A target recognition technique employing geometric invariants

Song, Bong Seop; Yun, Il Dong; Lee, Sang Uk

doi:10.1016/s0031-3203(99)00085-0

Cited by 10 publications

(2 citation statements)

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“…For polygonal objects of fixed shape but variable size, matching is normally attempted by assembling collections of primitive features such as edges / lines as in [2,3], or corners / geometric invariants as in [1,9,10]. As opposed to other methods in which polygon are detected by making a single attempt to match models to images, the efficient particle filtering approach in [7] combines many attempts, based on sequential Monte Carlo sampling, to track a single polygon boundary.…”

Section: Introductionmentioning

confidence: 99%

Detecting polygons of variable dimension in overhead images with particle filters

Manay

Paglieroni

2008

2008 15th IEEE International Conference on Image Processing

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We extend the particle filtering approach to detecting polygonal structures of fixed shape and variable size in overhead images to the more practically useful case of unconstrained side lengths. Our approach deals with multiple candidate sides by validating and clustering particles based on evidence that corners of proper acuteness and orientation (as constrained by the polygon model) might exist. A queue-regulated tracking algorithm that handles multiple candidates across multiple sides is discussed. Compelling detection results in overhead images that involve entire families of polygonal structure are provided.

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Section: Introductionmentioning

confidence: 99%

Detecting polygons of variable dimension in overhead images with particle filters

Manay

Paglieroni

2008

2008 15th IEEE International Conference on Image Processing

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“…B.S. Song et al present in [7] a target recognition method based in CR that selects stable points for complex scenes. More general algebraic invariants can be derived from configurations of conics [8], points [9] and lines [10].…”

Section: Introductionmentioning

confidence: 99%

Planar Object Detection under Scaled Orthographic Projection

Cózar

Guil

Zapata

2000

Discrete Geometry for Computer Imagery

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In this work a new method to detect objects under scaled orthographic projections is shown. It also calculates the parameters of the transformations the object has suffered. The method is based on the use of the Generalized Hough Transform (GHT) that compares a template with a projected image. The computational requirements of the algorithm are reduced by restricting the transformation to the template edge points and using invariant information during the comparison process. This information is obtained from a precomputed table of the template that is directly transformed and compared with the image table. Moreover, a multiresolution design of the algorithm speeds-up the parameters calculation.

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