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From FNS to HEIV: a link between two vision parameter estimation methods.

Wojciech Chojnacki·Michael J Brooks·Anton van den Hengel·Darren Gawley
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Abstract

Problems requiring accurate determination of parameters from image-based quantities arise often in computer vision. Two recent, independently developed frameworks for estimating such parameters are the FNS and HEIV schemes. Here, it is shown that FNS and a core version of HEIV are essentially equivalent, solving a common underlying equation via different means. The analysis is driven by the search for a nondegenerate form of a certain generalized eigenvalue problem and effectively leads to a new derivation of the relevant case of the HEIV algorithm. This work may be seen as an extension of previous efforts to rationalize and interrelate a spectrum of estimators, including the renormalization method of Kanatani and the normalized eight-point method of Hartley.

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AlgorithmsArtificial IntelligenceComputer SimulationImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalModels, StatisticalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedPhotogrammetryReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedSubtraction TechniqueComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Wojciech Chojnacki, Michael J Brooks, Anton van den Hengel, Darren Gawley. 2004. From FNS to HEIV: a link between two vision parameter estimation methods.. https://doi.org/10.1109/tpami.2004.1262197

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