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Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices

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Abstract

The projection of a real world scenery to a planar image sensor inherits the loss of information about the 3D structure as well as the absolute dimensions of the scene. For image analysis and object classification tasks, however, absolute size information can make results more accurate. Today, the creation of size annotated image datasets is effort intensive and typically requires measurement equipment not available to public image contributors. In this paper, we propose an effective annotation method that utilizes the camera within smart mobile devices to capture the missing size information along with the image. The approach builds on the fact that with a camera, calibrated to a specific object distance, lengths can be measured in the object’s plane. We use the camera’s minimum focus distance as calibration distance and propose an adaptive feature matching process for precise computation of the scale change between two images facilitating measurements on larger object distances. Eventually, the measured object is segmented and its size information is annotated for later analysis. A user study showed that humans are able to retrieve the calibration distance with a low variance. The proposed approach facilitates a measurement accuracy comparable to manual measurement with a ruler and outperforms state-of-the-art methods in terms of accuracy and repeatability. Consequently, the proposed method allows in-situ size annotation of objects in images without the need for additional equipment or an artificial reference object in the scene.

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Notes

  1. https://thoth.inrialpes.fr/people/mikolajczyk/Database/zoom.html.

  2. http://roboimagedata.compute.dtu.dk/?page_id=24.

References

  • Aanæs, H., Dahl, A. L., & Perfanov, V. (2010). A ground truth data set for two view image matching. Technical report, DTU Informatics, Technical University of Denmark. http://roboimagedata.imm.dtu.dk/papers/technicalReport.pdf.

  • Aanæs, H., Dahl, A. L., & Steenstrup Pedersen, K. (2011). Interesting interest points. International Journal of Computer Vision, 97(1), 18–35. https://doi.org/10.1007/s11263-011-0473-8.

    Article  Google Scholar 

  • Agarwal, S. (2009). R.: Building rome in a day. In International conference on computer vision (ICCV).

  • Apple Inc. (2017). Arkit. https://developer.apple.com/arkit/.

  • Arandjelovic, R., & Zisserman, A. (2012). Three things everyone should know to improve object retrieval. In 2012 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2911–2918). https://doi.org/10.1109/CVPR.2012.6248018.

  • Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3), 346–359.

    Article  Google Scholar 

  • Bradski, G. (2000). The OpenCV library. Dr Dobb’s Journal of Software Tools, 25, 120–123.

    Google Scholar 

  • Bursuc, A., Tolias, G., & Jégou, H. (2015). Kernel local descriptors with implicit rotation matching. In Proceedings of the 5th ACM on international conference on multimedia retrieval (pp. 595–598). ACM, New York, NY, USA, ICMR ’15. https://doi.org/10.1145/2671188.2749379.

  • Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., et al. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. https://doi.org/10.1109/TRO.2016.2624754.

    Article  Google Scholar 

  • Criminisi, A., Reid, I., & Zisserman, A. (1999). A plane measuring device. Image and Vision Computing, 17(8), 625–634.

    Article  Google Scholar 

  • Criminisi, A., Reid, I., & Zisserman, A. (2000). Single view metrology. International Journal of Computer Vision, 40(2), 123–148. https://doi.org/10.1023/A:1026598000963.

    Article  MATH  Google Scholar 

  • Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007). Monoslam: Real-time single camera slam. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 1052–1067.

    Article  Google Scholar 

  • Dong, J., & Soatto, S. (2015). Domain-size pooling in local descriptors: Dsp-sift. In 2015 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5097–5106). https://doi.org/10.1109/CVPR.2015.7299145.

  • Eigen, D., & Fergus, R. (2015). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In 2015 IEEE international conference on computer vision (ICCV) (pp. 2650–2658). https://doi.org/10.1109/ICCV.2015.304.

  • Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2), 303–338.

    Article  Google Scholar 

  • Fuentes-Pacheco, J., Ruiz-Ascencio, J., & Rendón-Mancha, J. M. (2015). Visual simultaneous localization and mapping: A survey. Artificial Intelligence Review, 43(1), 55–81.

    Article  Google Scholar 

  • Google Inc. (2017). Arcore. https://developers.google.com/ar/.

  • Harris, C., & Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the alvey vision conference (pp. 23.1–23.6). Alvety Vision Club. https://doi.org/10.5244/C.2.23.

  • Karlsson, N., di Bernardo, E., Ostrowski, J., Goncalves, L., Pirjanian, P., & Munich, M. E. (2005). The vslam algorithm for robust localization and mapping. In Proceedings of the 2005 IEEE international conference on robotics and automation (pp. 24–29). https://doi.org/10.1109/ROBOT.2005.1570091.

  • Ke, Y., & Sukthankar, R. (2004). Pca-sift: A more distinctive representation for local image descriptors. In Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004 (Vol. 2, pp. II–506–II–513). CVPR 2004. https://doi.org/10.1109/CVPR.2004.1315206.

  • Kim, H., Richardt, C., & Theobalt, C. (2016). Video depth-from-defocus. In 2016 fourth international conference on 3D vision (3DV) (pp. 370–379). IEEE.

