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Detection and localization of specular surfaces using image motion cues

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Abstract

Successful identification of specularities in an image can be crucial for an artificial vision system when extracting the semantic content of an image or while interacting with the environment. We developed an algorithm that relies on scale and rotation invariant feature extraction techniques and uses motion cues to detect and localize specular surfaces. Appearance change in feature vectors is used to quantify the appearance distortion on specular surfaces, which has previously been shown to be a powerful indicator for specularity (Doerschner et al. in Curr Biol, 2011). The algorithm combines epipolar deviations (Swaminathan et al. in Lect Notes Comput Sci 2350:508–523, 2002) and appearance distortion, and succeeds in localizing specular objects in computer-rendered and real scenes, across a wide range of camera motions and speeds, object sizes and shapes, and performs well under image noise and blur conditions.

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Notes

  1. But see [19] who use only minimal assumptions about the scene, motion and 3D shape.

  2. Also see [28] specular shape perception in human observers.

  3. When referring to matte or diffusely reflecting objects, we imply that these objects also have a 2D texture.

  4. It may be that bi-modality does not work well as a global parameter; however, at a particular spatial scale it may continue to correctly predict surface reflectance.

  5. At the fundamental matrix estimation stage, motion vectors from the entire image contribute.

  6. Note that the aim in [1] was to predict human perception. Thus this measure predicts apparent or perceived shininess not physical reflectance.

  7. SIFT features have also been used for sparse specular surface reconstruction [65].

  8. This is crucial since appearance distortion critically depends on the change in feature vectors.

  9. As discussed below: nonrigid and specular motion share similar features and may be confused by a classifier; see, for example [1].

  10. Precision-recall curves are obtained by varying a specific threshold parameter, analogous to ROC curves.

  11. Given the results in experiment 5.1, we did not expect differences in performance for rotation and zoom.

  12. Interestingly, it has been shown that such objects tend to be perceived as less shiny by human observers [71].

  13. We suggest below that by complementing our motion-based features with static cues to specularity, e.g., [19], also simple 3D specular shapes may be detected.

  14. In [1] images to be classified as matte or shiny contained only a single object and a black background.

  15. Compared to the video sequences in [1].

  16. Specular highlights have been suggested as robust features for matching between 2D images and object’s 3D representation for pose estimation [73]. This suggests that highlights may also be useful for specular object detection.

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Acknowledgments

This work was supported by a Marie Curie International Reintegration Grant (239494) within the Seventh European Community Framework Programme awarded to KD. KD has also been supported by a Turkish Academy of Sciences Young Scientist Award (TUBA GEBIP), a grant by the Scientific and Technological Research Council of Turkey (TUBITAK 1001, 112K069), and the EU Marie Curie Initial Training Network PRISM (FP7-PEOPLE-2012-ITN, Grant Agreement: 316746).

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Authors and Affiliations

  1. National Magnetic Resonance Research Center (UMRAM), Bilkent Cyberpark, C-Blok Kat 2, 06800 , Ankara, Turkey

    Katja Doerschner

  2. Department of Psychology, Bilkent University, 06800 , Ankara, Turkey

    Katja Doerschner

  3. Department of Computer Engineering, Turgut Ozal University, 06010 , Ankara, Turkey

    Ozgur Yilmaz

Authors
  1. Ozgur Yilmaz
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  2. Katja Doerschner
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Correspondence to Ozgur Yilmaz.

Appendix

Appendix

1.1 Algorithm parameters

  • SIFT peak threshold = 3

  • SIFT edge threshold = 10

  • SIFT feature elimination threshold = 5

  • SIFT matching threshold = 2

  • RANSAC iteration = 2,000

  • Sampson error = 0.02

  • Convolution kernel size = 60

  • Convolution kernel, Gaussian standard deviation = 30

  • Specular field threshold = \(1.5 \times 10^{-6}\)

  • Connected component area threshold = 1,000

1.2 Optic flow experiments

For the optical flow-based detection experiment, we kept the parameters the same as in [1]. However, we used 5% of the optical flow vectors for epipolar deviation computation. The Sampson error, kernel size and standard deviation are identical to the ones used for the SIFT-based method.

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Yilmaz, O., Doerschner, K. Detection and localization of specular surfaces using image motion cues. Machine Vision and Applications 25, 1333–1349 (2014). https://doi.org/10.1007/s00138-014-0610-9

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