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Invariant Methods in Computer Vision

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Computer Vision

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Definition

The term invariant methods in computer vision refers to a broad class of ideas for designing both representations and metrics that are invariant/robust to (and only to) nuisance factors in computer vision such as viewpoint, motion, defocus, etc. for different related modalities including images, videos, and point clouds.

Background

Computer vision consists of inferring geometric and semantic properties of objects and scenes, from 2D projections as seen through cameras. This theme appears in applications such as object recognition, localization, segmentation, 3D reconstruction, etc. As canonical examples, we will focus on object, scene, and action recognition. Usually, these tasks need to be performed given a single image of the object/scene or a video. That is, we usually do not have access to the full 3D structure of the object or scenes, but have 2D projections obtained using a camera. As such, a lot of...

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References

  1. Vedaldi A (2008) Invariant representations and learning for computer vision. Ph.D. thesis, Citeseer

    Google Scholar 

  2. Simard PY, LeCun YA, Denker JS, Victorri B (1998) Transformation invariance in pattern recognition-tangent distance and tangent propagation. In: Neural networks: tricks of the trade. Springer, pp 239–274

    Google Scholar 

  3. Werman M (2014) Affine invariants. Springer US, Boston, pp 20–22

    Google Scholar 

  4. Flusser J, Suk T (1993) Pattern recognition by affine moment invariants. Pattern Recognit 26(1):167–174

    Article  MathSciNet  Google Scholar 

  5. Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  6. Jaderberg M, Simonyan K, Zisserman A et al (2015) Spatial transformer networks. In: Advances in neural information processing systems, pp 2017–2025

    Google Scholar 

  7. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856–3866

    Google Scholar 

  8. Srivastava A, Klassen EP (2016) Functional and shape data analysis. Springer, New York

    Book  Google Scholar 

  9. Flusser J, Suk T, Boldyš J, Zitová B (2014) Projection operators and moment invariants to image blurring. IEEE Trans Pattern Anal Mach Intell 37(4):786–802

    Article  Google Scholar 

  10. Zhang Z, Klassen E, Srivastava A, Turaga P, Chellappa R (2011) Blurring-invariant Riemannian metrics for comparing signals and images. In: 2011 international conference on computer vision, pp 1770–1775. IEEE

    Google Scholar 

  11. Gopalan R, Taheri S, Turaga P, Chellappa R (2012) A blur-robust descriptor with applications to face recognition. IEEE Trans Pattern Anal Mach Intell 34(6):1220–1226

    Article  Google Scholar 

  12. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1): 43–49

    Article  Google Scholar 

  13. Srivastava A, Klassen E, Joshi SH, Jermyn IH (2010) Shape analysis of elastic curves in Euclidean spaces. IEEE Trans Pattern Anal Mach Intell 33(7):1415–1428

    Article  Google Scholar 

  14. Veeraraghavan A, Srivastava A, Roy-Chowdhury AK, Chellappa R (2009) Rate-invariant recognition of humans and their activities. IEEE Trans Image Process 18(6):1326–1339

    Article  MathSciNet  Google Scholar 

  15. Lohit S, Wang Q, Turaga P (2019) Temporal transformer networks: joint learning of invariant and discriminative time warping. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 12426–12435

    Google Scholar 

  16. Hallinan PW et al (1994) A low-dimensional representation of human faces for arbitrary lighting conditions. In: CVPR, vol 94, pp 995–999

    Google Scholar 

  17. Belhumeur PN, Kriegman DJ (1998) What is the set of images of an object under all possible illumination conditions? Int J Comput Vis 28(3):245–260

    Article  Google Scholar 

  18. Basri R, Jacobs DW (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25:218–233

    Article  Google Scholar 

  19. Lohit S, Turaga P (2017) Learning invariant Riemannian geometric representations using deep nets. In: Proceedings of the IEEE international conference on computer vision, pp 1329–1338

