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Machine Vision and Applications

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01.07.2014 | Original Paper

Detection and localization of specular surfaces using image motion cues

verfasst von: Ozgur Yilmaz, Katja Doerschner

Erschienen in: Machine Vision and Applications | Ausgabe 5/2014

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

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

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

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.

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

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

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

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

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

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

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

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

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.

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

Compared to the video sequences in [1].

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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Titel: Detection and localization of specular surfaces using image motion cues
verfasst von: Ozgur Yilmaz
Katja Doerschner
Publikationsdatum: 01.07.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 5/2014
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-014-0610-9

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