Rotation invariant feature extraction using Ridgelet and Fourier transforms

Chen, Y.; Bui, Dinh-Mao; Krzyżak, Adam

doi:10.1007/s10044-006-0028-8

Cited by 29 publications

(17 citation statements)

References 42 publications

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“…This independence can be achieved by preprocessing or by extracting features that are translation-, rotation-, and scale-invariant. Some of the papers on pattern recognition and invariant descriptors include [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Pattern recognition with SVM and dual-tree complex wavelets

Chen

Xie

2007

Image and Vision Computing

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Section: Introductionmentioning

confidence: 99%

“…Tieng and Boles considered wavelet-based affine invariant pattern recognition in [11,12]. More recently, Chen et al proposed two invariant descriptors for pattern recognition by using ridgelets in [13,14].…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition with SVM and dual-tree complex wavelets

Chen

Xie

2007

Image and Vision Computing

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“…DESCRIPTOR This research is based on the work done by Chen et al [14] where enhancements on their work have been made so that the descriptor will now results in a RST invariant Ridgelet transform for images of arbitrary size, hence given the name Invariant Generalised Ridgelet-Fourier descriptor.…”

Section: Invariant Generalised Ridgelet-fouriermentioning

confidence: 99%

“…Another weakness of the existing Ridgelet transform is that they are usually defined on square images. According to [14], Ridgelet transform defined on square images is not suitable for extracting rotation-invariant features. They propose a rotation-invariant Ridgelet transform defined on a circular disc.…”

Section: Introductionmentioning

confidence: 99%

Invariant Generalised Ridgelet-Fourier for shape-based image retrieval

Mustaffa

Ahmad

Mahmod

et al. 2010

2010 International Conference on Information Retrieval &Amp; Knowledge Management (CAMP)

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A new shape descriptor called the Invariant Generalised Ridgelet-Fourier is defined for the application of Content-based Image Retrieval (CBIR). The proposed spectralbased method is invariant to rotation, scaling, and translation (RST) as well as able to handle images of arbitrary size. The implementation of Ridgelet transform on the ellipse containing the shape and the normalisation of the Radon transform is introduced. The 1D Wavelet transform is then applied to the Radon slices. In order to extract the rotation invariant feature, Fourier transform is implemented in the Ridgelet domain. The performance of the proposed method is accessed on a standard MPEG-7 CE-1 B dataset in terms of few objective evaluation criteria. From the experiments, it is shown that the proposed method provides promising results compared to several previous methods.

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“…Chen and Bhattacharya [3] developed an invariant descriptor by using ridgelet packets and the Fourier transform. Chen et al [4] [5] investigated rotation invariant feature extraction by using ridgelets, wavelet cycle-spinning, and the Fourier transform. Bui et al [6] proposed a rotation invariant descriptor for pattern recognition by using orthonormal shell and the Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

Invariant pattern recognition using ring-projection and dual-tree complex wavelets

Chen

Xie

2011

2011 International Conference on Wavelet Analysis and Pattern Recognition

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A novel descriptor is proposed for invariant pattern recognition by using ring-projection and dual-tree complex wavelets. The ring-projection takes the summation of all pixels that lie on the circle with radius r and centre at the centroid of the pattern. This transforms the pattern from a 2-D image to a 1-D signal, so less memory is needed and it is faster than existing 2-D descriptors in the recognition process. Dual-tree complex wavelet transform is applied to the ring-projection since it has the approximate shift-invariant property, which is very important in pattern recognition. Experimental results show that the proposed descriptor in this paper is a feasible choice for invariant pattern recognition.

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Rotation invariant feature extraction using Ridgelet and Fourier transforms

Cited by 29 publications

References 42 publications

Pattern recognition with SVM and dual-tree complex wavelets

Pattern recognition with SVM and dual-tree complex wavelets

Invariant Generalised Ridgelet-Fourier for shape-based image retrieval

Invariant pattern recognition using ring-projection and dual-tree complex wavelets

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