Locally Non-rigid Registration for Mobile HDR Photography

Image registration for stack-based HDR photography is challenging. If not properly accounted for, camera motion and scene changes result in artifacts in the composite image. Unfortunately, existing methods to address this problem are either accurate, but too slow for mobile devices, or fast, but prone to failing. We propose a method that fills this void: our approach is extremely fast---under 700ms on a commercial tablet for a pair of 5MP images---and prevents the artifacts that arise from insufficient registration quality

Image enhancement methods and applications in computational photography

Computational photography is currently a rapidly developing and cutting-edge topic in applied optics, image sensors and image processing fields to go beyond the limitations of traditional photography. The innovations of computational photography allow the photographer not only merely to take an image, but also, more importantly, to perform computations on the captured image data. Good examples of these innovations include high dynamic range imaging, focus stacking, super-resolution, motion deblurring and so on. Although extensive work has been done to explore image enhancement techniques in each subfield of computational photography, attention has seldom been given to study of the image enhancement technique of simultaneously extending depth of field and dynamic range of a scene. In my dissertation, I present an algorithm which combines focus stacking and high dynamic range (HDR) imaging in order to produce an image with both extended depth of field (DOF) and dynamic range than any of the input images. In this dissertation, I also investigate super-resolution image restoration from multiple images, which are possibly degraded by large motion blur. The proposed algorithm combines the super-resolution problem and blind image deblurring problem in a unified framework. The blur kernel for each input image is separately estimated. I also do not make any restrictions on the motion fields among images; that is, I estimate dense motion field without simplifications such as parametric motion. While the proposed super-resolution method uses multiple images to enhance spatial resolution from multiple regular images, single image super-resolution is related to techniques of denoising or removing blur from one single captured image. In my dissertation, space-varying point spread function (PSF) estimation and image deblurring for single image is also investigated. Regarding the PSF estimation, I do not make any restrictions on the type of blur or how the blur varies spatially. Once the space-varying PSF is estimated, space-varying image deblurring is performed, which produces good results even for regions where it is not clear what the correct PSF is at first. I also bring image enhancement applications to both personal computer (PC) and Android platform as computational photography applications

Digital Stack Photography and Its Applications

<p>This work centers on digital stack photography and its applications.</p><p>A stack of images refer, in a broader sense, to an ensemble of</p><p>associated images taken with variation in one or more than one various </p><p>values in one or more parameters in system configuration or setting.</p><p>An image stack captures and contains potentially more information than</p><p>any of the constituent images. Digital stack photography (DST)</p><p>techniques explore the rich information to render a synthesized image</p><p>that oversteps the limitation in a digital camera's capabilities.</p><p>This work considers in particular two basic DST problems, which had</p><p>been challenging, and their applications. One is high-dynamic-range</p><p>(HDR) imaging of non-stationary dynamic scenes, in which the stacked</p><p>images vary in exposure conditions. The other</p><p>is large scale panorama composition from multiple images. In this</p><p>case, the image components are related to each other by the spatial</p><p>relation among the subdomains of the same scene they covered and</p><p>captured jointly. We consider the non-conventional, practical and</p><p>challenge situations where the spatial overlap among the sub-images is</p><p>sparse (S), irregular in geometry and imprecise from the designed</p><p>geometry (I), and the captured data over the overlap zones are noisy</p><p>(N) or lack of features. We refer to these conditions simply as the</p><p>S.I.N. conditions.</p><p>There are common challenging issues with both problems. For example,</p><p>both faced the dominant problem with image alignment for</p><p>seamless and artifact-free image composition. Our solutions to the</p><p>common problems are manifested differently in each of the particular</p><p>problems, as a result of adaption to the specific properties in each</p><p>type of image ensembles. For the exposure stack, existing</p><p>alignment approaches struggled to overcome three main challenges:</p><p>inconsistency in brightness, large displacement in dynamic scene and</p><p>pixel saturation. We exploit solutions in the following three</p><p>aspects. In the first, we introduce a model that addresses and admits</p><p>changes in both geometric configurations and optical conditions, while</p><p>following the traditional optical flow description. Previous models</p><p>treated these two types of changes one or the other, namely, with</p><p>mutual exclusions. Next, we extend the pixel-based optical flow model</p><p>to a patch-based model. There are two-fold advantages. A patch has</p><p>texture and local content that individual pixels fail to present. It</p><p>also renders opportunities for faster processing, such as via</p><p>two-scale or multiple-scale processing. The extended model is then</p><p>solved efficiently with an EM-like algorithm, which is reliable in the</p><p>presence of large displacement. Thirdly, we present a generative</p><p>model for reducing or eliminating typical artifacts as a side effect</p><p>of an inadequate alignment for clipped pixels. A patch-based texture</p><p>synthesis is combined with the patch-based alignment to achieve an</p><p>artifact free result.</p><p>For large-scale panorama composition under the S.I.N. conditions, we</p><p>have developed an effective solution scheme that significantly reduces</p><p>both processing time and artifacts. Previously existing approaches can</p><p>be roughly categorized as either geometry-based composition or feature</p><p>based composition. In the former approach, one relies on precise</p><p>knowledge of the system geometry, by design and/or calibration. It</p><p>works well with a far-away scene, in which case there is only limited</p><p>variation in projective geometry among the sub-images. However, the</p><p>system geometry is not invariant to physical conditions such as</p><p>thermal variation, stress variation and etc.. The composition with</p><p>this approach is typically done in the spatial space. The other</p><p>approach is more robust to geometric and optical conditions. It works</p><p>surprisingly well with feature-rich and stationary scenes, not well</p><p>with the absence of recognizable features. The composition based on</p><p>feature matching is typically done in the spatial gradient domain. In</p><p>short, both approaches are challenged by the S.I.N. conditions. With</p><p>certain snapshot data sets obtained and contributed by Brady et al, </p><p>these methods either fail in composition or render images with</p><p>visually disturbing artifacts. To overcome the S.I.N. conditions, we</p><p>have reconciled these two approaches and made successful and</p><p>complementary use of both priori and approximate information about</p><p>geometric system configuration and the feature information from the</p><p>image data. We also designed and developed a software architecture</p><p>with careful extraction of primitive function modules that can be</p><p>efficiently implemented and executed in parallel. In addition to a</p><p>much faster processing speed, the resulting images are clear and</p><p>sharper at the overlapping zones, without typical ghosting artifacts.</p>Dissertatio

Model-Based Environmental Visual Perception for Humanoid Robots

The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling

Use of Coherent Point Drift in computer vision applications

This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration

Automatic Reconstruction of Textured 3D Models

Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models