Invariant pattern recognition using radon, dual-tree complex wavelet and Fourier transforms

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

doi:10.1016/j.patcog.2008.10.008

Cited by 46 publications

(15 citation statements)

References 20 publications

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“…The properties of approximately good directional selectivity, magnitude shift-invariance, limited redundancy, efficient linear computation can be harnessed to compute accurate estimates of the geometrical structure in images (the details can be commence in [6][27]). Thus, DT-CWT is natural for analyzing images, and has been pre-owned in some fields of image/video processing, such as image/ video denoising [12], image segmentation [13], pattern recognition [14] and facial gimmick eradication [15][27].…”

Section: Fig 1: Face Recognition Systemmentioning

confidence: 99%

Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier

Bhati¹

2017

IJCA

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Wavelet Transform is basically used for magnitude depletion. It is used for axing the proportion of picture. Including good multi-resolution and multi-scale analysis, wavelet transform also has the propensity of denoting local signal attribute by using the high and low pass filtering, image can be decomposed into divergent scales of approximation components. But in wavelet transform, the higher decomposition layers will lost a lot of information, by which reduce the recognition rate. 2DPCA is a sort of image extraction method deal directly with image data and does not need dimension reduction. It is undertaking image data without step of vectorization. However 2DPCA algorithm reduces the computational complexity, it takes up more storage space. In that paper proposed an advanced technique which is stationed on dual-tree complex wavelet transform and improved 2DPCA employing SVM classifier in order to give higher coherent recognition rate. The experimental results on ORL and YALE face databases shows that the proposed method improves the performance of face recognition with respect to exiting techniques.

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Section: Fig 1: Face Recognition Systemmentioning

confidence: 99%

Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier

Bhati¹

2017

IJCA

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“…[26], the RT and WT methods are combined to deal with texture analysis. The RT, dual-tree complex WT and FT are adopted in pattern recognition [27]. However, in the case of larger scale changes, it induces many noises in the Fourier coefficients when recovering the rotation, scale, and translation parameters.…”

Section: Related Workmentioning

confidence: 99%

SPiraL Aggregation Map (SPLAM): A new descriptor for robust template matching with fast algorithm

Shih

2015

Pattern Recognition

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“…where σ 2 is the variance of the noise per pixel and x = n∈L x(n)/M is the average pixel value of the image x; see for example Chen, Bui, and Krzyzak [4]. Note that the above definition of SNR is independent of image size and can be interpreted as the image SNR and as the average per pixel SNR.…”

Section: Simulationsmentioning

confidence: 99%

“…Gardenier, McCallum, and Bates [3] used the Fourier transform amplitudes in pattern recognition applications. More recently, Chen, Bui, and Krzyżak [4] employed the Radon transform and dualtree complex wavelets, in addition to Fourier transforms, in the invariant pattern recognition. The well-known Fourier descriptor method for the shape analysis and classification is fundamentally different from the proposed method; see Sec.…”

Section: Introductionmentioning

confidence: 99%

Discrete Fourier transform based pattern classifiers

Hui¹,

Żak²

2014

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract.A technique for pattern classification using the Fourier transform combined with the nearest neighbor classifier is proposed. The multidimensional fast Fourier transform (FFT) is applied to the patterns in the data base. Then the magnitudes of the Fourier coefficients are sorted in descending order and the first P coefficients with largest magnitudes are selected, where P is a design parameter. These coefficients are then used in further processing rather than the original patterns. When a noisy pattern is presented for classification, the pattern's P Fourier coefficients with largest magnitude are extracted. The coefficients are arranged in a vector in the descending order of their magnitudes. The obtained vector is referred to as the signature vector of the corresponding pattern. Then the distance between the signature vector of the pattern to be classified and the signature vectors of the patterns in the data base are computed and the pattern to be classified is matched with a pattern in the data base whose signature vector is closest to the signature vector of the pattern being classified.

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Invariant pattern recognition using radon, dual-tree complex wavelet and Fourier transforms

Cited by 46 publications

References 20 publications

Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier

Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier

SPiraL Aggregation Map (SPLAM): A new descriptor for robust template matching with fast algorithm

Discrete Fourier transform based pattern classifiers

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