Planar Shape Databases with Affine Invariant Search

Startchik, Sergei; Milanese, Ruggero; Rauber, Christian; Pun, Thierry

doi:10.1142/9789812797988_0025

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…5.1. Startchik [39] employed intersections between line segments, bi-tangents and cusp tangents as a representation scheme for the object shape. Other techniques using the affine model for comparing [32].…”

Section: Review Of Related Literaturementioning

confidence: 99%

An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases

Alferez

Wang

Jiao

2005

Multimedia Tools and Applications

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In this paper, we examine the complexities involved in retrieving images from a database comprised of objects of very similar appearance. Such an operation requires a process that can discriminate among images at a very fine level, such as distinguishing among various species of fish. Furthermore, incidental environmental factors such as change in viewpoints and slight, nonessential shape deformation must be excluded from the similarity criteria. To this end, we propose a new method for content-based image retrieval and indexing, one that is well suited for discriminating among objects within the same class in a way that is insensitive to incidental environmental changes. The scheme comprises a global alignment and a local matching process. Affine transform is used to model the different viewpoints associated with positioning the camera, while multidimensional indexing techniques are used to make the global alignment scheme efficient.A local matching process based on dynamic programming allows the optimal matching of local structures using cost metrics that may ignore nonessential local shape deformation.Results show the method's ability to cancel out visual distortions caused by a changing viewpoint, and its tolerance to noise, occlusion, and slight deformations of the object.

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Section: Review Of Related Literaturementioning

confidence: 99%

An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases

Alferez

Wang

Jiao

2005

Multimedia Tools and Applications

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“…Contour matching under similar or affine transformations (e.g. weak perspective) is a notably easier problem [20,21,22]. For instance, robust affine alignment can be based on shape covariance equalization and Fourier analysis.…”

Section: Introductionmentioning

confidence: 99%

Robust Homography Estimation from Planar Contours Based on Convexity

Ruiz

Teruel

Fernández

2006

Computer Vision – ECCV 2006

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We propose a homography estimation method from the contours of planar regions. Standard projective invariants such as cross ratios or canonical frames based on hot points obtained from local differential properties are extremely unstable in real images suffering from pixelization, thresholding artifacts, and other noise sources. We explore alternative constructions based on global convexity properties of the contour such as discrete tangents and concavities. We show that a projective frame can be robustly extracted from arbitrary shapes with at least one appreciable concavity. Algorithmic complexity and stability are theoretically discussed and experimentally evaluated in a number of real applications including projective shape matching, alignment and pose estimation. We conclude that the procedure is computationally efficient and notably robust given the ill-conditioned nature of the problem.

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“…As an alternative to the projective camera model, an affine model can be used. This has the advantage of simplifying the invariant properties, and proves useful in a wide range of applications [21,28]. Advantages of differential invariants include locality, and the coverage of a relatively large class of shapes.…”

Section: Introductionmentioning

confidence: 99%

Projective and illumination invariant representation of disjoint shapes

Startchik

Milanese

Pun

1998

Image and Vision Computing

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We describe a new projectively invariant representation of disjoint contour groups which is suitable for shape-based retrieval from an image database. It consists of simultaneous polar reparametrization of multiple curves where an invariant point is used as the origin. For each ray orientation, a cross-ratio of its intersections with other curves is taken as a value associated to the radius. With respect to other methods this representation is less reliant on single curve properties, both for the construction of the projective basis and for calculating the signature. It is therefore more robust to contour gaps and image noise and is better suited to describing complex planar shapes defined by multiple disjoint curves. The proposed representation has been originally developed for planar shapes, but an extension is proposed and validated for 3D faceted objects. Moreover, we show that illumination invariance fits well within the proposed framework and can easily be introduced in the representation in order to make it more appropriate for shape-based retrieval. Experiments are reported on a database of real trademarks.

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Planar Shape Databases with Affine Invariant Search

Cited by 4 publications

References 11 publications

An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases

An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases

Robust Homography Estimation from Planar Contours Based on Convexity

Projective and illumination invariant representation of disjoint shapes

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