Recognizing Human Emotional State Based on the Phase Information of the Two Dimensional Fractional Fourier Transform

Gao, Lei; Qi, Lin; Chen, Enqing; Mu, Xiaomin; Guan, Ling

doi:10.1007/978-3-642-15696-0_64

Cited by 14 publications

(10 citation statements)

References 9 publications

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“…The ath fractional-order of FrFT can be defined as its effect on classical FT eigenfunctions [31]. FrFT is defined as a linear operator corresponding to the counterclockwise rotation through an angle f a = p 2 in the time-frequency space [33,34]. The FrFT of fractional transform of the desired function such as x(t) can be written as:…”

Section: Fractional Fourier Transformmentioning

confidence: 99%

Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

Mehrabbeik¹,

Rashidi²,

Fallah³

et al. 2020

Biomed. Phys. Eng. Express

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Mitral Valve Prolapse (MVP) is a common condition among people, which is often benign and does not need any serious treatment. However, this doesn’t mean that MVP can’t cause any problems. In malignant conditions, MVP can cause mitral failure and also heart failure. Early diagnosis of MVP is significantly important to control and reduce its complications. Since the phonocardiogram signal provides useful information about heart valves function, it can be used for MVP detection. To detect MVP, the signal was denoised and segmented into heart cycles and constant three-second pieces in the first and second approaches, respectively. Next, based on the Fractional Fourier Transform (FrFT), the desired features were extracted. Then, the extracted features were windowed by a Moving Logarithmic Median Window (MLMW) and optimum features were selected using Mahalanobis, Bhattacharyya, Canberra, and Minkowski distance criteria. Finally, using the selected features, classification was performed by using the K-Nearest Neighbor (KNN) and the Suppor Vector Machine (SVM) classifiers to find out whether a segment is prolapsed. The best results of the experiment on the collected database contain 15 prolapsed and 6 non-prolapsed subjects using the A-test method show 96.25 ± 2.43 accuracy, 98.5 ± 3.37 sensitivity, 94.0 ± 5.16 specificity, 96.0 ± 3.44 precision, 92.5 ± 4.86 kappa, and 96.6 ± 2.34 f-score with the SVM classifier.

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Section: Fractional Fourier Transformmentioning

confidence: 99%

Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

Mehrabbeik¹,

Rashidi²,

Fallah³

et al. 2020

Biomed. Phys. Eng. Express

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“…Furthermore, considering that more and more image processing algorithms pay attention to the FRFT phase for its carrying the importance image texture information [4], we also focus on the statistical analysis of the amplitude and phase parts of FRFT coefficients of natural images. According to the analysis of the previous section, two FRFT orders p 1 ; p 2 2 0; 1 ½ with the interval D ¼ 0:1 are adopted when analyzing the statistical probability distribution of FRFT coefficients of natural images.…”

Section: Statistic Distribution Modeling Of the Frft Coefficients Of mentioning

confidence: 99%

“…Thus, this paper will focus on the statistical probability distribution of FRFT coefficients of natural images. As the FRFT phase gaining more and more attention for carrying the important image texture information [4], this paper will explore the statistical distribution of the amplitude and phase parts also with the real and imaginary parts of FRFT coefficients of natural images. …”

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confidence: 99%

Distribution of FRFT Coefficients of Natural Images

Jiang

Liu

2015

Lecture Notes in Computer Science

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Abstract. For the convenience of providing temporal and spectral information by a single variable, fractional Fourier transformation (FRFT) is more and more applied to image processing recently. This paper focuses on the statistical regularity of FRFT coefficients of natural images and proposes that the real and imaginary parts of FRFT coefficients of natural images take on the generalized Gaussian distribution, the coefficient modulus follow the gamma distribution and the coefficient phase angles tend to the uniform distribution, moreover, the real and imaginary parts of coefficient phases similar to the extended beta distribution. These underlying statistics can provide theoretical basis for image processing in FRFT, such as dimensionality reduction, feature extraction, smooth denoising, digital forensics, watermarking, etc.Fractional Fourier transformation (FRFT), as a kind of generalized Fourier transformation (FT), can be interpreted as a rotation of the signal in the time-frequency plane. Different from wavelet transformation, short-time Fourier transformation, Gabor transformation and other common two-parameters time frequency distributions, FRFT can provide the related local information of the signal in the time domain and the frequency domain simultaneously by a single variable, thus it has a wide application prospect in the field of signal processing [1]. FRFT is also introduced into the image processing, which is a significant branch of signal processing, such as the data compression [2], the image registration [3], the facial expression recognition [4], the image encryption [5], the digital watermarking [6] and so on. For the image processing based on FRFT, it is necessary to learn the related prior knowledge of FRFT coefficients of natural images. However, to the author's knowledge, there has not been relevant research report currently. Thus, this paper will focus on the statistical probability distribution of FRFT coefficients of natural images. As the FRFT phase gaining more and more attention for carrying the important image texture information [4], this paper will explore the statistical distribution of the amplitude and phase parts also with the real and imaginary parts of FRFT coefficients of natural images.

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“…2D-DFrFT can capture more characters of a face image in different angles, and the lower-frequency bands contain most facial discriminating features, while high bands contain the noise. Thus, it has been employed in face recognition [21], human emotional state recognition [22] and facial expression recognition [23], and obtained good results.…”

Section: Introductionmentioning

confidence: 99%

Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network

Shi

Tao

et al. 2019

Remote Sensing

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As an important part of maritime traffic, ships play an important role in military and civilian applications. However, ships’ appearances are susceptible to some factors such as lighting, occlusion, and sea state, making ship classification more challenging. This is of great importance when exploring global and detailed information for ship classification in optical remote sensing images. In this paper, a novel method to obtain discriminative feature representation of a ship image is proposed. The proposed classification framework consists of a multifeature ensemble based on convolutional neural network (ME-CNN). Specifically, two-dimensional discrete fractional Fourier transform (2D-DFrFT) is employed to extract multi-order amplitude and phase information, which contains such important information as profiles, edges, and corners; completed local binary pattern (CLBP) is used to obtain local information about ship images; Gabor filter is used to gain the global information about ship images. Then, deep convolutional neural network (CNN) is applied to extract more abstract features based on the above information. CNN, extracting high-level features automatically, has performed well for object classification tasks. After high-feature learning, as the one of fusion strategies, decision-level fusion is investigated for the final classification result. The average accuracy of the proposed approach is 98.75% on the BCCT200-resize data, 92.50% on the original BCCT200 data, and 87.33% on the challenging VAIS data, which validates the effectiveness of the proposed method when compared to the existing state-of-art algorithms.

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Recognizing Human Emotional State Based on the Phase Information of the Two Dimensional Fractional Fourier Transform

Cited by 14 publications

References 9 publications

Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

Distribution of FRFT Coefficients of Natural Images

Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network

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