Rotation invariant pattern recognition using ridgelets, wavelet cycle-spinning and Fourier features

Chen, G. Y.; Bui, Tien D.; Krzyak, Adam

doi:10.1016/j.patcog.2005.02.008

Cited by 41 publications

(18 citation statements)

References 9 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%

Pattern recognition with SVM and dual-tree complex wavelets

Chen

Xie

2007

Image and Vision Computing

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Section: Introductionmentioning

confidence: 99%

Pattern recognition with SVM and dual-tree complex wavelets

Chen

Xie

2007

Image and Vision Computing

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“…Wavelet transform preserves both, the time domain and frequency domain information and also the relation between them. Wavelet theory has so far been widely used for edge detection [2], denoising [5], signal processing using hidden Markov models [8], image enhancement [6], pattern classification [9], pattern recognition [10]. The success of Wavelet transform was mainly due to its good performance in both the dimensions at varying resolutions; but one at a time [8] due to its orthogonality.…”

Section: Y (X Y) =I (X Y) +N(x Y)mentioning

confidence: 99%

Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform

Neole¹,

Lad²,

Bhurchandi³

2017

IJCA

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Denoising is still a challenging area of research due to its commercial and technical applications. We present a novel approach to image denoising using edge profile detection and edge preservation in spatial domain in presence of zero mean additive Gaussian noise. A Noisy image is initially preprocessed using the proposed local edge profile detection and subsequent edge preserving filtering in spatial domain followed further by the modified threshold bivariate shrinkage algorithm. The proposed technique does not require any estimate of standard deviation of noise (σ) present in the image. Performance of the proposed algorithm is presented in terms of PSNR and SSIM on a variety of test images containing a wide range of σ starting from 15 to 100. The performance of the proposed algorithm is better than NL means and Bivariate Shrinkage while it's comparable with BM3D.

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“…Mu ltiple techniqu es are u sed in sketch recognition to d etect or classify regu lar geom etric shapes [6][7][8], hand w riting characters [9,10], fingerprints [11], electric circu its [12,13], d iagram s [14,15], and other u ser com m and gestu res. For instance, w ith a classic linear d iscrim inator, Rubine [16] calcu lated featu res in ord er to classify single-stroke sketches as d igits, letters and basic com m and s introd u ced in a specific w ay.…”

Section: Related Workmentioning

confidence: 99%

New method to find corner and tangent vertices in sketches using parametric cubic curves approximation

Albert

Fernández-Pacheco

2013

Pattern Recognition

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Aleixos Borrás, MN. (2013). New method to find corner and tangent vertices in sketches using parametric cubic curves approximation. Pattern Recognition. 46 (5) Abstract-Some recent approaches have been presented as simple and highly accurate corner finders in the sketches including curves, which is useful to support natural human-computer interaction, but these in most cases do not consider tangent vertices (smooth points between two geometric entities, present in engineering models), what implies an important drawback in the field of design. In this article we present a robust approach based on the approximation to parametric cubic curves of the stroke for further radius function calculation in order to detect corner and tangent vertices. We have called our approach Tangent and Corner Vertices Detection (TCVD), and it works in the following way.First, corner vertices are obtained as minimum radius peaks in the discrete radius function, where radius is obtained from differences. Second, approximated piecewise parametric curves on the stroke are obtained and the analytic radius function is calculated. Then, curves are obtained from stretches of the stroke that have a small radius.Finally, the tangent vertices are found between straight lines and curves or between curves, where no corner vertices are previously located. The radius function to obtain curves is calculated from approximated piecewise curves, which is much more noise free than discrete radius calculation. Several tests have been carried out to compare our approach to that of the current best benchmarked, and the obtained results show that our approach achieves a significant accuracy even better finding corner vertices, and moreover, tangent vertices are detected with an Accuracy near to 92% and a False Positive Rate near to 2%.

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Rotation invariant pattern recognition using ridgelets, wavelet cycle-spinning and Fourier features

Cited by 41 publications

References 9 publications

Pattern recognition with SVM and dual-tree complex wavelets

Pattern recognition with SVM and dual-tree complex wavelets

Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform

New method to find corner and tangent vertices in sketches using parametric cubic curves approximation

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