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Related Experiment Video

Updated: May 20, 2026

High-speed Particle Image Velocimetry Near Surfaces

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

Fast cost-volume filtering for visual correspondence and beyond.

Asmaa Hosni¹, Christoph Rhemann, Michael Bleyer

¹Institute of Software Technology and Interactive Systems, Vienna University of Technology, Interactive Media Systems Group, Favoritenstrasse 9-11/188/2, A-1040 Vienna, Austria. asmaa@ims.tuwien.ac.at

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 1, 2012

Summary

This study introduces a fast, edge-preserving filtering framework for computer vision labeling tasks. It achieves state-of-the-art results in real-time disparity map generation and optical flow estimation.

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Published on: May 20, 2014

Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Many computer vision tasks involve labeling, requiring spatially smooth outputs aligned with image edges.
Existing methods often struggle with efficiency and fine structure preservation.

Purpose of the Study:

To propose a generic and efficient framework for image labeling tasks.
To demonstrate state-of-the-art performance in disparity map generation and optical flow estimation using this framework.

Main Methods:

A three-step framework: cost volume construction, fast edge-preserving filtering, and Winner-Takes-All label selection.
Utilizing a fast edge-preserving filter to smooth label costs.

Main Results:

Achieved real-time disparity map generation exceeding other fast local methods on the Middlebury benchmark.
Generated high-quality optical flow fields with fine structures and large displacements.
Demonstrated robustness with consistent parameter settings across applications.

Conclusions:

The proposed framework offers a simple yet powerful approach for various computer vision labeling problems.
The method achieves state-of-the-art results efficiently, outperforming existing techniques.
The framework's versatility suggests potential for application in other computer vision domains.