In this paper, we present a local adaptive filter for fast edge-preserving smoothing, a so-called cross-based filter.
The filter is mainly built on upright crosses and captures the local image structures adaptively. The cross-based
filter has some resemblance with the classic bilateral filter, when binarizing the support weight and imposing
a spatial connectivity constraint. For edge-preserving smoothing, our cross-based filter is capable of reaching
similar performance as bilateral filter, while being dozens of times faster. The proposed filter can be applied in
near-constant time, using the integral images technique. In addition, the cross-based filter is highly parallel and
suitable for parallel computing platforms, e.g. GPUs. The strength of the proposed filter is illustrated in several
applications, i.e. denoising and image abstraction.
We present a local area-based, discontinuity-preserving stereo matching algorithm that achieves high quality
results near depth discontinuities as well as in homogeneous regions. To address the well-known challenge of defining appropriate support windows for local stereo methods, we use the anisotropic Local Polynomial Approximation (LPA) - Intersection of Confidence Intervals (ICI) technique. It can adaptively select a nearoptimal
anisotropic local neighborhood for each pixel in the image. Leveraging this robust pixel-wise shape-adaptive
support window, the proposed stereo method performs a novel matching cost aggregation step and an
effective disparity refinement scheme entirely within a local high-confidence voting framework. Evaluation using
the benchmark Middlebury stereo database shows that our method outperforms other local stereo methods, and
it is even better than some algorithms using advanced but computationally complicated global optimization
techniques.
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