Comparison and fusion of multiresolution features for texture classification

Li, Shutao; Shawe‐Taylor, John

doi:10.1016/j.patrec.2004.09.013

Cited by 64 publications

(30 citation statements)

References 9 publications

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“…After the image binary, the line of the image is wide. For the feature extraction, the key step is to get the texture of the fingerprint [5], not the width, so a further step of image compressing and thinning is important. The purpose of image thinning is to eliminating some noise points on the base of keeping the original shape.…”

Section: Feature Extractionmentioning

confidence: 99%

Research and Simulation of an Improved Algorithm of Fingerprint Recognition

Li¹,

Miao²,

Zhen³

2017

Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)

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Abstract. The application of biological recognition technology is more and more extensive [1]. Human body carries some unique biological characteristic such as fingerprint or sclera. On the base of the complexity of implementation, most of the enterprises choose fingerprint as the new Recognition way. Fingerprint Recognition technology has good safety and reliability. The traditional Recognition technology will be placed by fingerprint technology with the improvement of fingerprint Recognition. This paper introduces the basic process of the fingerprint Recognition technology includes the image pre-processing, the fingerprint feature extraction and feature matching. Image pre-processing includes image noise reduction, image cutting, and image binary. Feature extraction includes image thinning, minutiae extraction and fake feature point elimination. A look-up table algorithm based an "eight-neighborhood" is used in this step. The research sets an improved algorithm of three steps of matching: ridge line length matching, triangle length matching, and the point type matching. The simulation results indicate that this method has a high anti-noise performance and stability.

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Section: Feature Extractionmentioning

confidence: 99%

Research and Simulation of an Improved Algorithm of Fingerprint Recognition

Li¹,

Miao²,

Zhen³

2017

Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)

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“…In the literature there are studies of time-frequency tools such as a Gabor filter or a wavelet transform which are used for extracting features from the texture of an image (Grigorescu et al, 2002;Huang et al, 2003;Kyrki et al, 2004;Li and Shawe-Taylor, 2005;Tai, 2007). The classifier is trained based on some labelled texture features as the training set, used to classify unlabelled texture features of images into some pre-defined classes.…”

Section: Selected Topics Of Classification Based On Time-frequency Rementioning

confidence: 99%

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Swiercz¹

2010

International Journal of Applied Mathematics and Computer Science

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A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requires a priori knowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to the max discriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

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“…Wavelet scale energy signatures have been used extensively for characterisation of textures [7][8][9][10][11][12][13][14][15]. Another line of wavelet development has been to introduce shift invariant wavelet algorithms.…”

Section: Introductionmentioning

confidence: 99%

The impact of Mexican hat and dual‐tree complex wavelet transforms on multivariate evaluation of image texture properties

Engelbrektsson

Abrahamsson

Breitholtz

et al. 2010

Journal of Chemometrics

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In the present paper, we apply the Mexican hat wavelet and the dual-tree complex wavelet transform (DT-CWT) on five sets of images: synthetic randomly positioned Gaussian peaks, synthetic randomly positioned triangular pillars, compressed score images from NIR, AFM images and images from a time-dependent process, i.e. tablet dissolution. The shift invariant stationary wavelet transform (SWT) was also compared for the synthetic images. After the transforms, the energy levels of the wavelet scales were calculated as the mean of the absolute values in each part of the 2D wavelet scales. These energy values were used as inputs in principal components analysis to estimate the relative changes in texture. It was shown that the energy values from the Mexican hat wavelet transform have a more repeatable response for high frequency features than DT-CWT, which implies that the Mexican hat wavelet transform used in this way have the potential to yield better repeatability than DT-CWT for texture analysis, especially at high spatial frequencies. However, examples are shown where the shift invariant property does not have a major impact on performance for real life samples.

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Comparison and fusion of multiresolution features for texture classification

Cited by 64 publications

References 9 publications

Research and Simulation of an Improved Algorithm of Fingerprint Recognition

Research and Simulation of an Improved Algorithm of Fingerprint Recognition

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

The impact of Mexican hat and dual‐tree complex wavelet transforms on multivariate evaluation of image texture properties

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