Heuristic method based on voting for extrinsic orientation through image epipolarization

[EN] Traditionally, the stereo-pair rectification, also known as epipolarization problem, (i.e., the projection of both images onto a common image plane) is solved once both intrinsic (interior) and extrinsic (exterior) orientation parameters are known. A heuristic method is proposed to solve both t...

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Detalles Bibliográficos
Autores: Martín, Santiago, Uzkeda, Hodei, Lerma, José Luis|||0000-0001-9443-9214
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/108472
Acceso en línea:https://riunet.upv.es/handle/10251/108472
Access Level:acceso abierto
Palabra clave:Epipolarization
Epipolar geometry
Extrinsic orientation
Image rectification
Relative orientation
INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA
Descripción
Sumario:[EN] Traditionally, the stereo-pair rectification, also known as epipolarization problem, (i.e., the projection of both images onto a common image plane) is solved once both intrinsic (interior) and extrinsic (exterior) orientation parameters are known. A heuristic method is proposed to solve both the extrinsic orientation problem and the epipolarization problem in just one single step. The algorithm uses the main property of a coplanar stereopair as fitness criteria: null vertical parallax between corresponding points to achieve the best stereopair. Using an iterative approach, each pair of corresponding points will vote for a rotation axis that may reduce vertical parallax. The votes will be weighted, the rotation applied, and an iteration will be carried out, until the vertical parallax residual error is below a threshold. The algorithm performance and accuracy are checked using both simulated and real case examples. In addition, its results are compared with those obtained using a traditional nonlinear least-squares adjustment based on the coplanarity condition. The heuristic methodology is robust, fast, and yields optimal results.