2013
DOI: 10.1007/978-3-642-38267-3_18
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Why Is the Census Transform Good for Robust Optic Flow Computation?

Abstract: Abstract. The census transform is becoming increasingly popular in the context of optic flow computation in image sequences. Since it is invariant under monotonically increasing grey value transformations, it forms the basis of an illumination-robust constancy assumption. However, its underlying mathematical concepts have not been studied so far. The goal of our paper is to provide this missing theoretical foundation. We study the continuous limit of the inherently discrete census transform and embed it into a… Show more

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Cited by 59 publications
(30 citation statements)
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References 22 publications
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“…Nevertheless, this alternative census-inspired perspective offers an unexpected insight: As pointed out in [10], each binary digit of a census signature can be regarded as the sign of the corresponding directional derivative (in a finite difference sense). Thus, from this point of view, one can conclude that the complete rank transform inherently contains rich local differential information.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Nevertheless, this alternative census-inspired perspective offers an unexpected insight: As pointed out in [10], each binary digit of a census signature can be regarded as the sign of the corresponding directional derivative (in a finite difference sense). Thus, from this point of view, one can conclude that the complete rank transform inherently contains rich local differential information.…”
Section: Discussionmentioning
confidence: 99%
“…There are several recent publications that incorporate the census transform in variational optical flow or stereo methods: Müller et al propose a census-based data term for optical flow [15], and Ranftl et al [18] as well as Mei et al [12] present censusbased stereo methods. The theoretical study of Hafner et al [10] explains the reasons why census-based data terms for variational optical flow are successful.…”
Section: Related Workmentioning
confidence: 99%
“…Census transform [283] recently regained interest and was promoted in [226] for optical flow estimation [108,118,179,182,183,205,252]. The Census signature is a bit string reflecting relative value of pixels of a patch with respect to the center pixel.…”
Section: Patch-based Similarity Measuresmentioning
confidence: 99%
“…Integrating the Census transform in variational optical flow is not trivial since it cannot be easily linearized. Solutions to remedy this problem are convex approximation [252], reformulation as a generalization of the gradient constancy conservation [108] or linearization of the data term [182,205] as previously mentioned for NCC [272].…”
Section: Patch-based Similarity Measuresmentioning
confidence: 99%
“…Considering the illumination variation on the KITTI dataset, we adopt Census Transform [37], which is proved to be powerful for robust optical flow computation [6]. We show the performance of different methods when integrated into cross-scale cost aggregation in Table 2.…”
Section: Kitti Datasetmentioning
confidence: 99%