Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Computer vision and applications: a guide for students and practitioners
Computer vision and applications: a guide for students and practitioners
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Prediction Error as a Quality Metric for Motion and Stereo
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The trilateral filter for high contrast images and meshes
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Gaussian Noise Removal of Image on the Local Feature
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 03
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study on Stereo and Motion Data Accuracy for a Moving Platform
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
A Third Eye for Performance Evaluation in Stereo Sequence Analysis
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Illumination-robust variational optical flow with photometric invariants
Proceedings of the 29th DAGM conference on Pattern recognition
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereoscopic Scene Flow Computation for 3D Motion Understanding
International Journal of Computer Vision
Real-World stereo-analysis evaluation
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Kalman-filter based spatio-temporal disparity integration
Pattern Recognition Letters
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The intensity (grey value) consistency of image pixels in a sequence or stereo camera setup is of central importance to numerous computer vision applications. Most stereo matching and optical flow algorithms minimise an energy function composed of a data term and a regularity or smoothing term. To date, well performing methods rely on the intensity consistency of the image pixel values to model the data term. Such a simple model fails if the illumination is (even slightly) different between the input images. Amongst other situations, this may happen due to background illumination change over the sequence, different reflectivity of a surface, vignetting, or shading effects. In this paper, we investigate the removal of illumination artifacts and show that generalised residual images substantially improve the accuracy of correspondence algorithms. In particular, we motivate the concept of residual images and show two evaluation approaches using either ground truth correspondence fields (for stereo matching and optical flow algorithms) or errors based on a predicted view (for stereo matching algorithms).