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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