The Gaussian scale-space paradigm and the multiscale local jet
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Moving Object Segmentation Using Optical Flow and Depth Information
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Integrating disparity images by incorporating disparity rate
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Ground truth evaluation of stereo algorithms for real world applications
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Illumination invariant cost functions in semi-global matching
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Spatio-temporal stereo disparity integration
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
6D-vision: fusion of stereo and motion for robust environment perception
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Stereo analysis by hybrid recursive matching for real-time immersive video conferencing
IEEE Transactions on Circuits and Systems for Video Technology
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Vision-based applications usually have as input a continuous stream of data. Therefore, it is possible to use the information generated in previous frames to improve the analysis of the current one. In the context of video-based driver-assistance systems, objects present in a scene typically perform a smooth motion through the image sequence. By considering a motion model for the ego-vehicle, it is possible to take advantage of previously processed data when analysing the current frame. This paper presents a Kalman filter-based approach that focuses on the reduction of the uncertainty in depth estimation (via stereo-vision algorithms) by using information from the temporal and spatial domains. For each pixel in the current disparity map, we refine the estimated value using the stereo data from a neighbourhood of pixels in previous and current frames. We aim at an improvement of existing methods that use data from the temporal domain by adding extra information from the spatial domain. To show the effectiveness of the proposed method, we analyse the performance on long synthetic sequences using different stereo matching algorithms, and compare the results obtained by the previous and the suggested approach.