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Centennial Campus, 1791 Varsity Dr. Get the latest tips, news, and developments. Please forward this error screen to 216. These complex structures, which have risen from hundreds of million years of evolution, are inspiring Materials Scientists in the design of novel materials. Their defining characteristics, hierarchy, multifunctionality, and self-healing capability, are illustrated. Self-organization is also a fundamental feature of many biological materials and the manner by which the structures are assembled from the molecular level up.
There are over 1000 proteins, and we describe only the principal ones, with emphasis on collagen, chitin, keratin, and elastin. The most important mineral phases are discussed: hydroxyapatite, silica, and aragonite. Using the classification of Wegst and Ashby, the principal mechanical characteristics and structures of biological ceramics, polymer composites, elastomers, and cellular materials are presented. Selected systems in each class are described with emphasis on the relationship between their structure and mechanical response. A fifth class is added to this: functional biological materials, which have a structure developed for a specific function: adhesion, optical properties, etc. An outgrowth of this effort is the search for bioinspired materials and structures.
Traditional approaches focus on design methodologies of biological materials using conventional synthetic materials. Although this approach is still in its infancy, it will eventually lead to a plethora of new materials systems as we elucidate the fundamental mechanisms of growth and the structure of biological systems. Check if you have access through your login credentials or your institution. We present an overview of computational approaches to biological vision. We develop a task centered presentation of biological vision studies.
We revisit three tasks: image sensing, scene segmentation and optical flow. We show how new computer vision methods could be developed from biological insights. We identify key task specific biological vision models which could be scaled up. Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations.