Image segmentation by nonlinear filtering of optical Hough transform

Fernández, Ariel; Flores, Jorge L.; Alonso, Julia R.; Ferrari, José A.

doi:10.1364/ao.55.003632

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…[LHT]) in edge binary images [4,5] and it was later on extended to other analytical shapes (Circular Hough Transform [6] [CHT], Elliptical [7] Hough Transform [EHT]). Besides, in order to detect patterns with no analytical representation, the transformation was extended to what is known as Generalized Hough Transform (GHT) [8,9] which has proven to be useful in many applications either under its computational implementation [10][11][12][13][14] or its optical counterpart [15][16][17][18], with the possibility in the latter of achieving real-time [19], fully-invariant [20] pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Generalized Hough transform for 3D object recognition and visualization in integral imaging

2023

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Object recognition is an automated image processing application of great interest in areas ranging from defect inspection to robot vision. In this regard, the generalized Hough transform is a well-established technique for the recognition of geometrical features even when they are partially occluded or corrupted by noise. To extend the original algorithm—aimed at detecting 2D geometrical features out of single images—we propose the robust integral generalized Hough transform, which corresponds to transformation under the generalized Hough transform of an elemental image array obtained from a 3D scene under integral imaging capture. The proposed algorithm constitutes a robust approach to pattern recognition in 3D scenes that takes into account information obtained not only from the individual processing of each image of the array but also from the spatial restrictions arising from perspective shifts between images. The problem of global detection of a 3D object of given size, position, and orientation is then exchanged under the robust integral generalized Hough transform for a more easily solved maximum detection in an accumulation (Hough) space dual to the elemental image array of the scene. Detected objects can then be visualized following refocusing schemes of integral imaging. Validation experiments for the detection and visualization of partially occluded 3D objects are presented. To the best of our knowledge, this is the first implementation of the generalized Hough transform for 3D object detection in integral imaging.

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Section: Introductionmentioning

confidence: 99%

Generalized Hough transform for 3D object recognition and visualization in integral imaging

2023

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Section: Introductionmentioning

confidence: 99%

Generalized Hough Transform for 3D Object Recognition and Visualization in Integral Imaging

Fernández¹,

Umpiérrez²,

Alonso³

2023

Preprint

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Object recognition is an automated image processing application of great interest in areas ranging from defect inspection to robot vision. In this regard, the Generalized Hough Transform is a well-established technique for the recognition of geometrical features even when they are partially occluded or corrupted by noise. In order to extend the original algorithm -aimed at detecting 2D geometrical features out of single images- we propose the Robust Integral Generalized Hough Transform which corresponds to the transformation under Generalized Hough Transform of an array of Elemental Images taken from a 3D scene in Integral Imaging capture. The proposed algorithm constitutes a robust approach to pattern recognition in 3D scenes that takes into account information obtained not only from the individual processing of each image of the array but also from the spatial restrictions arising from perspective shifts between images. The problem of global detection of a 3D object of given size, position and orientation is then exchanged under Robust Integral Generalized Hough Transform for a more easily solved maxima detection in an accumulation (Hough) space dual to the array of Elemental Images of the scene. Detected objects can then be visualized following refocusing schemes of Integral Imaging. Validation experiments for the detection and visualization of partially occluded three-dimensional objects are presented. To the best of our knowledge this is the first implementation of Generalized Hough Transform for 3D object detection in Integral Imaging.

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Automated coronary artery tree segmentation in coronary CTA using a multiobjective clustering and toroidal model-guided tracking method

Kai

Bao

et al. 2021

Computer Methods and Programs in Biomedicine

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Image segmentation by nonlinear filtering of optical Hough transform

Cited by 10 publications

References 28 publications

Generalized Hough transform for 3D object recognition and visualization in integral imaging

Generalized Hough transform for 3D object recognition and visualization in integral imaging

Generalized Hough Transform for 3D Object Recognition and Visualization in Integral Imaging

Automated coronary artery tree segmentation in coronary CTA using a multiobjective clustering and toroidal model-guided tracking method

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