The 12 references with contexts in paper S. Paśko, M. Sutkowski, С. Пасько, М. Сутковский (2016) “ИЗМЕРЕНИЯ АНТРОПОМЕТРИЧЕСКИХ ПАРАМЕТРОВ НА ОСНОВЕ ТЕХНОЛОГИИ ВИЗУАЛИЗАЦИИ ДВИЖЕНИЯ // ANTHROPOMETRIC MEASUREMENT BASED ON STRUCTURE FROM MOTION IMAGING TECHNIQUE” / spz:neicon:pimi:y:2016:i:3:p:305-311

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Xu B., Yu W., Yao M., Reese Pepper M., Freeland-Graves J. H. Three-dimensional surface imaging system for assessing human obesity. Opt. Eng., 2009, October, vol. 48? no. 10, nihpa156427. doi: 10.1117/1.3250191
Total in-text references: 1
  1. In-text reference with the coordinate start=8053
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    The measurements of anthropometric values of the humans’ face are widely used in many different applications, beginning from physical anthropology, medicine (medical analysis, surgery) through security systems (face recognition) to industrial and design (clothing, ergonomics etc.). Most of these values can be determined by use of optical methods
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    [1, 2]
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    . Many of these methods are based on image recognition and image processing. There are divided for two general branches: two-dimensional and 3D data collection [3–5]. Two-dimensional methods are limited by lack of some information (no depth data or only predicted values).

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Stancic I., Supuk T., Cecic M. Computer vision system for human anthropometric parameters estimation. WSEAS TRANSACTIONS on SYSTEMS, 2009, iss. 3, vol. 8, pp. 430–439.
Total in-text references: 1
  1. In-text reference with the coordinate start=8053
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    The measurements of anthropometric values of the humans’ face are widely used in many different applications, beginning from physical anthropology, medicine (medical analysis, surgery) through security systems (face recognition) to industrial and design (clothing, ergonomics etc.). Most of these values can be determined by use of optical methods
    Exact
    [1, 2]
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    . Many of these methods are based on image recognition and image processing. There are divided for two general branches: two-dimensional and 3D data collection [3–5]. Two-dimensional methods are limited by lack of some information (no depth data or only predicted values).

3
Enciso R., Shaw A., Neumann U., Mah J. 3D head anthropometric analysis. Proc. SPIE 5029. Medical Imaging 2003: Visualization, Image-Guided Procedures and Display, 590. doi: 10.1117/12.479752
Total in-text references: 1
  1. In-text reference with the coordinate start=8225
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    Most of these values can be determined by use of optical methods [1, 2]. Many of these methods are based on image recognition and image processing. There are divided for two general branches: two-dimensional and 3D data collection
    Exact
    [3–5]
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    . Two-dimensional methods are limited by lack of some information (no depth data or only predicted values). However some of the applications can successfully adopt them (i.e. face recognition) they has some limitations (i.e. impossibility of measure of important anthropometric values).

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Davis J.P., Valantine T., Davis R.E. Computer assisted photo-anthropometric analyses of full-face and profile facial images. Forensic Sci Int. 2010 Jul 15, vol. 300, no. 1–3, pp. 165–176. doi: 10.1016/j.forsciint.2010.04.012
Total in-text references: 1
  1. In-text reference with the coordinate start=8225
    Prefix
    Most of these values can be determined by use of optical methods [1, 2]. Many of these methods are based on image recognition and image processing. There are divided for two general branches: two-dimensional and 3D data collection
    Exact
    [3–5]
    Suffix
    . Two-dimensional methods are limited by lack of some information (no depth data or only predicted values). However some of the applications can successfully adopt them (i.e. face recognition) they has some limitations (i.e. impossibility of measure of important anthropometric values).

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Smeets D., Claes P., Vandermeulenb D., Clementa J. G. Objective 3D face recognition: Evolution, approaches and challenges. Forensic Sci Int., 2010 Sep 10; vol. 201, no. 1- 3, pp. 125–132. doi: 10.1016/j.forsciint.2010.03.023
Total in-text references: 1
  1. In-text reference with the coordinate start=8225
    Prefix
    Most of these values can be determined by use of optical methods [1, 2]. Many of these methods are based on image recognition and image processing. There are divided for two general branches: two-dimensional and 3D data collection
    Exact
    [3–5]
    Suffix
    . Two-dimensional methods are limited by lack of some information (no depth data or only predicted values). However some of the applications can successfully adopt them (i.e. face recognition) they has some limitations (i.e. impossibility of measure of important anthropometric values).

