Automated stereophotogrammetry

Brookshire, Greg; Nadler, Morton; Lee, Choon

doi:10.1016/0734-189x(90)90131-e

Cited by 1 publication

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“…It has been, however, necessary to specify the corresponding points in the images manually. When the matching of right-eye and left-eye images is computed, it can be immediately used for stereo photogrammetry (e.g., [16], [15], [1], [5], [17], [6], [32], [28], [31]). Even if we reduce the number of candidate pairs of points taking advantage of epipolar lines, the complexity is high.…”

Section: Introductionmentioning

confidence: 99%

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Unconstrained automatic image matching using multiresolutional critical-point filters

Shinagawa

Kunii

1998

IEEE Trans. Pattern Anal. Machine Intell.

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This paper proposes a novel method for matching images. The results can be used for a variety of applications: fully automatic morphing, object recognition, stereo photogrammetry, and volume rendering. Optimal mappings between the given images are computed automatically using multiresolutional nonlinear filters that extract the critical points of the images of each resolution. Parameters are set completely automatically by dynamical computation analogous to human visual systems. No prior knowledge about the objects is necessary. The matching results can be used to generate intermediate views when given two different views of objects. When used for morphing, our method automatically transforms the given images. There is no need for manually specifying the correspondence between the two images. When used for volume rendering, our method reconstructs the intermediate images between cross-sections accurately, even when the distance between them is long and the cross-sections vary widely in shape. A large number of experiments has been carried out to show the usefulness and capability of our method.

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Section: Introductionmentioning

confidence: 99%

“…The resulting matched pairs of points are sparse. To fill the gaps between the matched points, the disparity values are interpolated [13], [26], [6]. In general, all edge detectors suffer from the problem of judging whether a change in the pixel intensity in a local window they use really means the existence of an edge [8].…”

Section: Introductionmentioning

confidence: 99%

Unconstrained automatic image matching using multiresolutional critical-point filters

Shinagawa

Kunii

1998

IEEE Trans. Pattern Anal. Machine Intell.

View full text Add to dashboard Cite

show abstract

Automated stereophotogrammetry

Cited by 1 publication

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Unconstrained automatic image matching using multiresolutional critical-point filters

Unconstrained automatic image matching using multiresolutional critical-point filters

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