Library

Description: Biological tissues achieve a wide range of properties and function, however with limited components. The organization of these constituent parts is a decisive factor in the impressive properties of biological materials, with tissues often exhibiting complex arrangements of hard and soft materials. The “tessellated” cartilage of the endoskeleton of sharks and rays, for example, is a natural composite of mineralized polygonal tiles (tesserae), collagen fiber bundles, and unmineralized cartilage, resulting in a material that is both flexible and strong, with optimal stiffness. The properties of the materials and the tiling geometry are vital to the growth and mechanics of the system, but had not been investigated due to the technical challenges involved. We use high-resolution materials characterization techniques (qBEI, µCT) to show that tesserae exhibit great variability in mineral density, supporting theories of accretive growth mechanisms. We present a developmental series of tesserae and outline the development of unique structural features that appear to function in load bearing and energy dissipation, with some structural features far exceeding cortical bone’s mineral content and tissue stiffness. To examine interactions among tesserae, we developed an advanced tiling-recognition-algorithm to semi-automatically detect and isolate individual tiles in microCT scans of tesseral mats. The method allows quantification of shape variation across a wide area, allowing localization of regions of high/low reinforcement or flexibility in the skeleton. The combination of our material characterization and visualization techniques allows the first quantitative 3d description of anatomy and material properties of tesserae and the organization of tesseral networks in elasmobranch mineralized cartilage, providing insight into form-function relationships of the repeating tiled pattern. We aim to combine detailed knowledge of intra-tesseral morphology and mineralization to model the relationships of tesseral shapes and skeletal surface curvature, to understand fundamental tiling laws important for complex, mechanically loaded 3d objects.

Description: Source code and novel dataset of basking shark head skeletons facilitating the reproduction of the results presented in 'A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks' - ECCV 2022.

Description: 3D shapes provide substantially more information than 2D images. However, the acquisition of 3D shapes is sometimes very difficult or even impossible in comparison with acquiring 2D images, making it necessary to derive the 3D shape from 2D images. Although this is, in general, a mathematically ill-posed problem, it might be solved by constraining the problem formulation using prior information. Here, we present a new approach based on Kendall’s shape space to reconstruct 3D shapes from single monocular 2D images. The work is motivated by an application to study the feeding behavior of the basking shark, an endangered species whose massive size and mobility render 3D shape data nearly impossible to obtain, hampering understanding of their feeding behaviors and ecology. 2D images of these animals in feeding position, however, are readily available. We compare our approach with state-of-the-art shape-based approaches both on human stick models and on shark head skeletons. Using a small set of training shapes, we show that the Kendall shape space approach is substantially more robust than previous methods and always results in plausible shapes. This is essential for the motivating application in which specimens are rare and therefore only few training shapes are available.