David Hafner

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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(More)
Most researchers agree that invariances are desirable in computer vision systems. However, one always has to keep in mind that this is at the expense of accuracy: By construction, all invariances inevitably discard information. The concept of morphological invariance is a good example for this trade-off and will be in the focus of this paper. Our goal is to(More)
Focus fusion methods combine a set of images focused at different depths into a single image where all parts are in focus. The quality of the fusion result strongly depends on a decision map that determines the in-focus areas. Most approaches in the literature achieve this by local decisions without explicitly enforcing smoothness of the depth map. The goal(More)
Invariances are one of the key concepts to render computer vision algorithms robust against severe illumination changes. However, there is no free lunch: With any invariance comes an unavoidable loss of information. The goal of our paper is to introduce two novel descriptors which minimise this loss: the complete rank transform and the complete census(More)
In recent years, the popularity of the census transform has grown rapidly. It provides features that are invariant under monotonically increasing intensity transformations. Therefore, it is exploited as a key ingredient of various computer vision problems, in particular for illumination-robust optic flow models. However, despite being extensively applied,(More)
In this paper we consider the problem of estimating depth maps from multiple views within a variational framework. Previous work has demonstrated that multiple views improve the depth reconstruction, and that higher order regularisers model a good prior for typical real-world 3D scenes. We build on these findings and stress an important aspect that has not(More)