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Feature-Based Object Recognition and Localization in 3D-Space, Using a Single Video Image

We introduce a robust algorithm to recognize objects in 3D space from one 2D video image and to localize the objects in all six degrees of freedom. Point-like attached features are used in the input image and additional edge information provides grouping. In an initial phase, a 3D model of all objects to be recognized is stored in the computer represented by their features. Combining the location of the detected features in the 2D input scene with the features of the 3D computer model, each single feature gives a subspace as possible solutions of the location parameters to be determined. The points of intersection of the corresponding trajectories are accumulated as possible solutions in a Hough table. The location of the highest peak in the space of hypothetical solutions delivers the desired rotation and translation parameters, even for partially hidden objects. The fully analytical algorithm is adapted to weak perspective (orthographic and scale) as well as to perspective projection. An application to range images leads to the automated feature modeling of the required 3D reference objects.