A new shape descriptor defined on the Radon transform

Tabbone, Salvatore; Wendling, Laurent; Salmon, Jean-Pierre

doi:10.1016/j.cviu.2005.06.005

Cited by 176 publications

(110 citation statements)

References 38 publications

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“…Then we perform another assessment in order to make comparison of the complete method with the state-of-the-art approaches. For this, we first take a few representative global signal-based descriptors: region based Zernike Moments (ZM) [Kim and Kim, 2000], Generic Fourier Descriptors (GFD) [Zhang and Lu, 2002], Shape Context (SC) [Belongie et al, 2002] and R−signature [Tabbone et al, 2006], applied directly to the symbol. Then we take two recent pixel-based approaches: Statistical Integration of Histogram Array (SIHA) Yang [2005] and 2D kernel density Zhang et al [2006] based symbol representation.…”

Section: Methodsmentioning

confidence: 99%

“…Under statistical approaches, global signal-based descriptors [Yuen et al, 1998;Kim and Kim, 2000;Tabbone et al, 2006;Belongie et al, 2002;Lu, 2002, 2004] are usually quite fault tolerant to image distortions, since they tend to filter out small detail changes. This is unfortunately an inconvenient in our context.…”

Section: State-of-the-art 121 Symbol Representationsmentioning

confidence: 99%

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Symbol recognition using spatial relations

Santosh

Lamiroy

Wendling

2012

Pattern Recognition Letters

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In this paper, we present a method for symbol recognition based on the spatio-structural description of a 'vocabulary' of extracted visual elementary parts. It is applied to symbols in electrical wiring diagrams. The method consists of first identifying vocabulary elements into different groups based on their types (e.g., circle, corner ). We then compute spatial relations between the possible pairs of labelled vocabulary types which are further used as a basis for building an Attributed Relational Graph that fully describes the symbol. These spatial relations integrate both topology and directional information.The experiments reported in this paper show that this approach, used for recognition, significantly outperforms both structural and signal-based state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Section: State-of-the-art 121 Symbol Representationsmentioning

confidence: 99%

Symbol recognition using spatial relations

Santosh

Lamiroy

Wendling

2012

Pattern Recognition Letters

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“…There exist several approaches to achieve such invariances (Arodz, 2005). In (Tabbone et al, 2006), they presented a variant of the Radon transform, the R transform, which is invariant to translation and scale.…”

Section: Preliminariesmentioning

confidence: 99%

Action Recognition using the Rf Transform on Optical Flow Images

Carmona

Climent

2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Abstract:The objective of this paper is the automatic recognition of human actions in video sequences. The use of spatiotemporal features for action recognition has become very popular in recent literature. Instead of extracting the spatio-temporal features from the raw video sequence, some authors propose to project the sequence to a single template first.As a contribution we propose the use of several variants of the R transform for projecting the image sequences to templates. The R transform projects the whole sequence to a single image, retaining information concerning movement direction and magnitude. Spatio-temporal features are extracted from the template, they are combined using a bag of words paradigm, and finally fed to a SVM for action classification. The method presented is shown to improve the state-of-art results on the standard Weizmann action dataset.

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“…The RFM shape descriptor is compared with shape context (SC) [4], generic Fourier descriptor (GFD) [9], Zernike moments [18], angular radial transform (ART) [19], R-signature [10], and Radon 2D Fourier-Mellin transforms (R2DFM) [11]. Except for the contour-based SC descriptor, all other descriptors are region-based and additionally Rsignature and R2DFM descriptors are also defined on the Radon transform.…”

Section: Radon Transform Implymentioning

confidence: 99%

“…Current techniques thus usually exploit this encoded information to define invariant descriptors. Notable work in this direction is the R-signature proposed in [10]. This approach uses an integral function for the columns and the 1D Fourier transform for the rows of the Radon image respectively to get an 1D signature of the shape image that is invariant to translation, rotation, and scaling.…”

Section: Introductionmentioning

confidence: 99%

A Geometric Invariant Shape Descriptor Based on the Radon, Fourier, and Mellin Transforms

Hoang

Tabbone

2010

2010 20th International Conference on Pattern Recognition

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To cite this version:Thai V. Hoang, Salvatore Tabbone. A geometric invariant shape descriptor based on the Radon, Fourier, and Mellin transforms. International Conference on Pattern Recognition -ICPR '2010, Aug 2010, Istanbul, Turkey. IEEE, pp.2085-2088, 2010, ICPR 2010 Abstract-A new shape descriptor invariant to geometric transformation based on the Radon, Fourier, and Mellin transforms is proposed. The Radon transform converts the geometric transformation applied on a shape image into transformation in the columns and rows of the Radon image. Invariances to translation, rotation, and scaling are obtained by applying 1D Fourier-Mellin and Fourier transforms on the columns and rows of the shape's Radon image respectively. Experimental results on different datasets show the usefulness of the proposed shape descriptor.

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A new shape descriptor defined on the Radon transform

Cited by 176 publications

References 38 publications

Symbol recognition using spatial relations

Symbol recognition using spatial relations

Action Recognition using the Rf Transform on Optical Flow Images

A Geometric Invariant Shape Descriptor Based on the Radon, Fourier, and Mellin Transforms

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