  • Klein, G., & Murray, D. (2007). Parallel tracking and mapping for small ar workspaces. In 2007 6th IEEE and ACM international symposium on mixed and augmented reality (pp. 225–234). https://doi.org/10.1109/ISMAR.2007.4538852.

  • Koenderink, J. J., & van Doorn, A. J. (1991). Affine structure from motion. Journal of the Optical Society of America A, 8(2), 377–385. https://doi.org/10.1364/JOSAA.8.000377.

    Article  Google Scholar 

  • Kuhl, A., Wöhler, C., Krüger, L., d’Angelo, P., & Groß, H. M. (2006). Monocular 3D scene reconstruction at absolute scales by combination of geometric and real-aperture methods (pp. 607–616). Berlin, Heidelberg: Springer. https://doi.org/10.1007/11861898_61.

    Book  Google Scholar 

  • Lai, K., Bo, L., Ren, X., & Fox, D. (2011). A large-scale hierarchical multi-view rgb-d object dataset. In 2011 IEEE international conference on robotics and automation (pp. 1817–1824). https://doi.org/10.1109/ICRA.2011.5980382.

  • Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., & Furgale, P. (2015). Keyframe-based visualinertial odometry using nonlinear optimization. The International Journal of Robotics Research, 34(3), 314–334. https://doi.org/10.1177/0278364914554813.

    Article  Google Scholar 

  • Levin, A., Fergus, R., Durand, F., & Freeman, W. T. (2007). Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics (TOG), 26(3), 70.

    Article  Google Scholar 

  • Li, J., & Allinson, N. M. (2008). A comprehensive review of current local features for computer vision. Neurocomputing, 71(1012), 17711787. https://doi.org/10.1016/j.neucom.2007.11.032.

    Article  Google Scholar 

  • Lin, J., Ji, X., Xu, W., & Dai, Q. (2013). Absolute depth estimation from a single defocused image. IEEE Transactions on Image Processing, 22(11), 4545–4550. https://doi.org/10.1109/TIP.2013.2274389.

    Article  Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  MathSciNet  Google Scholar 

  • Luhmann, T., Robson, S., Kyle, S., & Harley, I. (2006). Close range photogrammetry: Principles, methods and applications. Dunbeath: Whittles.

    Google Scholar 

  • McGuinness, K., & O’Connor, N. E. (2010). A comparative evaluation of interactive segmentation algorithms. Pattern Recognition, 43(2), 434–444. https://doi.org/10.1016/j.patcog.2009.03.008.

    Article  MATH  Google Scholar 

  • Mikolajczyk, K., & Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86. https://doi.org/10.1023/B:VISI.0000027790.02288.f2.

    Article  Google Scholar 

  • Moeller, M., Benning, M., Schnlieb, C., & Cremers, D. (2015). Variational depth from focus reconstruction. IEEE Transactions on Image Processing, 24(12), 5369–5378. https://doi.org/10.1109/TIP.2015.2479469.

    Article  MathSciNet  MATH  Google Scholar 

  • Moreels, P., & Perona, P. (2006). Evaluation of features detectors and descriptors based on 3d objects. International Journal of Computer Vision, 73(3), 263–284. https://doi.org/10.1007/s11263-006-9967-1.

    Article  Google Scholar 

  • Mur-Artal, R., Montiel, J. M. M., & Tards, J. D. (2015). Orb-slam: A versatile and accurate monocular slam system. IEEE Transactions on Robotics, 31(5), 1147–1163. https://doi.org/10.1109/TRO.2015.2463671.

    Article  Google Scholar 

  • Mur-Artal, R., & Tards, J. D. (2017). Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Transactions on Robotics, 33(5), 1255–1262. https://doi.org/10.1109/TRO.2017.2705103.

    Article  Google Scholar 

  • Mustafah, Y. M., Noor, R., Hasbi, H., & Azma, A. W. (2012). Stereo vision images processing for real-time object distance and size measurements. In 2012 international conference on computer and communication engineering (ICCCE) (pp. 659–663). https://doi.org/10.1109/ICCCE.2012.6271270.

  • Nayar, S. K., & Nakagawa, Y. (1994). Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(8), 824–831. https://doi.org/10.1109/34.308479.

    Article  Google Scholar 

  • Nitzan, D. (1985). Development of intelligent robots: Achievements and issues. IEEE Journal on Robotics and Automation, 1(1), 3–13.

    Article  Google Scholar 

  • Peng, B., Zhang, L., & Zhang, D. (2013). A survey of graph theoretical approaches to image segmentation. Pattern Recognition, 46(3), 1020–1038. https://doi.org/10.1016/j.patcog.2012.09.015.

    Article  Google Scholar 

  • Pentland, A. P. (1987). A new sense for depth of field. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, 9(4), 523–531. https://doi.org/10.1109/TPAMI.1987.4767940.

    Article  Google Scholar 

  • Piasco, N., Sidib, D., Demonceaux, C., & Gouet-Brunet, V. (2018). A survey on visual-based localization: On the benefit of heterogeneous data. Pattern Recognition, 74, 90–109. https://doi.org/10.1016/j.patcog.2017.09.013.

