Illumination Invariant Outdoor Perception

This thesis proposes the use of a multi-modal sensor approach to achieve illumination invariance in images taken in outdoor environments. The approach is automatic in that it does not require user input for initialisation, and is not reliant on the input of atmospheric radiative transfer models. While it is common to use pixel colour and intensity as features in high level vision algorithms, their performance is severely limited by the uncontrolled lighting and complex geometric structure of outdoor scenes. The appearance of a material is dependent on the incident illumination, which can vary due to spatial and temporal factors. This variability causes identical materials to appear differently depending on their location. Illumination invariant representations of the scene can potentially improve the performance of high level vision algorithms as they allow discrimination between pixels to occur based on the underlying material characteristics. The proposed approach to obtaining illumination invariance utilises fused image and geometric data. An approximation of the outdoor illumination is used to derive per-pixel scaling factors. This has the effect of relighting the entire scene using a single illuminant that is common in terms of colour and intensity for all pixels. The approach is extended to radiometric normalisation and the multi-image scenario, meaning that the resultant dataset is both spatially and temporally illumination invariant. The proposed illumination invariance approach is evaluated on several datasets and shows that spatial and temporal invariance can be achieved without loss of spectral dimensionality. The system requires very few tuning parameters, meaning that expert knowledge is not required in order for its operation. This has potential implications for robotics and remote sensing applications where perception systems play an integral role in developing a rich understanding of the scene

Solar Power

A wide variety of detail regarding genuine and proprietary research from distinguished authors is presented, ranging from new means of evaluation of the local solar irradiance to the manufacturing technology of photovoltaic cells. Also included is the topic of biotechnology based on solar energy and electricity generation onboard space vehicles in an optimised manner with possible transfer to the Earth. The graphical material supports the presentation, transforming the reading into a pleasant and instructive labor for any interested specialist or student

Extraction and Integration of Physical Illumination in Dynamic Augmented Reality Environments

Indiana University-Purdue University Indianapolis (IUPUI)Although current augmented, virtual, and mixed reality (AR/VR/MR) systems are facing advanced and immersive experience in the entertainment industry with countless media forms. Theses systems suffer a lack of correct direct and indirect illumination modeling where the virtual objects render with the same lighting condition as the real environment. Some systems are using baked GI, pre-recorded textures, and light probes that are mostly accomplished offline to compensate for precomputed real-time global illumination (GI). Thus, illumination information can be extracted from the physical scene for interactively rendering the virtual objects into the real world which produces a more realistic final scene in real-time. This work approaches the problem of visual coherence in AR by proposing a system that detects the real-world lighting conditions in dynamic scenes, then uses the extracted illumination information to render the objects added to the scene. The system covers several major components to achieve a more realistic augmented reality outcome. First, the detection of the incident light (direct illumination) from the physical scene with the use of computer vision techniques based on the topological structural analysis of 2D images using a live-feed 360-degree camera instrumented on an AR device that captures the entire radiance map. Also, the physics-based light polarization eliminates or reduces false-positive lights such as white surfaces, reflections, or glare which negatively affect the light detection process. Second, the simulation of the reflected light (indirect illumination) that bounce between the real-world surfaces to be rendered into the virtual objects and reflect their existence in the virtual world. Third, defining the shading characteristic/properties of the virtual object to depict the correct lighting assets with a suitable shadow casting. Fourth, the geometric properties of real-scene including plane detection, 3D surface reconstruction, and simple meshing are incorporated with the virtual scene for more realistic depth interactions between the real and virtual objects. These components are developed methods which assumed to be working simultaneously in real-time for photo-realistic AR. The system is tested with several lighting conditions to evaluate the accuracy of the results based on the error incurred between the real/virtual objects casting shadow and interactions. For system efficiency, the rendering time is compared with previous works and research. Further evaluation of human perception is conducted through a user study. The overall performance of the system is investigated to reduce the cost to a minimum

Incorporation of therapeutic effect of daylight in the architectural design of in-patient rooms to reduce patient length of stay (LoS) in hospitals