6
Fitzgibbon A.W., Zisserman A. Automatic camera recovery for closed or open image sequences. Computer Vision – ECCV’98, vol. 1406 of the series Lecture Notes in Computer Science, pp. 311–326. doi: 10.1007/BFb0055675
Total in-text references: 1
  1. In-text reference with the coordinate start=8903
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    In the paper a very simple imaging system with no calibration demand for anthropometry is presented. The system is based on widely known Structure from Motion (SfM) technique
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    [6–9]
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    . With use of SfM reconstruction of shape of the object from multiple camera views can be realized. The SfM theorem assume unknown or partially unknown parameters of the camera which may change in time additionally.

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Muhamad S., Hebert M. Iterative projective reconstruction from multiple views. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ‘00). 2000, june, vol. 2, pp. 430–437.
Total in-text references: 1
  1. In-text reference with the coordinate start=8903
    Prefix
    In the paper a very simple imaging system with no calibration demand for anthropometry is presented. The system is based on widely known Structure from Motion (SfM) technique
    Exact
    [6–9]
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    . With use of SfM reconstruction of shape of the object from multiple camera views can be realized. The SfM theorem assume unknown or partially unknown parameters of the camera which may change in time additionally.

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Faugeras O., Luong Q.T., Papadopoulo T. The geometry of multiple images, The MIT Press, 2001.
Total in-text references: 1
  1. In-text reference with the coordinate start=8903
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    In the paper a very simple imaging system with no calibration demand for anthropometry is presented. The system is based on widely known Structure from Motion (SfM) technique
    Exact
    [6–9]
    Suffix
    . With use of SfM reconstruction of shape of the object from multiple camera views can be realized. The SfM theorem assume unknown or partially unknown parameters of the camera which may change in time additionally.

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Liu L., Stamos I., Yu G., Wolberg G., Zokai S. Multiview geometry for texture mapping 2D images onto 3D range data. Proc. Computer Vision and Pattern Recognition. 2006, IEEE Computer Society Conference, 2006, vol. 2, pp. 2293–2300. doi: 10.1109/CVPR.2006.204
Total in-text references: 1
  1. In-text reference with the coordinate start=8903
    Prefix
    In the paper a very simple imaging system with no calibration demand for anthropometry is presented. The system is based on widely known Structure from Motion (SfM) technique
    Exact
    [6–9]
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    . With use of SfM reconstruction of shape of the object from multiple camera views can be realized. The SfM theorem assume unknown or partially unknown parameters of the camera which may change in time additionally.

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Wu Ch. Towards Linear-time Incremental Structure From Motion. Proceedings of the 2013. International Conference on 3D Vision. IEEE Computer Society Washington, DC, pp. 127–134. doi: 10.1109/3DV.2013.25
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  1. In-text reference with the coordinate start=14888
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    Obj. – position of the object (patient or examined person); P1...Pn – points where the consequent images was taken from (for convenience only start-point and end-point are marked) Taken images were directed to the reconstruction software which in this case was a VisualSFM v 0.5.25
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    . The calculation was performed on the PC-machine equipped with CPU Intel Core i5 M520@2.40GHz, memory RAM 8GB and operating system Windows 8.1 Pro 64-bit. A series of 42 images was enough to make reconstruction of the 3D model of the scene.

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Wu Ch. VisualSFM: A Visual Structure from Motion System [Website of Synkera Technologies, Inc.]. Available at: http://ccwu.me/vsfm/ (accessed 31.10.2016).
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  1. In-text reference with the coordinate start=14888
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    Obj. – position of the object (patient or examined person); P1...Pn – points where the consequent images was taken from (for convenience only start-point and end-point are marked) Taken images were directed to the reconstruction software which in this case was a VisualSFM v 0.5.25
    Exact
    [10, 11]
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    . The calculation was performed on the PC-machine equipped with CPU Intel Core i5 M520@2.40GHz, memory RAM 8GB and operating system Windows 8.1 Pro 64-bit. A series of 42 images was enough to make reconstruction of the 3D model of the scene.

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Website of Synkera Technologies, Inc.]. Available at: https://commons.wikimedia.org/wiki/ File:Sfm1.jpg (accessed 20.02.2016).
Total in-text references: 1
  1. In-text reference with the coordinate start=11987
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    A series of images taken from fixed points (on the figure points P1, P2, P3 shows few selected camera positions only) is needed. Numerical calculations allow to determine relative 3D coordinates of the object’s points. Collecting data of many points lead to shape reconstruction
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    . Figure 1 – The basic idea of object’s point estimation with use of SfM (by commons.wikimedia.org) Shape reconstruction Experimental set-up used in our experiment consists of the consumer type digital photographic camera (Nikon D610, full frame sensor, 24.3 million of effective pixels) with varifocal lens (Nikkor 24– 85 mm) and a diffused light source (for proper and s