Self-supervised Outdoor Scene Relighting

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a solution. Such renderings are synthesized using priors learned from limited data. In contrast, we propose a self-supervised approach for relighting. Our approach is trained only on corpora of images collected from the internet without any user-supervision. This virtually endless source of training data allows training a general relighting solution. Our approach first decomposes an image into its albedo, geometry and illumination. A novel relighting is then produced by modifying the illumination parameters. Our solution capture shadow using a dedicated shadow prediction map, and does not rely on accurate geometry estimation. We evaluate our technique subjectively and objectively using a new dataset with ground-truth relighting. Results show the ability of our technique to produce photo-realistic and physically plausible results, that generalizes to unseen scenes.Comment: Published in ECCV '20, http://gvv.mpi-inf.mpg.de/projects/SelfRelight

Development of a vision-based situational awareness capability for unmanned surface vessels

The current generations of unmanned surface vessels (USVs) are reliant on the human operator for collision avoidance. This reliance poses a constraint on the operational envelope of the USV as it requires a high bandwidth and low latency communication link between the USV and control station. This thesis adopts a systems engineering approach in identifying the capability gap and the factors that drive the need for a USV with autonomous capability. An algorithm employing edge detection and morphological structuring methods is developed in this thesis to explore the feasibility of using a computer vision--based technique to provide a situational awareness capability, which is required to achieve autonomous navigation. The algorithm was tested with both color video imagery and infrared video imagery, and the results obtained from processing the images demonstrated the viability of using this information to provide situational awareness to the USV. It is recommended that further work be done to improve the robustness of the algorithm.http://archive.org/details/developmentofvis1094556185Civilian, Singapore Technologies Electronics LimitedApproved for public release; distribution is unlimited

Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns. It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter this picture. That is, if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented by an unknown encoded representation of a visual image. The conception of the dynamics of the nonlinear line attractor network to operate between stable and unstable states is the second contribution in this dissertation research. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilizes stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can manifest complex dynamics in an unsupervised manner. The third contribution of this dissertation is the introduction of the concept of manifold of color perception. The fourth contribution of this dissertation is the development of a nonlinear dimensionality reduction technique by embedding a set of related observations into a low-dimensional space utilizing the result attained by the learned memory matrices of the nonlinear line attractor network. Development of a system for affective states computation is also presented in this dissertation. This system is capable of extracting the user\u27s mental state in real time using a low cost computer. It is successfully interfaced with an advanced learning environment for human-computer interaction

Combining Image Processing with Signal Processing to Improve Transmitter Geolocation Estimation

This research develops an algorithm which combines image processing with signal processing to improve transmitter geolocation capability. A building extraction algorithm is compiled from current techniques in order to provide the locations of rectangular buildings within an aerial, orthorectified, RGB image to a geolocation algorithm. The geolocation algorithm relies on measured TDOA data from multiple ground sensors to locate a transmitter by searching a grid of possible transmitter locations within the image region. At each evaluated grid point, theoretical TDOA values are computed for comparison to the measured TDOA values. To compute the theoretical values, the shortest path length between the transmitter and each of the sensors is determined. The building locations are used to determine if the LOS path between these two points is obstructed and what would be the shortest reflected path length. The grid location producing theoretical TDOA values closest to the measured TDOA values is the result of the algorithm. Measured TDOA data is simulated in this thesis. The thesis method performance is compared to that of a current geolocation method that uses Taylor series expansion to solve for the intersection of hyperbolic curves created by the TDOA data. The average online runtime of thesis simulations range from around 20 seconds to around 2 minutes, while the Taylor series method only takes about 0.02 seconds. The thesis method also includes an offline runtime of up to 30 minutes for a given image region and sensor configuration. The thesis method improves transmitter geolocation error by an average of 44m, or 53% in the obstructed simulation cases when compared with the current Taylor series method. However, in cases when all sensors have a direct LOS, the current method performs more accurately. Therefore, the thesis method is most applicable to missions requiring tracking of slower-moving targets in an urban environment with stationary sensors

Algorithms for the enhancement of dynamic range and colour constancy of digital images & video

One of the main objectives in digital imaging is to mimic the capabilities of the human eye, and perhaps, go beyond in certain aspects. However, the human visual system is so versatile, complex, and only partially understood that no up-to-date imaging technology has been able to accurately reproduce the capabilities of the it. The extraordinary capabilities of the human eye have become a crucial shortcoming in digital imaging, since digital photography, video recording, and computer vision applications have continued to demand more realistic and accurate imaging reproduction and analytic capabilities. Over decades, researchers have tried to solve the colour constancy problem, as well as extending the dynamic range of digital imaging devices by proposing a number of algorithms and instrumentation approaches. Nevertheless, no unique solution has been identified; this is partially due to the wide range of computer vision applications that require colour constancy and high dynamic range imaging, and the complexity of the human visual system to achieve effective colour constancy and dynamic range capabilities. The aim of the research presented in this thesis is to enhance the overall image quality within an image signal processor of digital cameras by achieving colour constancy and extending dynamic range capabilities. This is achieved by developing a set of advanced image-processing algorithms that are robust to a number of practical challenges and feasible to be implemented within an image signal processor used in consumer electronics imaging devises. The experiments conducted in this research show that the proposed algorithms supersede state-of-the-art methods in the fields of dynamic range and colour constancy. Moreover, this unique set of image processing algorithms show that if they are used within an image signal processor, they enable digital camera devices to mimic the human visual system s dynamic range and colour constancy capabilities; the ultimate goal of any state-of-the-art technique, or commercial imaging device

Ergonomics of the Operative Field in Paediatric Minimal Access Surgery

Imperial Users onl

Analysis of the sedimentary characteristics of the tees estuary using remote sensing and GIS techniques

This thesis examines the ability of airborne remotely sensed data to provide quantitative information about the characteristics of intertidal sediments. The research was undertaken on Seal Sands in the Tees estuary, UK, and the airborne imagery was acquired by the Natural Environment Research Council (NERC) using a Daedalus 1268 11 channel scanning radiometer. The research focused upon establishing calibration and correction procedures for the airborne imagery as well as developing GIS techniques to process and analyze the data. A database was produced for the National Nature Reserve of Seal Sands to integrate remotely sensed imagery data, primary data from fieldwork (particle size analysis) and digital map data. Quantitative analysis of the relationship between radiance and particle size characteristics was undertaken. Results show that a multiple regression model is able to predict sand fractions in intertidal sediments and explain over 70% of the variance in radiance data. GIS techniques have facilitated predictions of the ATM data and particle size analysis of the intertidal sediments, sediment interpolation, and spatial patterns of birds' feeding behaviour. In addition, a digital elevation model (DEM) was established to investigate the relationship of sediment distribution to topography. Although limited to a single study area, the integrated approach employed in this research should be of use in monitoring estuarine environments elsewhere