The biological need for lighting by an individual differs from the merely visual purpose, such as viewing objects and doing work or movement. Lack of adequate daylight for biological stimulation can lead to health problems, for e.g. imbalanced circadian rhythm. The importance of daylight is vital for hospital patients who are mostly physically and/or psychologically stressed. As, many patients stay indoors for 24 hours, they might be vulnerable to the lack of daylight which is necessary for health reasons. Hence, for hospital patients, daylight can be a strong therapeutic environmental design element to ensure good health and accelerate clinical recovery. The complex relationship between daylight environment and individuals responses are not fully understood. Controversy results that are debated by the previous researchers, has made the implementation of daylighting strategies in the architectural design of hospital in-patient rooms critical, mainly for therapeutic purpose. Strong evidence needs to be established that can build confidence to both architects and policy makers to use daylight for therapeutic purpose and integration of therapeutic effect of daylight to in-patient room architecture is necessary as well. This thesis provides information to architects (with examples) for incorporation of therapeutic effect of daylight in the design of in-patient rooms to reduce patient length of stay (LoS) in hospitals. A triangulation research method was applied in this work, where theories were developed qualitatively and tested quantitatively. Literature review was carried out to establish the potential effect of daylight on patient health. Retrospective field investigations were conducted to establish the quantitative relationship between daylight intensity and patient LoS inside in-patient rooms by developing Multiple Linear Regression (MLR) models under a general hospital environment. Using the daylighting goal to enhance therapeutic benefit for hospital patients, referred from literature and verified from field investigation data, a daylight design concept (sky window configurations) was developed and evaluated by prospective simulation study, and found better compared to traditional standard hospital window configurations, in order to enhance therapeutic benefit for hospital patients. A dynamic annual Climate-Based Daylight Modelling (CBDM) method that uses RADIANCE (backward) raytracer combined with a daylight coefficient approach considering Perez all weather sky luminance model (i.e. DAYSIM), was used for simulation analysis. This thesis develops strategies for architects to incorporate therapeutic effect of daylight in the architectural design of hospital in-patient rooms, including guidelines to support architectural decisions in case of conflicting situations, and to identify the range of daylight intensities within which patient LoS is expected to be reduced. The strategies also consider the ultraviolet radiation (UVR) protections and discuss the challenges of climate change for daylight researchers for the incorporation of therapeutic effect of daylight in the design of hospital in-patient rooms. The thesis provides a contribution to knowledge by establishing strong evidence of quantitative relationship between daylight and LoS, and by presenting new architectural forms for hospital in-patient room design as one of the possible ways to incorporate therapeutic effect of daylight in the design of hospital in-patient rooms effectively. It is expected that the research will encourage and help architects and policy makers to incorporate therapeutic effect of daylight in the design of hospital in-patient rooms, efficiently

Development of an improved shade environment for the reduction of personal UV exposure

The research from this project has quantified the solar UV environment beneath and surrounding typical local council public shade structures. The effects of changing seasons, atmospheric conditions, structural modifications and surrounding plant life on diffuse UV have been quantified. Strategies to improve current shade structures, so as to significantly reduce the levels of diffuse UV reaching the human body in the shade, have also been developed. For the shade structures used in this research it was found that ultraviolet protection factors ranged from 1.5 to 18.3 for a decreasing solar zenith angle. Correlations have been found relating diffuse erythemal UV to UV in the shade for clear skies and a changing solar zenith angle. The effect of changing atmospheric ozone levels on diffuse erythemal UV levels has been quantified. UV exposures were assessed for a decrease in scattered UV beneath specific shade structures by the use of two types of protection, namely, side-on polycarbonate sheeting and evergreen vegetation. Broadband radiometric and dosimetric measurements conducted in the shade of a scale model shade structure, during summer and winter, showed significant decreases in exposure of up to 65% for summer and 57% for winter when comparing the use and non-use of polycarbonate sheeting. Measurements conducted in the shade of four shade structures, with various amounts of vegetation blocking different sides, showed that adequate amounts and positioning of vegetation decreased the scattered UV in the shade by up to 89% when compared to the shade structure that had no surrounding vegetation. This research shows that major UV reduction could be achieved by the ‘shade creation and design industry’, and that shade guidelines should be updated as soon as possible

