Quality and performance analysis of automatic relative orientation

Beate Haala; Michael Hahn; Dieter Schmidt

doi:10.1117/12.155799

24 September 1993 Quality and performance analysis of automatic relative orientation

Beate Haala, Michael Hahn, Dieter Schmidt

Proceedings Volume 1944, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision; (1993) https://doi.org/10.1117/12.155799
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

With this paper we continue work done in the area of automatic orientation of images. The term relative orientation usually is understood to be the task of estimating five rotational and translational parameters from a given set of homologous image points. With the attribute automatic we want to indicate that the parameter estimation is not the main task. The main task is that the stereo measurement process (i.e. establishing point correspondences) has to be done automatically. The presented procedure for automatic relative orientation of two images consists of modules for the computation of image pyramids, feature extraction, correspondence and the determination of orientation parameters. The procedure works in a hierarchical fashion, in which not more than some general a priori knowledge, e.g. that the images are taken at a standard image flight, is used. In this paper we describe the parallelization of the total procedure and the implementation on a SIMD computer system. A comparison to the sequential algorithms is given, discussing aspects mainly due to computational performance. Results concerning the quality are derived from experiments with two pairs of aerial images. Finally we show that with some modifications of the orientation procedure we arrive at a shape from stereo approach.

Citation Download Citation

Beate Haala, Michael Hahn, and Dieter Schmidt "Quality and performance analysis of automatic relative orientation", Proc. SPIE 1944, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, (24 September 1993); https://doi.org/10.1117/12.155799

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