High Accuracy Edge Matching with an Extension of the MPGC-Matching Algorithm

Gruen, Armin; Stallmann, Dirk

doi:10.1007/978-3-642-77958-9_30

Cited by 8 publications

(7 citation statements)

References 2 publications

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“…Compared to the conventional feature‐based matching techniques, this method does not require the extraction of image edges, but matching is done directly by using the original grey value edges. For algorithmic details, see Gruen and Stallmann (1991). An efficient automatic measurement procedure can be realised via implementation of a tracking technique, which tracks the edges either in object or in image space.…”

Section: New Approaches (1980s)mentioning

confidence: 99%

Development and Status of Image Matching in Photogrammetry

Gruen

2012

The Photogrammetric Record

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221

131

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Image and template matching is probably the most important function in digital photogrammetry and also in automated modelling and mapping. Many approaches for matching have evolved over the years, but the problem is still unsolved in general terms. This paper describes the development of image matching techniques in photogrammetry over the past 50 years, addresses the results of some empirical accuracy studies and also provides a critical account of some of the problems that remain. Although automated approaches have quite a number of advantages, the quality of the results is still not satisfactory and, in some cases, far from acceptable. Even with the most advanced techniques, it is not yet possible to achieve the quality of results that a human operator can produce. There is an urgent need for further improvements and innovations, be it through more powerful multi‐sensor approaches, thereby enlarging the information spectrum, and/or through advancements in image understanding algorithms, thus coming closer to human capabilities of reading and understanding image content.

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Section: New Approaches (1980s)mentioning

confidence: 99%

Development and Status of Image Matching in Photogrammetry

Gruen

2012

The Photogrammetric Record

Self Cite

221

131

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“…It has been applied to many different types of measurement and feature extraction problems due to its high level of flexibility and its powerful mathematical model: Adaptive Least Squares Image Matching (Gruen, 1984(Gruen, , 1985a, Geometrically Constrained Multiphoto Matching (Gruen and Baltsavias, 1988), Image Edge Matching (Gruen and Stallmann, 1991), Multiple Patch Matching with 2D images (Gruen, 1985b), Multiple Cuboid (voxel) Matching with 3D images (Maas, 1994;Maas and Gruen, 1995), Globally Enforced Least Squares Template Matching (Gruen and Agouris, 1994), Least Squares B-spline (LSB) Snakes (Gruen and Li, 1996). For a detailed survey the authors refer to Gruen (1996).…”

Section: Our Proposed Methodsmentioning

confidence: 99%

Least squares 3D surface and curve matching

Gruen

Akça

2005

ISPRS Journal of Photogrammetry and Remote Sensing

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479

276

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The automatic co-registration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. Our proposed method estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface, using the Generalized Gauss-Markoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surface patches. It fully considers 3D geometry. Besides the mathematical model and execution aspects we address the further extensions of the basic model. We also show how this method can be used for curve matching in 3D space and matching of curves to surfaces. Some practical examples based on the registration of close-range laser scanner and photogrammetric point clouds are presented for the demonstration of the method. This surface matching technique is a generalization of the least squares image matching concept and offers high flexibility for any kind of 3D surface correspondence problem, as well as statistical tools for the analysis of the quality of final matching results. D

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“…They were able to reconstruct the diffusion process in fluids through the tracking of volume elements with LSM. Gruen and Stallmann (1991) also presented a LSM approach for the localization of linear features rather than point features. They use an artificial template of an idealized edge which has to be translated and rotated during the matching process.…”

Section: Least Squares Template Matching (Lsm)mentioning

confidence: 98%

Light optical deformation measurements in microbars with nanometer resolution

Mazza

Danuser

Dual

1996

Microsystem Technologies

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In a microsample tensile test the deformation of the testing region is observed with a light microscope. Using a new vision algorithm, elongations are determined with nanometer resolution. The image analysis system operates with adaptive least squares correlation and contains a diagnostic tool which provides information about the computational precision and determinability of the parameters to be estimated. Various tests are performed to verify the reliability and sensitivity of the presented method and to show its potential in the field of micromechanics.

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High Accuracy Edge Matching with an Extension of the MPGC-Matching Algorithm

Cited by 8 publications

References 2 publications

Development and Status of Image Matching in Photogrammetry

Development and Status of Image Matching in Photogrammetry

Least squares 3D surface and curve matching

Light optical deformation measurements in microbars with nanometer resolution

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