Consistent joint photometric and geometric image registration

In this paper, we derive a novel robust image alignment technique that performs joint geometric and photometric registration in the total least square sense. The main idea is to use the total least square metrics instead of the ordinary least square metrics, which is commonly used in the literature. While the OLS model indicates that the target image may contain noise and the reference image should be noise-free, this puts a severe limitation on practical registration problems. By introducing the TLS model, which allows perturbations in both images, we can obtain mutually consistent parameters. Experimental results show that our method is indeed much more consistent and accurate in presence of noise compared to existing registration algorithms

Radiometric calibration methods from image sequences

In many computer vision systems, an image of a scene is assumed to directly reflect the scene radiance. However, this is not the case for most cameras as the radiometric response function which is a mapping from the scene radiance to the image brightness is nonlinear. In addition, the exposure settings of the camera are adjusted (often in the auto-exposure mode) according to the dynamic range of the scene changing the appearance of the scene in the images. Vignetting effect which refers to the gradual fading-out of an image at points near its periphery also contributes in changing the scene appearance in images. In this dissertation, I present several algorithms to compute the radiometric properties of a camera which enable us to find the relationship between the image brightness and the scene radiance. First, I introduce an algorithm to compute the vignetting function, the response function, and the exposure values that fully explain the radiometric image formation process from a set of images of a scene taken with different and unknown exposure values. One of the key features of the proposed method is that the movement of the camera is not limited when taking the pictures whereas most existing methods limit the motion of the camera. Then I present a joint feature tracking and radiometric calibration scheme which performs an integrated radiometric calibration in contrast to previous radiometric calibration techniques which require the correspondences as an input which leads to a chicken-and-egg problem as precise tracking requires accurate radiometric calibration. By combining both into an integrated approach we solve this chicken-and-egg problem. Finally, I propose a radiometric calibration method suited for a set of images of an outdoor scene taken at a regular interval over a period of time. This type of data is a challenging problem because the illumination for each image is changing causing the exposure of the camera to change and the conventional radiometric calibration framework cannot be used for this type of data. The proposed methods are applied to radiometrically align images for seamless mosaics and 3D model textures, to create high dynamic range mosaics, and to build an adaptive stereo system

Automatic exposure control in network video cameras

The overall objective of this study is to describe, analyse and suggest improvements on existing automatic exposure control systems in selected network video cameras. Since an image sensor has a limited dynamic range compared to a real scene, it is necessary to automatically control the exposure level and thus adapt to the amount of light in the scene. This can be done by adjusting parameters such as exposure time, gain and variable aperture in an automatic control loop. The two cameras in this study run different implementations of such a control loop and the topic of this study is to test their performance, to review their implementation of automatic exposure control, to comment on their implementation from a theoretical stand point, and to suggest improvements. The most focus has been correction of integrator function or adding of integrator functionality to the controllers to remove steady state errors. Integrator windup was solved for two cases. Some other minor bugs giving unwanted behavior such ass finite word length in the integrators. Also improving gain scheduling and correction of clamping of signals are suggested. A suggestion for smear control improvement is to use feed forward the changes when changes are needed to exposure, this enables to control faster and still limit the impact on the picture quality

Improving SLI Performance in Optically Challenging Environments

The construction of 3D models of real-world scenes using non-contact methods is an important problem in computer vision. Some of the more successful methods belong to a class of techniques called structured light illumination (SLI). While SLI methods are generally very successful, there are cases where their performance is poor. Examples include scenes with a high dynamic range in albedo or scenes with strong interreflections. These scenes are referred to as optically challenging environments. The work in this dissertation is aimed at improving SLI performance in optically challenging environments. A new method of high dynamic range imaging (HDRI) based on pixel-by-pixel Kalman filtering is developed. Using objective metrics, it is show to achieve as much as a 9.4 dB improvement in signal-to-noise ratio and as much as a 29% improvement in radiometric accuracy over a classic method. Quality checks are developed to detect and quantify multipath interference and other quality defects using phase measuring profilometry (PMP). Techniques are established to improve SLI performance in the presence of strong interreflections. Approaches in compressed sensing are applied to SLI, and interreflections in a scene are modeled using SLI. Several different applications of this research are also discussed

Multiple-filtering-process for the edge detection of high-dynamic-range Images

Edge detection is a basic image processing operation usually used in the first stage of the complex image processing systems, such as restoration, and its quality has a direct effect on the performance of the systems. The extraction of correct edges from a noise-contaminated image or an image with severe deformation is a challenging task. The objective of the work of this thesis is to develop an edge detection method to extract effectively edge signals from the images with the edge information seriously damaged while being acquired in high dynamic range (HDR) scenes. To achieve the objective, an edge detection method based on a multiple-high-pass-filtering process scheme has been proposed. Each of the filtering processes is designed to suit one of the signal deformation conditions, and is applied to the entire input image, instead of the designated regions, in order to spare the computation of image segmentation. A fusion process is then performed to merge the gradient maps generated by the multiple filtering processes into one. A detection procedure has been designed for a typical case of HDR images acquired with three different kinds of deformations due to the non-ideal characteristics of acquisition device. Based on the study of the characteristics, three high-pass filtering processes are designed to generate gradient signals with different modulations. A simple selection algorithm is developed for an easy fusion process. The results of the simulation with different types of HDR images have shown that, compared to some of most commonly used detection processes, the proposed one leads to a better quality of edge signals from severely deformed HDR images