    Google Scholar 

  20. Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 652–660

    Google Scholar 

  21. Reiss TH (ed) (1993) Invariance to projective transformations. Springer Berlin/Heidelberg, pp 101–114

    Google Scholar 

  22. Yu G, Morel J-M (2011) ASIFT: an algorithm for fully affine invariant comparison. Image Processing On Line 1:11–38

    Google Scholar 

  23. Dey TK, Mandal S, Varcho W (2017) Improved image classification using topological persistence. In: Vision, modeling & visualization, VMV 2017, Bonn, 25–27 Sept 2017, pp 161–168

    Google Scholar 

  24. Vemulapalli R, Arrate F, Chellappa R (2014) Human action recognition by representing 3D skeletons as points in a lie group. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 588–595

    Google Scholar 

  25. Chen H, Belhumeur P, Jacobs D (2000) In search of illumination invariants. In: Proceedings IEEE conference on computer vision and pattern recognition, CVPR 2000 (Cat. No. PR00662), vol 1. IEEE, pp 254–261

    Google Scholar 

  26. Ho J, Kriegman D (2005) On the effect of illumination and face recognition. In: In face processing: advanced modeling and methods. Citeseer

    Google Scholar 

  27. LeCun Y, Huang FJ, Bottou L (2004) Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Book  Google Scholar 

  28. Goodfellow I, Lee H, Le QV, Saxe A, Ng AY (2009) Measuring invariances in deep networks. In: Advances in neural information processing systems, pp 646–654

    Google Scholar 

  29. Erhan D, Courville A, Bengio Y (2010) Understanding representations learned in deep architectures. Technical Report

    Google Scholar 

  30. Fawzi A, Frossard P (2015) Manitest: are classifiers really invariant? In: British machine vision conference

    Google Scholar 

  31. Dodge S, Karam L (2016) Understanding how image quality affects deep neural networks. In: 2016 eighth international conference on quality of multimedia experience (QoMEX), pp 1–6. IEEE

    Google Scholar 

  32. Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2014) Intriguing properties of neural networks. In: International conference on learning representations

    Google Scholar 

  33. Rifai S, Mesnil G, Vincent P, Muller X, Bengio Y, Dauphin Y, Glorot X (2011) Higher order contractive auto-encoder. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 645–660

    Google Scholar 

  34. Bruna J, Mallat S (2013) Invariant scattering convolution networks. IEEE Trans Pattern Anal Mach Intell 35(8):1872–1886

    Article  Google Scholar 

  35. Xu Y, Xiao T, Zhang J, Yang K, Zhang Z (2014) Scale-invariant convolutional neural networks. arXiv preprint arXiv:1411.6369

    Google Scholar 

  36. Kulkarni TD, Whitney WF, Kohli P, Tenenbaum J (2015) Deep convolutional inverse graphics network. In: Advances in neural information processing systems, pp 2539–2547

    Google Scholar 

  37. Kim H, Mnih A (2018) Disentangling by factorising. In: International conference on machine learning, pp 2654–2663

    Google Scholar 

  38. Shu Z, Sahasrabudhe M, R. Alp Guler, Samaras D, Paragios N, Kokkinos I (2018) Deforming autoencoders: unsupervised disentangling of shape and appearance. In: Proceedings of the European conference on computer vision (ECCV), pp 650–665

    Google Scholar 

  39. Koneripalli K, Lohit S, Anirudh R, Turaga P (2020) Rate-invariant autoencoding of time-series. In: IEEE international conference on acoustics, speech and signal processing (ICASSP)

    Google Scholar 

  40. Shukla A, Bhagat S, Uppal S, Anand S, Turaga P. Product of orthogonal spheres parameterization for disentangled representation learning. In: British machine vision conference (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA

    Suhas Lohit

  2. Arizona State University, Tempe, AZ, USA

    Pavan Turaga

  3. Rice University, Houston, TX, USA

    Ashok Veeraraghavan

Authors
  1. Suhas Lohit
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  2. Pavan Turaga
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  3. Ashok Veeraraghavan
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Corresponding author

Correspondence to Pavan Turaga .

Section Editor information

  1. University of Maryland, College Park, MD, USA

    Rama Chellappa

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Lohit, S., Turaga, P., Veeraraghavan, A. (2020). Invariant Methods in Computer Vision. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_826-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_826-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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