Illumination Normalization using 2D Wavelet

Emadi, Mehran; Khalid, Marzuki; Yusof, Rubiyah; Navabifar, Farhad

doi:10.1016/j.proeng.2012.07.254

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Discrete wavelet transform is a viable and powerful feature extraction technique to analyse ECG signals locally in multi-resolution manner in time and frequency domain simultaneously and separate the signal frequency sub-bands. A signal can be displayed with different scaling and wavelet basis functions (Emadi et al, 2012). DWT extracts the approximation (low frequency components) and detailed coefficients (high frequency components) as shown in Fig 3 ( and are approximation and detail coefficients and 1, 2 3).…”

Section: Waveletmentioning

confidence: 99%

Pattern Recognition Application in ECG Arrhythmia Classification

Nikan

Gwadry-Sridhar

Bauer

2017

Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies

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In this paper, we propose a pattern recognition algorithm for arrhythmia recognition. Irregularity in the electrical activity of the heart (arrhythmia) is one of the leading reasons for sudden cardiac death in the world. Developing automatic computer aided techniques to diagnose this condition with high accuracy can play an important role in aiding cardiologists with decisions. In this work, we apply an adaptive segmentation approach, based on the median value of R-R intervals, on the de-noised ECG signals from the publically available MIT-BIH arrhythmia database and split signal into beat segments. The combination of wavelet transform and uniform one dimensional local binary pattern (1-D LBP) is applied to extract sudden variances and distinctive hidden patterns from ECG beats. Uniform 1-D LBP is not sensitive to noise and is computationally effective. ELM classification is adopted to classify beat segments into five types, based on the ANSI/AAMI EC57:1998 standard recommendation. Our preliminary experimental results show the effectiveness of the proposed algorithm in beat classification with 98.99% accuracy compared to the state of the art approaches.

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Section: Waveletmentioning

confidence: 99%

Pattern Recognition Application in ECG Arrhythmia Classification

Nikan

Gwadry-Sridhar

Bauer

2017

Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies

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“…Many objective factors restrict the development of face recognition (FR) and facial expression recognition (FER) systems, such as face posture, illumination variation, and so on. Some works [ 1 , 2 ] have pointed out that the changes caused by the variation of illumination could be more significant than the differences between individual’s physical appearance. Furthermore, some researchers [ 3 ] affirm that the variation of illumination could bring more negative influence to FR comparing with the pose and expression.…”

Section: Introductionmentioning

confidence: 99%

“…The recognition rates can be found in table 1 of their paper [ 16 ]. Emadi et al [ 2 ] conclude that the illumination components reside in the low frequency sub-band. In their method, an input face image is decomposed into its high frequency and low frequency components.…”

Section: Introductionmentioning

confidence: 99%

Illumination Normalization of Face Image Based on Illuminant Direction Estimation and Improved Retinex

Mao

Chen

et al. 2015

PLoS ONE

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Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.

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“…The main existing methods are the homomorphic filter algorithm, 11 and the method on wavelet transforms. 12,13 However, neither of them can meet the requirements of high accuracy image quality measurement.…”

Section: Introductionmentioning

confidence: 99%

Novel background calibration algorithm for image in non-uniform illumination field

Wang

et al. 2015

The Imaging Science Journal

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For the purpose of solving the high quality required in some image based measurement systems, a new calibration algorithm for a non-uniform illumination field image is proposed on the basis of some existing algorithm. In this algorithm, by analysing the characteristics of the potential function for target detection, a new potential function for target detection was designed. A function for standardisation of variety was deduced thereafter. After that, the mapping function from the original image to the background calibration image was given. Numerical simulations showed that the proposed algorithm can restrain the non-uniform background illumination field and calibrate the strong fluctuant background of the illumination field image and thus improve the quality of the image. Meanwhile, the algorithm is simple to carry out and very suitable in practice.

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Illumination Normalization using 2D Wavelet

Cited by 11 publications

References 7 publications

Pattern Recognition Application in ECG Arrhythmia Classification

Pattern Recognition Application in ECG Arrhythmia Classification

Illumination Normalization of Face Image Based on Illuminant Direction Estimation and Improved Retinex

Novel background calibration algorithm for image in non-uniform illumination field

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