Title: MAELab: a framework to automatize landmark estimation
Authors: Le Van, Linh
Beurton-Aimar, Marie
Krahenbuhl, Adrien
Parisey, Nicolas
Citation: WSCG '2017: short communications proceedings: The 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 29 - June 2 2017, p. 153-158.
Issue Date: 2017
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2017/!!_CSRN-2702.pdf
ISBN: 978-80-86943-45-9
ISSN: 2464-4617
Keywords: morfologie;registrace obrázků;SIFT deskriptor;brouk;mandibula
Keywords in different language: morphology;image registration;SIFT descriptor;beetle;mandible
Abstract: In biology, the morphometric analysis is widely used to analyze the inter-organisms variations. It allows to classify and to determine the evolution of an organism’s family. The morphometric methods consider features such as shape, structure, color, or size of the studied objects. In previous works [8], we have analyzed beetle mandibles by using the centroid as feature, in order to classify the beetles. We have shown that the Probabilistic Hough Transform (PHT) is an efficient unsupervised method to compute the centroid. This paper proposes a new approach to precisely estimate the landmark geometry, points of interest defined by biologists on the mandible contours. In order to automatically register the landmarks on different mandibles, we defined patches around manual landmarks of the reference image. Each patch is described by computing its SIFT descriptor. Considering a query image, we apply a registration step performed by an Iterative Principal Component Analysis which identify the rotation and translation parameters. Then, the patches in the query image are identified and the SIFT descriptors computed. The biologists have collected 293 beetles to provide two sets of mandible images separated into left and right side. The experiments show that, depending on the position of the landmarks on the mandible contour, the performance can go up to 98% of good detection. The complete workflow is implemented in the MAELab framework, freely available as library on GitHub.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2017: Short Papers Proceedings

Files in This Item:
File Description SizeFormat 
Van.pdfPlný text4,67 MBAdobe PDFView/Open

Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/29746

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.