Illumination Invariant Deep Learning for Hyperspectral Data

Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination invariant feature representations and classification models for hyperspectral data captured outdoors, under natural sunlight. The approaches integrate domain knowledge into learning algorithms and hence does not rely on a priori knowledge of atmospheric parameters, additional sensors or large amounts of labelled training data. Hyperspectral sensors record rich semantic information from a scene, making them useful for robotics or remote sensing applications where perception systems are used to gain an understanding of the scene. Images recorded by hyperspectral sensors can, however, be affected to varying degrees by intrinsic factors relating to the sensor itself (keystone, smile, noise, particularly at the limits of the sensed spectral range) but also by extrinsic factors such as the way the scene is illuminated. The appearance of the scene in the image is tied to the incident illumination which is dependent on variables such as the position of the sun, geometry of the surface and the prevailing atmospheric conditions. Effects like shadows can make the appearance and spectral characteristics of identical materials to be significantly different. This degrades the performance of high-level algorithms that use hyperspectral data, such as those that do classification and clustering. If sufficient training data is available, learning algorithms such as neural networks can capture variability in the scene appearance and be trained to compensate for it. Learning algorithms are advantageous for this task because they do not require a priori knowledge of the prevailing atmospheric conditions or data from additional sensors. Labelling of hyperspectral data is, however, difficult and time-consuming, so acquiring enough labelled samples for the learning algorithm to adequately capture the scene appearance is challenging. Hence, there is a need for the development of techniques that are invariant to the effects of illumination that do not require large amounts of labelled data. In this thesis, an approach to learning a representation of hyperspectral data that is invariant to the effects of illumination is proposed. This approach combines a physics-based model of the illumination process with an unsupervised deep learning algorithm, and thus requires no labelled data. Datasets that vary both temporally and spatially are used to compare the proposed approach to other similar state-of-the-art techniques. The results show that the learnt representation is more invariant to shadows in the image and to variations in brightness due to changes in the scene topography or position of the sun in the sky. The results also show that a supervised classifier can predict class labels more accurately and more consistently across time when images are represented using the proposed method. Additionally, this thesis proposes methods to train supervised classification models to be more robust to variations in illumination where only limited amounts of labelled data are available. The transfer of knowledge from well-labelled datasets to poorly labelled datasets for classification is investigated. A method is also proposed for enabling small amounts of labelled samples to capture the variability in spectra across the scene. These samples are then used to train a classifier to be robust to the variability in the data caused by variations in illumination. The results show that these approaches make convolutional neural network classifiers more robust and achieve better performance when there is limited labelled training data. A case study is presented where a pipeline is proposed that incorporates the methods proposed in this thesis for learning robust feature representations and classification models. A scene is clustered using no labelled data. The results show that the pipeline groups the data into clusters that are consistent with the spatial distribution of the classes in the scene as determined from ground truth

Modelling of thermal radiation exchange at glass-covered greenhouse surfaces under different climatic conditions

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Cast shadow modelling and detection

Computer vision applications are often confronted by the need to differentiate between objects and their shadows. A number of shadow detection algorithms have been proposed in literature, based on physical, geometrical, and other heuristic techniques. While most of these existing approaches are dependent on the scene environments and object types, the ones that are not, are classified as superior to others conceptually and in terms of accuracy. Despite these efforts, the design of a generic, accurate, simple, and efficient shadow detection algorithm still remains an open problem. In this thesis, based on a physically-derived hypothesis for shadow identification, novel, multi-domain shadow detection algorithms are proposed and tested in the spatial and transform domains. A novel "Affine Shadow Test Hypothesis" has been proposed, derived, and validated across multiple environments. Based on that, several new shadow detection algorithms have been proposed and modelled for short-duration video sequences, where a background frame is available as a reliable reference, and for long duration video sequences, where the use of a dedicated background frame is unreliable. Finally, additional algorithms have been proposed to detect shadows in still images, where the use of a separate background frame is not possible. In this approach, the author shows that the proposed algorithms are capable of detecting cast, and self shadows simultaneously. All proposed algorithms have been modelled, and tested to detect shadows in the spatial (pixel) and transform (frequency) domains and are compared against state-of-art approaches, using popular test and novel videos, covering a wide range of test conditions. It is shown that the proposed algorithms outperform most existing methods and effectively detect different types of shadows under various lighting and environmental conditions