The field of neuroscience has recently seen optogenetics emerge as a highly utilized and powerful method of non-invasive neural activation and inhibition. This thesis seeks to enhance the optogenetic toolbox through the design, construction, and evaluation of a number of hardware and software modules for research in Caenorhabditis elegans neuroscience. In the first aim, we combine optogenetics, microfluidics, and automated image processing, to create a system capable of high-throughput analysis of synaptic function. In the second aim, we develop a multi-modal illumination system for the manipulation of optogenetic reagents. The system is capable of multi-spectral illumination in definable patterns, with the ability to dynamically alter the intensity, color, and shape of the illumination. The illumination system is controlled by a set of software programs introduced in aim three, and is demonstrated through a set of experiments in aim four where we selectively activate and inhibit specific neural nodes expressing optogenetic reagents in freely moving C. elegans. With the ability to target specific nodes in a freely moving animal, we can correlate specific neural states to behaviors allowing for the dissection of neural circuits. Taken together, the developed technologies for optogenetic researchers will allow for experimentation with previously unattainable speed, precision and flexibility.

Global illumination is an important factor when striving for photo realism in computergraphics. This thesis describes why this is the case, and why global illumination is considered acomplex problem to solve. The problem becomes even more demanding when considering realtime purposes. Resent research has proven it possible to produce global illumination in realtime. Therefore the subject of this thesis is to compare and evaluate a number of those methods. An implementation is presented based on the Imperfect shadow maps method, which per se isbased on instant radiosity and reflective shadow maps. The implementation is able to renderplausible global illumination effects in real time, for fully dynamic scenes. With conclusions that while it demonstrably is possible to provide believable global illum-ination in real time, it is not without shortcomings. In every case approximations or restrictionshas to be done to some extent, sometimes leading to wrong results. Though in most cases, notvisually unpleasing by a great deal. The final conclusion is that global illumination is possible on current hardware, with believablequality and good speed. Showing great potential for future implementations on next generationof hardware.

This research mainly develops a new z-position measurement based on the chromatic aberration effect. An objective-type dark-field illumination scheme is built to produce diascopic chromatic aberration light, and aimed to enhance the signal-to-noise ratio. The xenon lamp is adapted to create white light with continuous spectrum, besides, lens with low Abbe number is needed to extend the degree of chromatic aberration, so lens made of PMMA is as a chromatic aberration component. In the proposed system, the depths of samples in micro-channel is illuminated by the dispersed light and scatter the optical signals, which are captured by a low numerical aperture (N.A.) objective lens. After the simple normalization, the intensity ratio of two selected wavelengths 450 nm (blue light) and 670 nm (red light) from the scattered spectrum becomes a reliable index for the depth information of the detecting objects. By means of establishing the relationship between depth and intensity ratio, every object flowing through diagnosed spot is able to be determined the depth level by cross-referencing the database. By using spectrometer as detector, delicate moving components for light filtering or electrical stage for light scanning can be excluded for high-speed z-position detection. Furthermore, in order to identify the depth level of sample with high flowing rate, avalanche photodiodes are adapted to achieve rapid detection. The experimental results show that the relationship between depth and intensity ratio is a parabola curve, but in this research, the region which tends to behavior linearly is adapted. The proposed system provides a linear detection range of ¡Ó15 £gm for particles with a diameter of 20 £gm. The lens with high Abbe number only obtains ¡Ó10 £gm with linear detection range though, the resolution for size is better than PMMA. The BK7 lens is capable to discriminate the depth change of 2 £gm micro-beads, note that there is no limitation of depth discrimination in this system, because of the measurement is achieved by cross-referencing the linear line. The use of UV-Vis-NIR spectrometer enable this system to analyze the depths of the samples in flow rate 0.5 mm/s. To gain the higher performance, the two avalanche photodiodes are utilized, and the short(CWL=450 nm, ¡Ó20 nm) and long(CWL=650 nm, ¡Ó20 nm) band pass filter are also equipped to represent enhancements of blue and red ray. The effective detection range extends to ¡Ó25 £gm and has high linearity(R square=0.99285) after the optimization of light stop. In high flowing rate detection, this system is able to identify the depth of sample when the flow velocity is 4.167 mm/s, the calculated throughput is 126 particles/s. It also successfully analyzes the depth of flowing human erythrocytes under the flow velocity is 2.778 mm/s, the velocity which the developed system is capable to analyze is about 5-8 folds to the conventional micro-PIV system. With this novel and simple approach, there will be the quantified information from z-direction of flowing body for bio-analysis, and also benefits estimating the performance of micro structure or device in the microfluidic chip, also the analysis of flow field. Except for dynamical detection, this system also be capable to apply in a open and static situation, such as cell or tissue proliferation assay.

There exist many methods for facial recognition, such as eigenface, templates, artificial neural networks, etc., based on the given facial sample data (patterns). When an input facial image (target) involve simple geometrical transformations and illumination, the performance of these methods are not very satisfactory. In this thesis, following Li et al., we propose a new face recognition system, which can eliminate translation, rotation, scaling, and prospective transformations of facial images automatically, and can also eliminate illumination. According to facial features, we use this method to find the best transformation and the closet illumination, and then to eliminate them for identification by the best matching between a target and the patterns. Finally, we use the least squares method to recognize the target. This method is validated by numerical examples.

Required illumination levels in an electronics manufacturing facility were studied in order to provide feedback for architects and engineers. The study was carried out for the production floor of an electronics company in Ankara. The space considered was a Printed Circuit Board Assembly (PCBA) and Surface Mount Device (SMD) production department. The selected area was subdivided into different areas according to required illumination levels for each year. These levels were determined according to the specific work types over the total area according to each individual department. Data compiled by the field survey was analyzed and evaluated for investigating the relation of the potential effectiveness of the lighting systems regarding time, together with illumination levels needed for specific work areas. Changes in the sizes of areas needing various illuminance levels over past years were analyzed. It was found that there had been a decrease in the total required illumination levels.

