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Micropalaeontology, a discipline that contributes to climate research and hydrocarbon exploration, is driven by the taxonomic analysis of huge volumes of microfossils. Unfortunately, this repetitive analysis is a serious bottleneck to progress because it depends on the scarce time of experts. These issues propel research into computerized taxonomic analysis, including a promising new approach called computer-aided microfossil identification. However, the existing computer-aided system relies on image-based representations, which severely limits its ability to discriminate specimens. These limitations motivate using computer vision to support richer video and shape-based representations, which is the focus of this thesis. An important contribution is a scheme to localize, capture, and extract video and shape-based representations from large microfossil batches. These representations encapsulate information across multiple lighting conditions. In addition, the thesis describes a method based on photometric stereo to correct misalignments in images of the same object illuminated from different directions. Not only does this correction benefit the application at hand, but it can also benefit a variety of other applications. The thesis also introduces a visual-surface reconstruction method based on maximum likelihood estimation, which constructs usable depth maps even from extraordinarily noisy images. State of the art methods lack this capability. By freeing classification from the bounds imposed by images, these contributions significantly advance computerized microfossil identification toward the ultimate goal of a practical and reliable tool for high-throughput taxonomic analysis. / Digital Signals and Image Processing

This dissertation proposes an approach for 3D micro-scale shape reconstruction using photometric stereo (PS) with surface normal integration (SNI). Based on the proposed approach, a portable cost-effective stationary system is developed to capture 3D shapes in the order of micrometer scale. The PS with SNI technique is adopted to reconstruct 3D microtopology since this technique is highlighted for its capability to reproduce fine surface details at pixel resolution. Furthermore, since the primary hardware components are merely a camera and several typical LEDs, the system based on PS with SNI can be made portable at low cost. The principal contributions are three folds. First, a PS method based on dichromatic reflectance model (DRM) using color input images is proposed to generalize PS applicable to a wider range of surfaces with non-Lambertian reflectances. The proposed method not only estimates surface orientations from diffuse reflection but also exploits information from specularities owing to the proposed diffuse-specular separation algorithm. Using the proposed PS method, material-dependent features can be simultaneously extracted in addition to surface orientations, which offers much richer information in understanding the 3D scene and poses more potential functionalities, such as specular removal, intrinsic image decomposition, digital relighting, material-based segmentation, material transfer and material classification. The second contribution is the development of an SNI method dealing with perspective distortion. The proposed SNI is performed on the image plane instead of on the target surface as did by orthographic SNI owing to the newly derived representation of surface normals. The motivation behind the representation is from the observation that spatially uniform image points are simpler for integration than the non-uniform distribution of surface points under perspective projection. The new representation is then manipulated to the so-called log gradient space in analogy to the gradient space in orthographic SNI. With this analogy, the proposed method can inherit most past algorithms developed for orthographic SNI. By applying the proposed SNI, perspective distortion can be efficiently tackled with for smooth surfaces. In addition, the method is PS-independent, which can keep the image irradiance equation in a simple form during PS. The third contribution is the design and calibration of a 3D micro-scale shape reconstruction system using the derived PS and SNI methods. This system is originally designed for on-site measurement of pavement microtexture, while its applicability can be generalized to a wider range of surfaces. Optimal illumination was investigated in theory and through numerical simulations. Five different calibrations regarding various aspects of the system were either newly proposed or modified from existing methods. The performances of these calibrations were individually evaluated. Efficacy of the developed system was finally demonstrated through comprehensive comparative studies with existing systems. Its capability for on-site measurement was also confirmed. / Ph. D.

This paper describes a hand-eye system we developed to perform the binpicking task. Two basic tools are employed: the photometric stereo method and the extended Gaussian image. The photometric stereo method generates the surface normal distribution of a scene. The extended Gaussian image allows us to determine the attitude of the object based on the normal distribution. Visual analysis of an image consists of two stages. The first stage segments the image into regions and determines the target region. The photometric stereo system provides the surface normal distribution of the scene. The system segments the scene into isolated regions using the surface normal distribution rather than the brightness distribution. The second stage determines object attitude and position by comparing the surface normal distribution with the extended-Gaussian-image. Fingers, with LED sensor, mounted on the PUMA arm can successfully pick an object from a pile based on the information from the vision part.

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.

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

The goal of this work is to examine the possibility of surface reconstruction from the images produced by the Tobii H5 eye tracker. It starts of by examining classic photometric stereo and its limitations under the illuminator configuration of the eye tracker. It then proceeds to investigate two alternative solutions: photometric stereo under the assumption that the albedo is known and a method that uses the images from the eye tracker as a guide to mold a reference model into the users face. In the second method the pose of the reference model is estimated by minimizing a photometric error under the assumption that the face is Lambertian, using particle swarm optimization. The position of the generic 3D model is then used in an attempt to mold its shape into the face of the user using shape-from-shading.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23312号 / 情博第748号 / 新制||情||128(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 飯山 将晃, 教授 西野 恒, 教授 中村 裕一, 教授 美濃 導彦(京都大学 名誉教授) / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Image-based 3D reconstruction has been employed in industrial metrology for micro measurements and quality control purposes. However, generating a highly-detailed and reliable 3D reconstruction of non-collaborative surfaces (textureless, shiny, and transparent) is still an open issue. This thesis presents various methodologies to successfully generate a highly-detailed and reliable 3D reconstruction of non-collaborative objects using the proposed photometric stereo image acquisition system. The first proposed method employs geometric construction to integrate photogrammetry and photometric stereo in order to overcome each technique's limitations and to leverage each technique's strengths in order to reconstruct an accurate and high-resolution topography of non-collaborative surfaces. This method uses accurate photogrammetric 3D measurements to rectify the global shape deviation of photometric stereo meanwhile uses photometric stereo to recover the high detailed topography of the object. The second method combines the high spatial frequencies of photometric stereo depth map with the low frequencies of photogrammetric depth map in frequency domain to produce accurate low frequencies while retaining high frequencies. For the third approach, we utilize light directionality to improve texture quality by leveraging shade and shadow phenomena using the proposed image-capturing system that employs several light sources for highlighting roughness and microstructures on the surface. And finally, we present two methods that effectively orient images by leveraging the low-contrast textures highlighted on object surfaces (roughness and 3D microstructures) using proper lighting system. Various objects with different surface characteristics including textureless, reflective, and transparent are used to evaluate different proposed approaches. To assess the accuracy of each approach, a comprehensive comparison between reference data and generated 3D points is provided.