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Likelihood functions for Window-based stereo vision

Vonk, R.V. (2012) Likelihood functions for Window-based stereo vision.

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Abstract:The biological process of stereopsis — the brain is able to perceive depth from the information of two eyes — inspired researchers to bring this ability to computers and robotics. As this proofs to be a complex task it let to the introduction of a whole new field: Computer Vision. Two or more cameras at different positions take pictures of the same scene. A computer compares these images to determine the shift of local features. The shift (disparity) of an object in the images is used to calculate the distance. Most algorithms use a similarity measure to compute the disparity of local features between images. The quality of the similarity measure determines the potential of the algorithm. This research concentrates on the earlier work of Damjanovi´c, Van der Heijden, and Spreeuwers, who took a different approach. They introduced a new likelihood function for window-based stereo matching, based on a sound probabilistic model to cope with unknown textures, uncertain gain factors, uncertain offsets, and correlated noise. The derivation of the likelihood function is the first part. The likelihood function is obtained by marginalization of the texture and the gains. In the paper this research is based on, a solution is obtained by a few approximations. However, we show that one approximation is not allowed due to an error in the solution for the first integration step. Through several attempts is tried to bring a (partial) solution within reach. Also, it is shown that a generalization for n-view vision does not complicate the final integration step further. The main goal of the proposed likelihood function is to outperform the normalized cross correlation (NCC) and the sum of squared differences (SSD). A simplification of the likelihood function (in which the gains are left out) results in a metric with the Mahalanobis distance at its basis compared to the Euclidean distance for the SSD. Information within the windows (e.g. distortions, occlusions, and importance of pixels) is exploited to train the Mahalanobis distance with an optimal covariance matrix. Experiments show that the simplified likelihood function decreases the number of errors for difficult regions in the scene. In recent research, the focus lies primarily on post-processing such as belief propagation. However, one of the main findings of this research is that a good similarity measure such as the Mahalanobis distance decreases the number of errors in stereo correspondence for difficult regions. The correct matches near occlusions and discontinuities of the disparity map provide important information that can be directly used within a probabilistic framework (HMM/BP). Although an analytic solution for the complete likelihood function remains unsolved, progress has been made. Alternative methods are suggested that could lead to a proper analytic solution for the proposed probabilistic model.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology
Programme:Electrical Engineering MSc (60353)
Link to this item:http://purl.utwente.nl/essays/69544
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