Thesis: Matching, Archiving and Visualizing Cultural Heritage Artifacts Using Multi-Channel Images
Matching, Archiving and Visualizing Cultural Heritage Artifacts Using Multi-Channel Images
PhD thesis, Princeton University, May 2011.
Recent advancements in low-cost acquisition technologies have made it more practical to acquire real world datasets on a large scale. This has lead to a number of computer-based solutions for reassembling, archiving and visualizing cultural heritage artifacts. In this the- sis, we combine aspects of these technologies in novel ways and introduce algorithms to improve upon their overall efficiency and robustness. First, we introduce a 2-D acquisition system to address the challenge of acquiring higher resolution color and normal maps for large datasets than those available with 3-D scanning devices. Next, we incorporate our normal maps into a novel multi-cue matching system for reassembling small fragments of artifacts. We then present a non-photorealistic rendering pipeline for illustrating geometrically complex objects using images with multiple channels of information.
State-of-the-art 3-D acquisition systems capture 3-D geometry at archeological sites using affordable, off-the-shelf scanners. Although multiple scans at varying viewpoints are required to assemble a complete model, robust registration and alignment algorithms, as well as new work-flow methodologies, significantly reduce the post-processing time. How- ever, the color and normal maps obtained from these systems lack the subtle sub-millimeter details necessary for careful analysis, and high fidelity documentation. We introduce an algorithm that generates higher resolution normal maps and diffuse reflectance (true color texture), while minimizing acquisition time. Using shape from shading, we compute our normal maps from high resolution color scans of the object taken at four orientations on a 2-D flatbed scanner. A key contribution of our work is a novel calibration process to measure the observed brightness as a function of the surface normal. This calibration is important because the scanner’s light is linear (rather than a point), and we cannot solve for the surface normal using the traditional formulation of the Lambertian lighting law. High resolution digital SLR cameras provide alternative solutions when objects are too large or fragile to place on a scanner. However, they require more control over the ambient light in the environment and additional manual effort to continually reposition a hand-held flash. They lack the high resolutions we obtain from the scanner.
Several projects have been explored to leverage these newly acquired datasets for digital reassembly, and have proven successful in some domains. However, current matching algorithms do not perform well when artifacts have deteriorated over many years. One limitation is their reliance on previous acquisition methods that do not capture fine surface details. These details are often important matching cues when features such as color, 2-D contours or 3-D geometry are no longer reliable. We introduce a set of feature descriptors that are based not only on color and shape, but also normal maps with a high data quality. Rather than rely exclusively on one form of data, we use machine-learning techniques to combine descriptors in a multi-cue matching framework. We have tested our system on three datasets of fresco fragments: Theran Frescoes from the site of Akrotiri, Greece; Roman frescoes from Kerkrade in the Netherlands; and a Synthetic fresco created by con- servators in a style similar to Akrotiri frescoes. We demonstrate that multi-cue matching using different subsets of features leads to different tradeoffs between efficiency and effectiveness. We observe that individual feature performance varies from dataset to dataset and discuss the implications of feature importance for matching in this domain. Our results show good retrieval performance, significantly improving upon the match prediction rate of state-of the-art 3-D matching algorithms.
The Illustrative depictions found in biology or medical textbooks are one possible method of archiving and distributing historic information. Using a datatype that stores both color and normals, RGBN images, we develop 2-D analogs to 3-D NPR rendering equations. Our approach extends signal processing tools such as scale-space analysis and segmentation for this new data type. We investigate stylized depiction techniques such as toon shading, line drawing and exaggerated shading. By incorporating some 3-D information, we reveal fine details while maintaining the simplicity of a 2-D implementation. Our results achieve levels of detail that are impractical to create with more conventional methods like manual 3-D modeling or 3-D scanning