Linear Structures Identification in Images Using Scale Space Radon Transform and Multiscale Image Hessian

Goumeidane, Aicha Baya; Nacereddine, Nafaâ; Ziou, Djemel

doi:10.1007/978-3-030-89817-5_25

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…However, in real world applications, this parameter is often unknown. That is why, we propose in the following experimental part to apply the Hessian of the image in order to derive the components number M from the computed Hessians orientations as done in [16]. In addition, this Hessian-based method provides the initial values of the orientation angle θ.…”

Section: A Dynamic Background Subtractionmentioning

confidence: 99%

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Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters

Nacereddine

Goumeidane

Ziou

2019

Computers in Industry

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Section: A Dynamic Background Subtractionmentioning

confidence: 99%

“…To find how much structures are related to each orientation, some orientation maps are used. For more details, please refer to [16].…”

Section: A Dynamic Background Subtractionmentioning

confidence: 99%

Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters

Nacereddine

Goumeidane

Ziou

2019

Computers in Industry

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“…They have shown that this transform can be used to detect elegantly and accurately thick lines and ellipses through an embedded kernel tuned by a scale space parameter. When the scale space parameter is an appropriate one, the maximum of SSRT represents the centerlines of the linear/elliptical structures presented in the image [15,16]. In this paper we propose to investigate the ability of the SSRT to provide, through its maximum in the SSRT space, the main axis of inertia of the processed object.…”

Section: Introductionmentioning

confidence: 99%

Scale space radon transform-based inertia axis and object central symmetry estimation

Goumeidane¹,

Ziou²,

Nacereddine³

2023

Preprint

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Inertia Axes are involved in many techniques for image content measurement when involving information obtained from lines, angles, centroids... etc. We investigate, here, the estimation of the main axis of inertia of an object in the image. We identify the coincidence conditions of the Scale Space Radon Transform (SSRT) maximum and the inertia main axis. We show, that by choosing the appropriate scale parameter, it is possible to match the SSRT maximum and the main axis of inertia location and orientation of the embedded object in the image. Furthermore, an example of use case is presented where binary objects central symmetry computation is derived by means of SSRT projections and the axis of inertia orientation. To this end, some SSRT characteristics have been highlighted and exploited. The experimentations show the SSRT-based main axis of inertia computation effectiveness. Concerning the central symmetry, results are very satisfying as experimentations carried out on randomly created images dataset and existing datasets have permitted to divide successfully these images bases into centrally symmetric and non-centrally symmetric objects.

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“…This fact produces peaks in the SSRT space that do not correspond to lines. To overcome this shortcoming, authors in [22] have proposed to construct the SSRT space only around pre-computed SSRT parameters yielded with multiscales Hessian of an image, with the aim of discarding orientations and positions bringing useless information. However, the pixels values integration is done inside a band for the SSRT in all directions in the entire image, which increases the interference of the irrelevant parts of the image when constructing the SSRT space, leading, as a result, to the introduction of a bias in the detected linear structures.…”

Section: Introductionmentioning

confidence: 99%

Scale Space Radon Transform for Non Overlapping Thick Ellipses Detection

Goumeidane

Ziou

Nacereddine

2022

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Using integral transforms to the end of lines detection in images with complex background, makes the detection a hard task needing additional processing to manage the detection. As an integral transform, the Scale Space Radon Transform (SSRT) suffers from such drawbacks, even with its great abilities for thick lines detection. In this work, we propose a method to address this issue for automatic detection of thick linear structures in gray scale and binary images using the SSRT, whatever the image background content. This method involves the calculated Hessian orientations of the investigated image while computing its SSRT, in such a way that linear structures are emphasized in the SSRT space. As a consequence, the subsequent maxima detection in the SSRT space is done on a modified transform space freed from unwanted parts and, consequently, from irrelevant peaks that usually drown the peaks representing lines. Besides, highlighting the linear structure in the SSRT space permitting, thus, to efficiently detect lines of different thickness in synthetic and real images, the experiments show also the method robustness against noise and complex background.

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Linear Structures Identification in Images Using Scale Space Radon Transform and Multiscale Image Hessian

Cited by 5 publications

References 11 publications

Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters

Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters

Scale space radon transform-based inertia axis and object central symmetry estimation

Scale Space Radon Transform for Non Overlapping Thick Ellipses Detection

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