Three dimensional (3D) shape reconstruction that aims to reconstruct 3D surface of objects using acquired images, is one of the main problems in computer vision. There are many applications of 3D shape reconstruction, from satellite imaging to material sciences, considering a continent on earth or microscopic surface properties of a material. One of these applications is the automated firearm identification that is an old, yet an unsolved problem in forensic science. Firearm evidence matching algorithms rely on the fact that a firearm creates characteristic marks on surfaces of the bullets and the cartridge cases. These marks should be digitized unaffected from different surface material properties of evidences. Accuracy of 3D shape is one of the most important parameters affecting the overall identification performance. A very high resolution, accurate 3D data have to be reconstructed in the order of minutes. Photometric stereo (PS) method is capable of reconstructing high resolution surfaces in a fast manner. But, the metallic material and the surface topology of the firearm evidences generate highlights and shadows on their images that does not comply with the assumptions of conventional PS. In the scope of this work, it is intended to design an accurate, fast and robust 3D shape reconstruction scheme using PS considering highlights and shadows. These new PS procedures to be developed here should not be limited only to the ballistic evidences but they also could be used for a wider range of objects reflection properties and texture. For this purpose, masked PS methods which are quite fast when compared to other approaches, were classified and implemented. Simple additional masking methods are also proposed. A novel weighted PS method, using weighted least square estimation, is presented to eliminate false edges created by the masks. Concurrently, the calibration processes and the illumination configuration were improved. The disturbances due to close light sources were removed by image calibrations. From experimental tests to simulate the light positioning problem, it is concluded that the double zenith illumination configuration have better performance than the optimal single zenith illumination configuration, when the highlights and the shadows are considered. Double zenith illumination configuration results were further improved by the weighted normal PS with a small additional computation cost. All the implemented methods were tested firstly on the controlled environment using synthetic images. Later the same tests were conducted on real objects with varying characteristics as well as the firearm evidences.

Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses of color targets to changes in lighting intensity and spectrum. Surfaces of dissimilar color are found to respond significantly differently. Illumination compensation model error is reduced by 70% to 80% by individually optimizing model parameters for each distinct color region, and applying a model tuned for one region to a chromatically different region increases error by a factor of 15. A background model--called BigBackground--is presented to extract large, stable, chromatically self-similar background features by identifying the dominant colors in a scene. The stability and chromatic diversity of these features make them useful reference points for quantifying illumination changes. The model is observed to cover as much as 90% of a scene, and pixels belonging to the model are 20% more stable on average than non-member pixels. Several illumination compensation techniques are developed to exploit BigBackground, and are compared with several compensation techniques from the literature. Techniques are compared in terms of foreground / background classification, and are applied to an object tracking pipeline with kinematic and appearance-based correspondence mechanisms. Compared with other techniques, BigBackground-based techniques improve foreground classification by 25% to 43%, improve tracking accuracy by an average of 20%, and better preserve object appearance for appearance-based trackers. All algorithms are implemented in C or C++ to support the consideration of runtime performance. In terms of execution speed, the BigBackground-based illumination compensation technique is measured to run on par with the simplest compensation technique used for comparison, and consistently achieves twice the frame rate of the two next-fastest techniques.

Eine der beliebtesten Schmuckformen in der deutschen Buchmalerei sind die Feldinitialen mit „Akanthusrankenausläufern“. Das sind so genannte Initialbuchstaben, die mit floralen Ornamenten, meistens Blattformen oder Ranken, versehen sind. Neben dieser Ausführung winden sich von den Buchstaben oder Initialfeldern Ranken hinweg, welche sich über die Ränder der Seiten oder zwischen den Schriftspalten entlang ziehen. Diese sind dann häufig mit weiteren floralen und vor allem bildlichen Motiven besetzt, wie zum Beispiel mit Blüten, Vögeln oder menschlichen Figuren.

Univ., Diss.--Hamburg, 2003.

The visual appearance of an image is a complex function of factors such as scene geometry, material reflectances and textures, illumination, and the properties of the camera used to capture the image. Understanding how these factors interact to produce an image is a fundamental problem in computer vision and graphics. This dissertation examines two aspects of this problem: models of visual appearance that allow us to recover scene properties from images and videos, and tools that allow users to manipulate visual appearance in images and videos in intuitive ways. In particular, we look at these problems in three different applications. First, we propose techniques for compositing images that differ significantly in their appearance. Our framework transfers appearance between images by manipulating the different levels of a multi-scale decomposition of the image. This allows users to create realistic composites with minimal interaction in a number of different scenarios. We also discuss techniques for compositing and replacing facial performances in videos. Second, we look at the problem of creating high-quality still images from low-quality video clips. Traditional multi-image enhancement techniques accomplish this by inverting the camera’s imaging process. Our system incorporates feature weights into these image models to create results that have better resolution, noise, and blur characteristics, and summarize the activity in the video. Finally, we analyze variations in scene appearance caused by changes in lighting. We develop a model for outdoor scene appearance that allows us to recover radiometric and geometric infor- mation about the scene from images. We apply this model to a variety of visual tasks, including color-constancy, background subtraction, shadow detection, scene reconstruction, and camera geo-location. We also show that the appearance of a Lambertian scene can be modeled as a combi- nation of distinct three-dimensional illumination subspaces — a result that leads to novel bounds on scene appearance, and a robust uncalibrated photometric stereo method. / Engineering and Applied Sciences