a novel image synthesis technique based on Monte Carlo particle transport to daylight simulation. Many materials used in daylight simulation are specifically designed to redirect light, and as such give rise to complex effects such as caustics. The photon map technique was chosen for its efficent handling of these effects. To assess its ability to produce physically correct results which can be applied to lighting simulation, a validation was carried out based on analytical case studies and on simple experimental setups. As prerequisite to validation, the photon map\u27s inherent bias/noise tradeoff is investigated. This tradeoff depends on the density estimate bandwidth used in the reconstruction of the illumination. The error analysis leads to the development of a bias compensating operator which adapts the bandwidth according to the estimated bias in the reconstructed illumination. The work presented here was developed at the Fraunhofer Institute for Solar Energy Systems (ISE) as part of the FARESYS project sponsored by the German national research foundation (DFG), and embedded into the RADIANCE rendering system.Die Erzeugung physikalisch basierter Bilder gilt heute noch als eine der rechenintensivsten Aufgaben in der Computergraphik, dessen Anwendungen sowie auch Verfahren in den letzten Jahren kontinuierlich weiterentwickelt wurden, vorangetrieben primär durch den Preisverfall leistungsstarker Hardware. Physikalisch basiertes Rendering hat sich als Nische etabliert, die über die photorealistischen Anforderungen typischer Mainstream-Applikationen hinausgeht, mit dem Ziel, Lichttechnische Größen innerhalb einer komplexen 3D Szene zu berechnen. Im Gegensatz zu Mainstream-Applikationen, die visuell überzeugend wirken sollen und kurze Rechenzeiten erforden, liegt der Schwerpunkt bei physikalisch basiertem Rendering in der Genauigkeit, auf Kosten des Rechenaufwands. Zu den eher spezialisierten Anwendungen im Gebiet des physikalisch basiertem Renderings gehört die Lichtsimulation, besonders in Bezug auf Tageslicht. Das Ziel dieser Dissertation liegt darin, die Anwendbarkeit eines neuartigen Renderingverfahrens basierend auf Monte Carlo Partikeltransport hinsichtlich Tageslichtsimulation zu untersuchen. Viele Materialien, die in der Tageslichtsimulation verwendet werden, sind speziell darauf konzipiert, Tageslicht umzulenken, und somit komplexe Phänomene wie Kaustiken hervorrufen. Das Photon-Map-Verfahren wurde aufgrund seiner effizienten Simulation solcher Effekte herangezogen. Zur Beurteilung seiner Fähigkeit, physikalisch korrekte Ergebnisse zu liefern, die in der Tageslichtsimulation anwendbar sind, wurde eine Validierung anhand analytischer Studien sowie eines einfachen experimentellen Aufbaus durchgeführt. Als Voraussetzung zur Validierung wurde der Photon Map bezüglich seiner inhärenten Wechselwirkung zwischen Rauschen und systematischem Fehler (Bias) untersucht. Diese Wechselwirkung hängt von der Bandbreite des Density Estimates ab, mit dem die Beleuchtung aus den Photonen rekonstruiert wird. Die Fehleranalyse führt zur Entwicklung eines Bias compensating Operators, der die Bandbreite dynamisch anhand des geschätzten Bias in der rekonstruierten Beleuchtung anpasst. Die hier vorgestellte Arbeit wurde am Fraunhofer Institut für Solare Energiesysteme (ISE) als teil des FARESYS Projekts entwickelt, daß von der Deutschen Forschungsgemeinschaft (DFG) finanziert wurde. Die Implementierung erfolgte im Rahmen des RADIANCE Renderingsystems

Optical techniques applied to measurements in art

Optical diagnostic techniques are particularly attractive for the non-destructive detection of incipient damage and the evaluation of the state of surface decay. Non-contact, high precision measurements of the shape and deformation of an artifact can be performed using laser methods based on holographic and speckle interferometry. [Continues.

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

The Estimation and Correction of Rigid Motion in Helical Computed Tomography

X-ray CT is a tomographic imaging tool used in medicine and industry. Although technological developments have significantly improved the performance of CT systems, the accuracy of images produced by state-of-the-art scanners is still often limited by artefacts due to object motion. To tackle this problem, a number of motion estimation and compensation methods have been proposed. However, no methods with the demonstrated ability to correct for rigid motion in helical CT scans appear to exist. The primary aims of this thesis were to develop and evaluate effective methods for the estimation and correction of arbitrary six degree-of-freedom rigid motion in helical CT. As a first step, a method was developed to accurately estimate object motion during CT scanning with an optical tracking system, which provided sub-millimetre positional accuracy. Subsequently a motion correction method, which is analogous to a method previously developed for SPECT, was adapted to CT. The principle is to restore projection consistency by modifying the source-detector orbit in response to the measured object motion and reconstruct from the modified orbit with an iterative reconstruction algorithm. The feasibility of this method was demonstrated with a rapidly moving brain phantom, and the efficacy of correcting for a range of human head motions acquired from healthy volunteers was evaluated in simulations. The methods developed were found to provide accurate and artefact-free motion corrected images with most types of head motion likely to be encountered in clinical CT imaging, provided that the motion was accurately known. The method was also applied to CT data acquired on a hybrid PET/CT scanner demonstrating its versatility. Its clinical value may be significant by reducing the need for repeat scans (and repeat radiation doses), anesthesia and sedation in patient groups prone to motion, including young children