View synthesis for kinetic depth X-ray imaging

This thesis reports the development and analysis of feature based synthesis of transmission X-ray images. The synthetic imagery is formed through matching and morphing or warping line-scan format images produced by a novel multi-view X-ray machine. In this way video type sequences, which periodically alternate between synthetic and detector based views, may be formed. The purpose of these sequences is to provide depth from motion or kinetic depth effect (KDE) in a visual display; while the role of the synthesis is to reduce the total number of detector arrays, associated collimators and X-ray flux per inspection. A specific challenge is to explore the bounds for producing synthetic imagery that can be seamlessly introduced into the resultant sequences. This work is distinct from the image collection and display technique, termed KDEX, previously undertaken by the Imaging Science Group at NTU. The ultimate aim of the research programme in collaboration with The UK Home Office and The US Dept. of Homeland Security is to enhance the detection and identification of threats in X-ray scans of luggage. A multi-view „KDEX scanner‟ was employed to collect greyscale and colour coded image sequences of 30 different bags; each sequence comprised of 7 perspective views separated from one another by 10. This imagery was organised and stored in a database to enable a coherent series of experiments to be conducted. Corresponding features in sequential pairs of images, at various different angular separations, were identified by applying a scale invariant feature transform (SIFT)

Geological constraints on surface-based models through development of Rapid Reservoir Modelling

Surface-based geological modelling (SBM) represents all geological heterogeneity that impacts the spatial distribution of petrophysical properties using surfaces. To create surface-based models, rules are required to govern how surfaces interact such that resulting models are geologically sound. Previous studies used implicit rules or assumptions, often with the requirement that surfaces are created in stratigraphic or hierarchical order. A comprehensive set of explicit and universal rules to govern the interaction of stratigraphic surfaces has yet to be formalised. In this thesis, seven operators are presented that define how stratigraphic surfaces interact for geological modelling such that universal geological rules are obeyed. The operators can be applied through any SBM technique and are independent of geological process, scale and setting. The operators are demonstrated using three hand-drafted examples of siliciclastic and carbonate strata, at centimetre to kilometre scales, using outcrop, seismic and conceptual input data. These universal stratigraphic operators are then implemented in 3D in the sketch-based interface and modelling (SBIM) research prototype software Rapid Reservoir Modelling (RRM). Three case studies are presented using examples of siliciclastic and carbonate strata from different depositional environments, at multiple scales, using seismic, outcrop, and well log data to constrain and guide the sketches. The case studies demonstrate the operators and three different techniques for moving from 2D sketch to 3D model, revealing the flexibility and broad applicability of the operators for SBIM of stratigraphy. Lastly, the stratigraphic operators are leveraged in RRM to create structural models. Test cases are a conjugate fault model and a physical model of a salt-influenced passive margin. Gaps in the applicability of stratigraphic operators for ‘sketch-what-you-see’ structural modelling and diagenesis are identified and future updates to RRM are recommended. RRM is the first SBIM software that allows rapid prototyping of geological reservoir models and represents a step-change for the field.Open Acces

Colour videos with depth : acquisition, processing and evaluation

The human visual system lets us perceive the world around us in three dimensions by integrating evidence from depth cues into a coherent visual model of the world. The equivalent in computer vision and computer graphics are geometric models, which provide a wealth of information about represented objects, such as depth and surface normals. Videos do not contain this information, but only provide per-pixel colour information. In this dissertation, I hence investigate a combination of videos and geometric models: videos with per-pixel depth (also known as RGBZ videos). I consider the full life cycle of these videos: from their acquisition, via filtering and processing, to stereoscopic display. I propose two approaches to capture videos with depth. The first is a spatiotemporal stereo matching approach based on the dual-cross-bilateral grid – a novel real-time technique derived by accelerating a reformulation of an existing stereo matching approach. This is the basis for an extension which incorporates temporal evidence in real time, resulting in increased temporal coherence of disparity maps – particularly in the presence of image noise. The second acquisition approach is a sensor fusion system which combines data from a noisy, low-resolution time-of-flight camera and a high-resolution colour video camera into a coherent, noise-free video with depth. The system consists of a three-step pipeline that aligns the video streams, efficiently removes and fills invalid and noisy geometry, and finally uses a spatiotemporal filter to increase the spatial resolution of the depth data and strongly reduce depth measurement noise. I show that these videos with depth empower a range of video processing effects that are not achievable using colour video alone. These effects critically rely on the geometric information, like a proposed video relighting technique which requires high-quality surface normals to produce plausible results. In addition, I demonstrate enhanced non-photorealistic rendering techniques and the ability to synthesise stereoscopic videos, which allows these effects to be applied stereoscopically. These stereoscopic renderings inspired me to study stereoscopic viewing discomfort. The result of this is a surprisingly simple computational model that predicts the visual comfort of stereoscopic images. I validated this model using a perceptual study, which showed that it correlates strongly with human comfort ratings. This makes it ideal for automatic comfort assessment, without the need for costly and lengthy perceptual studies