    Article  Google Scholar 

  • Robertson, P., Frassl, M., Angermann, M., Doniec, M., Julian, B. J., Puyol, M. G., Khider, M., Lichtenstern, M., & Bruno, L. (2013). Simultaneous localization and mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments. In International conference on indoor positioning and indoor navigation (pp. 1–10). https://doi.org/10.1109/IPIN.2013.6817910.

  • Rother, C., Kolmogorov, V., & Blake, A. (2004). Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23(3), 309–314. https://doi.org/10.1145/1015706.1015720.

    Article  Google Scholar 

  • Rzanny, M., Seeland, M., Wäldchen, J., & Mäder, P. (2017). Acquiring and preprocessing leaf images for automated plant identification: Understanding the tradeoff between effort and information gain. Plant Methods, 13(1), 97. https://doi.org/10.1186/s13007-017-0245-8.

    Article  Google Scholar 

  • Saxena, A., Sun, M., & Ng, A. Y. (2009). Make3d: Learning 3d scene structure from a single still image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 824–840.

    Article  Google Scholar 

  • Schönberger, J. L., Hardmeier, H., Sattler, T., & Pollefeys, M. (2017). Comparative evaluation of hand-crafted and learned local features. In Conference on computer vision and pattern recognition (CVPR)

  • Seeland, M., Rzanny, M., Alaqraa, N., Wäldchen, J., & Mäder, P. (2017). Plant species classification using flower imagesa comparative study of local feature representations. PLoS ONE, 12(2), e0170,629.

    Article  Google Scholar 

  • Smith, R. C., & Cheeseman, P. (1986). On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research, 5(4), 56–68.

    Article  Google Scholar 

  • Subbarao, M., & Surya, G. (1994). Depth from defocus: A spatial domain approach. International Journal of Computer Vision, 13(3), 271–294. https://doi.org/10.1007/BF02028349.

    Article  Google Scholar 

  • Thrun, S., et al. (2002). Robotic mapping: A survey. Exploring Artificial Intelligence in the New Millennium, 1, 1–35.

    Google Scholar 

  • Torralba, A., Murphy, K. P., & Freeman, W. T. (2004). Sharing features: Efficient boosting procedures for multiclass object detection. In Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004 (Vol. 2, pp. II–762–II–769). CVPR 2004. https://doi.org/10.1109/CVPR.2004.1315241.

  • Tuytelaars, T., & Mikolajczyk, K. (2008). Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 3(3), 177–280. https://doi.org/10.1561/0600000017.

    Article  Google Scholar 

  • Uhrig, J., Cordts, M., Franke, U., & Brox, T. (2016). Pixel-level encoding and depth layering for instance-level semantic labeling (pp. 14–25). Cham: Springer. https://doi.org/10.1007/978-3-319-45886-1_2.

    Book  Google Scholar 

  • Wäldchen, J., & Mäder, P. (2018). Plant species identification using computer vision techniques: A systematic literature review. Archives of Computational Methods in Engineering, 25(2), 507–543. https://doi.org/10.1007/s11831-016-9206-z.

    Article  MathSciNet  MATH  Google Scholar 

  • Wäldchen, J., Rzanny, M., Seeland, M., & Mäder, P. (2018). Automated plant species identificationtrends and future directions. PLoS Computational Biology, 14(4), e1005,993.

    Article  Google Scholar 

  • Watanabe, M., & Nayar, S. K. (1998). Rational filters for passive depth from defocus. International Journal of Computer Vision, 27(3), 203–225. https://doi.org/10.1023/A:1007905828438.

    Article  Google Scholar 

  • Williams, B., Cummins, M., Neira, J., Newman, P., Reid, I., & Tards, J. (2009). A comparison of loop closing techniques in monocular slam. Robotics and Autonomous Systems, 57(12), 1188–1197. https://doi.org/10.1016/j.robot.2009.06.010.

    Article  Google Scholar 

  • Wittich, H. C., Seeland, M., Wäldchen, J., Rzanny, M., & Mäder, P. (2018). Recommending plant taxa for supporting on-site species identification. BMC Bioinformatics, 19. https://doi.org/10.1186/s12859-018-2201-7

  • ygx2011. (2017). Orb slam2 ios. https://github.com/ygx2011/ORB_SLAM2-IOS.

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Acknowledgements

We would like to thank all participants of our user experiment for supporting our work. We are funded through a scholarship of the Friedrich Naumann Stiftung; the German Ministry of Education and Research (BMBF) Grants: 01LC1319A and 01LC1319B; the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) Grant: 3514 685C19; and the Stiftung Naturschutz Thüringen (SNT) Grant: SNT-082-248-03/2014.

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  1. Technische Universität Ilmenau, Ilmenau, Germany

    Martin Hofmann, Marco Seeland & Patrick Mäder

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  1. Martin Hofmann
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Correspondence to Martin Hofmann.

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Communicated by V. Lepetit.

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Hofmann, M., Seeland, M. & Mäder, P. Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices. Int J Comput Vis 127, 207–224 (2019). https://doi.org/10.1007/s11263-018-1093-3

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