Model Selection Techniques and Merging Rules for Range Data Segmentation Algorithms

Bubna, Kishore; Stewart, Charles

doi:10.1006/cviu.2000.0871

Cited by 19 publications

(23 citation statements)

References 46 publications

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“…Model selection techniques have been studied extensively in the literature [9], [10], [29], [50]. In our earlier work [48], we used a Bayesian technique derived in [9] that depends on computing the determinant of the parameter-estimate covariance matrix,AE pq .…”

Section: Model Hierarchy and Model Selectionmentioning

confidence: 99%

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Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

Yang

Stewart

Sofka

et al. 2007

IEEE Trans. Pattern Anal. Machine Intell.

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250

174

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Abstract-Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8 percent of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.

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Section: Model Hierarchy and Model Selectionmentioning

confidence: 99%

“…In our earlier work [48], we used a Bayesian technique derived in [9] that depends on computing the determinant of the parameter-estimate covariance matrix,AE pq . For homographies, this is problematic becauseAE pq is not full rank.…”

Section: Model Hierarchy and Model Selectionmentioning

confidence: 99%

Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

Yang

Stewart

Sofka

et al. 2007

IEEE Trans. Pattern Anal. Machine Intell.

Self Cite

250

174

View full text Add to dashboard Cite

show abstract

“…1 and 2 and Sec. 4.3) typical values of κ 2 (P ) are in the range 1 to 2 for M 1 , in the range 30 to 40 for M 2 and M 3 , in the range 40 to 50 for M 4 , and in the range 3000 to 4000 for M 5 . Scaling brings all these condition numbers below 5, i.e.…”

Section: Scaling and The Aperture Problemmentioning

confidence: 90%

Controlling model complexity in flow estimation

Durić

Cherkassky

2003

Proceedings Ninth IEEE International Conference on Computer Vision

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“…Rissanen's MDL is also used in computer vision applications. Its use may be justified if the problem has the standard form of linear/nonlinear regression [2], [23]. Often, however, the solution having a shorter description length was chosen with a rather arbitrary definition of the complexity [7], [21], [24].…”

Section: Observationsmentioning

confidence: 99%

Uncertainty modeling and model selection for geometric inference

Kanatani

2004

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-We first investigate the meaning of "statistical methods" for geometric inference based on image feature points. Tracing back the origin of feature uncertainty to image processing operations, we discuss the implications of asymptotic analysis in reference to "geometric fitting" and "geometric model selection" and point out that a correspondence exists between the standard statistical analysis and the geometric inference problem. Then, we derive the "geometric AIC" and the "geometric MDL" as counterparts of Akaike's AIC and Rissanen's MDL. We show by experiments that the two criteria have contrasting characteristics in detecting degeneracy.

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Model Selection Techniques and Merging Rules for Range Data Segmentation Algorithms

Cited by 19 publications

References 46 publications

Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

Controlling model complexity in flow estimation

Uncertainty modeling and model selection for geometric inference

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