Whitening central projection descriptor for affine‐invariant shape description

Lan, Rushi; Yang, Jun; Jiang, Yong; Fyfe, Colin; Song, Zhan

doi:10.1049/iet-ipr.2012.0094

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…Instead of relying on a feature descriptor or correlation to match the MSERs, a method similar to that of [9]- [11] is utilized. This method has been applied to affine invariant shape matching and affine invariant region matching via correlation [2], however not in the context of approximating the orientation between two scenes.…”

Section: A Tentative Matching Of Affine Invariant Regionsmentioning

confidence: 99%

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Affine invariant matching based on orientation estimation

Brese

Zou

2013

2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS)

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In recent years several algorithms have been developed that allow feature matching methods to operate on images with large baseline variations such as Affine-Scale Invariant Feature Transform (ASIFT) and its variants. These algorithms solve the base line problem through simulating various potential transforms between image pairs. These simulated views may be easier to match using traditional feature matching algorithms than the original wide baseline views. This paper presents a novel approach to approximating the orientation between wide baseline views. The proposed method tentatively matches affine invariant regions, normalizes and aligns the regions using whitening transforms to produce an affine transform for the scene. To increase efficiency, edge pixels are utilized rather than correlating regions. Results show that the proposed method is able to match scenes containing up to 80 degrees in vertical and horizontal perspective change. The method is superior to stateof-the-art ASIFT algorithms in terms of execution time.

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Section: A Tentative Matching Of Affine Invariant Regionsmentioning

confidence: 99%

“…From [11], [13], [14], given that Y is canonical with I the relation between Y and Y ′ is geometrically congruent via rotation. i.e.…”

Section: B Approximation Of Scene Geometrymentioning

confidence: 99%

Affine invariant matching based on orientation estimation

Brese

Zou

2013

2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS)

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“…As a result, contour-based method can be applied to the closed curve obtained by the CPT for the extraction of affine invariants. Lan and Yang [8] have employed the CPT, the whitening transform and the Fourier transform to extract affine invariants. But objects of completely different appearances may have the same closed curves as the ones obtained by the CPT.…”

Section: Introductionmentioning

confidence: 99%

“…By equation (2.1), a single value corresponds to an angle θ ∈ [0, 2π). Consequently, a closed curve can be derived from any object by employing the CPT, which was used to extract invariant features by Lan and Yang [8] and Tang et al [12]. As previously mentioned, objects with different appearances may have the same CPT.…”

Section: Introductionmentioning

confidence: 99%

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The Mellin Central Projection Transform

Yang

Zhang

2017

ANZIAM J.

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The central projection transform can be employed to extract invariant features by combining contour-based and region-based methods. However, the central projection transform only considers the accumulation of the pixels along the radial direction. Consequently, information along the radial direction is inevitably lost. In this paper, we propose the Mellin central projection transform to extract affine invariant features. The radial factor introduced by the Mellin transform, makes up for the loss of information along the radial direction by the central projection transform. The Mellin central projection transform can convert any object into a closed curve as a central projection transform, so the central projection transform is only a special case of the Mellin central projection transform. We prove that closed curves extracted from the original image and the affine transformed image by the Mellin central projection transform satisfy the same affine transform relationship. A method is provided for the extraction of affine invariants by employing the area of closed curves derived by the Mellin central projection transform. Experiments have been conducted on some printed Chinese characters and the results establish the invariance and robustness of the extracted features.

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Quasi Fourier Descriptor for Affine Invariant Features

Yang

Zhang

et al. 2022

Intelligent Computing Theories and Application

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Whitening central projection descriptor for affine‐invariant shape description

Cited by 9 publications

References 35 publications

Affine invariant matching based on orientation estimation

Affine invariant matching based on orientation estimation

The Mellin Central Projection Transform

Quasi Fourier Descriptor for Affine Invariant Features

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