Confidence-based cost modulation for stereo matching

We present a novel operator to be applied at raw matching costs in the context of low level vision tasks such as stereo matching or optical \ufb02ow. It aims at im- proving matching reliability by ef\ufb01ciently modulating pixel-wise pairing costs, injecting a con\ufb01dence backed bias before the aggregation step. It works analyzing a noisy estimate of the correspondances in order to fa- vor or prune potential matches. We test the operator by developing a local, realtime stereo matching algorithm and showing that our solution can drastically clean the resulting depth map while also reducing border bleed- ing. Its good performance is also evaluated quanti- tavely by testing the algorithm against the popular Mid- dlebury benchmark where our local greedy implemen- tation is able to obtain results comparable to those of n\ua8 aive global approaches

Sztereo algoritmus légköri felhők rekonstrukciójához = Stereo Algorithm for Atmospheric Cloud Reconstruction

Ebben a cikkben bemutatunk egy légköri felhők felületét rekonstruáló eljárást, ami sztereó kamerarendszer képeit használja fel. Bemutatásra kerül az egész folyamat a javasolt kamerarendszertől a 3D ponthalmaz előállításáig, de a fő hangsúly a felhőszegmentáláson és a sztereó rekonstrukció képfeldolgozási lépésein lesz

Using genetic algorithms in computer vision : registering images to 3D surface model

This paper shows a successful application of genetic algorithms in computer vision. We aim at building photorealistic 3D models of real-world objects by adding textural information to the geometry. In this paper we focus on the 2D-3D registration problem: given a 3D geometric model of an object, and optical images of the same object, we need to find the precise alignment of the 2D images to the 3D model. We generalise the photo-consistency approach of Clarkson et al. who assume calibrated cameras, thus only the pose of the object in the world needs to be estimated. Our method extends this approach to the case of uncalibrated cameras, when both intrinsic and extrinsic camera parameters are unknown. We formulate the problem as an optimisation and use a genetic algorithm to find a solution. We use semi-synthetic data to study the effects of different parameter settings on the registration. Additionally, experimental results on real data are presented to demonstrate the efficiency of the method

Meaningful Matches in Stereovision

This paper introduces a statistical method to decide whether two blocks in a pair of of images match reliably. The method ensures that the selected block matches are unlikely to have occurred "just by chance." The new approach is based on the definition of a simple but faithful statistical "background model" for image blocks learned from the image itself. A theorem guarantees that under this model not more than a fixed number of wrong matches occurs (on average) for the whole image. This fixed number (the number of false alarms) is the only method parameter. Furthermore, the number of false alarms associated with each match measures its reliability. This "a contrario" block-matching method, however, cannot rule out false matches due to the presence of periodic objects in the images. But it is successfully complemented by a parameterless "self-similarity threshold." Experimental evidence shows that the proposed method also detects occlusions and incoherent motions due to vehicles and pedestrians in non simultaneous stereo.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence 99, Preprints (2011) 1-1

Road surface 3D reconstruction based on dense subpixel disparity map estimation

Various 3D reconstruction methods have enabled civil engineers to detect damage on a road surface. To achieve the millimetre accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used. However, none of the existing stereo matching algorithms are specially suitable for the reconstruction of the road surface. Hence in this paper, we propose a novel dense subpixel disparity estimation algorithm with high computational efficiency and robustness. This is achieved by first transforming the perspective view of the target frame into the reference view, which not only increases the accuracy of the block matching for the road surface but also improves the processing speed. The disparities are then estimated iteratively using our previously published algorithm where the search range is propagated from three estimated neighbouring disparities. Since the search range is obtained from the previous iteration, errors may occur when the propagated search range is not sufficient. Therefore, a correlation maxima verification is performed to rectify this issue, and the subpixel resolution is achieved by conducting a parabola interpolation enhancement. Furthermore, a novel disparity global refinement approach developed from the Markov Random Fields and Fast Bilateral Stereo is introduced to further improve the accuracy of the estimated disparity map, where disparities are updated iteratively by minimising the energy function that is related to their interpolated correlation polynomials. The algorithm is implemented in C language with a near real-time performance. The experimental results illustrate that the absolute error of the reconstruction varies from 0.1 mm to 3 mm.Comment: 11 pages, 16 figures, IEEE Transactions on Image Processin