Uncalibrated Stereo Correspondence by Singular Value Decomposition

image analysis, feature correspondence, stereo, singular value, decomposition This paper presents a new simple method for achieving feature correspondence across a pair of images which requires no calibration information and draws from the method proposed by Scott and Longuet Higgins [8]. Despite the well-known combinatorial complexity of the problem, this work shows that an acceptably good solution can be obtained directly by singular value decomposition of an appropriate image-based correspondence strength matrix. The paper includes several experiments and discusses the method and draws comparisons with a related relaxation-based method by [14]. Given its tremendous performance / complexity figure, the method is particularly suitable for research purposes where an off the shelf but reliable feature correspondence is needed. For this reason, a succinct MATLAB implementation of the method is included and a C version will be soon available on the WEB.