The advantages of solving the stereo correspondence problem by imposing a limit on the magnitude of allowable disparity gradients are examined. It is shown how the imposition of such a limit can provide a suitable balance between the twin requirements of disambiguating power and the ability to deal with a wide range of surfaces. Next, the design of a very simple stereo algorithm called PMF is described. In conjunction with certain other constraints used in many other stereo algorithms, PMF employs a limit on allowable disparity gradients of 1, a value that coincides with that reported for human stereoscopic vision. The excellent performance of PMF is illustrated on a series of natural and artificial stereograms. Finally, the differences between the theoretical justification for the use of disparity gradients for solving the stereo correspondence problems presented in the paper and others that exist in the stereo algorithm literature are discussed.
The topological obstacles to the matching of smooth curves in stereo images are shown to occur at epipolar tangencies. Such points are good matching primitives, even when the image curves correspond to smooth surface profiles. An iterative scheme for improving camera calibration based on these results is derived and performance demonstrated on real data. We begin by quickly deriving the 8-point algorithm [1] as an introduction to some notation and terminology.Suppose left and right camera coordinate frames are related by a rotation R and a translation t
PR = RPL + t.Taking the vector product with t then the scalar product with PH givesIntroducing the matrixwe can write this aswhere we have denned E = TR. Since this equation is homogeneous in p/, and p/j it applies just as well to their projections into the two images we can write this asEvery matched pair thus gives us a row q* of the matrix Q in a linear equation These lines are the intersections of the plane through the point p in the world and the centres of the left and right cameras with the two camera focal planes (see Figure 1). These epipolar lines are formed by the intersections of the pencil of planes through the line joining the image centres with the camera focal planes. Hence such lines form a pencil passing through the point of intersection of the line of centres with the focal plane, we will call this point the epipolar base point. The condition that all possible lines m = Epi form a pencil is satisfied only if rank(i?) < 2, but this follows from the non-linear constraint on E that det(E) = det(T) det(R) -0.
The components of the Sheffield Artificial Intelligence VisionResearch Unit (AIVRU) three-dimensional (3D) vision system, which currently supports model-based object recognition and location, are described. Its potential for robotics applications is demonstrated by its guidance of a Universal Machine Intelligence robot arm in a pick-and-place task. The system comprises (1) the recovery of a sparse depth map using edgebased, passive stereo triangulation; (2) the grouping, description, and segmentation of edge segments to recover a 3D
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