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      Capturing Natural-Colour 3D Models of Insects for Species Discovery and Diagnostics

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          Abstract

          Collections of biological specimens are fundamental to scientific understanding and characterization of natural diversity—past, present and future. This paper presents a system for liberating useful information from physical collections by bringing specimens into the digital domain so they can be more readily shared, analyzed, annotated and compared. It focuses on insects and is strongly motivated by the desire to accelerate and augment current practices in insect taxonomy which predominantly use text, 2D diagrams and images to describe and characterize species. While these traditional kinds of descriptions are informative and useful, they cannot cover insect specimens “from all angles” and precious specimens are still exchanged between researchers and collections for this reason. Furthermore, insects can be complex in structure and pose many challenges to computer vision systems. We present a new prototype for a practical, cost-effective system of off-the-shelf components to acquire natural-colour 3D models of insects from around 3 mm to 30 mm in length. (“Natural-colour” is used to contrast with “false-colour”, i.e., colour generated from, or applied to, gray-scale data post-acquisition.) Colour images are captured from different angles and focal depths using a digital single lens reflex (DSLR) camera rig and two-axis turntable. These 2D images are processed into 3D reconstructions using software based on a visual hull algorithm. The resulting models are compact (around 10 megabytes), afford excellent optical resolution, and can be readily embedded into documents and web pages, as well as viewed on mobile devices. The system is portable, safe, relatively affordable, and complements the sort of volumetric data that can be acquired by computed tomography. This system provides a new way to augment the description and documentation of insect species holotypes, reducing the need to handle or ship specimens. It opens up new opportunities to collect data for research, education, art, entertainment, biodiversity assessment and biosecurity control.

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          Accurate, dense, and robust multiview stereopsis.

          This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark shows that the proposed method outperforms all others submitted so far for four out of the six data sets.
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            The visual hull concept for silhouette-based image understanding

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              Computer Vision : Algorithms and Applications

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                23 April 2014
                : 9
                : 4
                : e94346
                Affiliations
                [1 ]Computational Informatics, Commonwealth Scientific and Industrial Research Organization (CSIRO), Canberra, ACT, Australia
                [2 ]Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organization (CSIRO), Canberra, ACT, Australia
                [3 ]Atlas of Living Australia, Canberra, ACT, Australia
                Institut National de la Recherche Agronomique (INRA), France
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: CVN. Performed the experiments: CVN. Analyzed the data: CVN. Contributed reagents/materials/analysis tools: CVN DRL MA JLS. Wrote the paper: CVN DRL MA JLS. Created, guided and informed the project: DRL JLS MA.

                Article
                PONE-D-13-53186
                10.1371/journal.pone.0094346
                3997343
                24759838
                bbdd9a6e-a109-46ad-a7c0-a7963540c704
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 December 2013
                : 13 March 2014
                Page count
                Pages: 11
                Funding
                The work is funded by CSIRO OCE Science, CSIRO Tranformational Biology TCP, CSIRO Computational Informatics BSA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cytometry
                Molecular Cell Biology
                Ecology
                Ecological Metrics
                Evolutionary Biology
                Evolutionary Systematics
                Taxonomy
                Animal Taxonomy
                Zoology
                Computer and Information Sciences
                Computer Applications
                Web-Based Applications
                Computer Modeling
                Computing Methods
                Computer Graphics
                Information Technology
                Databases
                Data Mining
                Ecology and Environmental Sciences
                Engineering and Technology
                Aerospace Engineering
                Avionics
                Synthetic Vision Systems
                Electrical Engineering
                Electronics Engineering
                Computer Engineering
                Signal Processing
                Image Processing
                Research and Analysis Methods
                Spectrum Analysis Techniques
                Spectrophotometry
                Fluorimetry
                Image Cytometry

                Uncategorized
                Uncategorized

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