nils-herrmann/clean_scirepeval huggingface dataset:
Commutative reduced biquaternions and their Fourier transform for signal and image processing applications

Digital signal and image processing using reduced biquaternions (RBs) are introduced in this paper. RBs are an extension of the complex numbers, following the doubling procedure. Two useful representations of RBs (e/sub 1/-e/sub 2/ form and matrix representation) are discussed in this paper. Besides, we propose a new representation of RBs (the polar form) to calculate the multiplication and conjugation of RBs easily. Furthermore, we define a unique and suitable RB norm and its conjugate. These definitions are similar and compatible with the complex numbers. The efficient algorithms of the discrete reduced biquaternion Fourier transform (DRBFT), convolution (DRBCV), correlation (DRBCR), and phase-only correlation are discussed in this paper. In addition, linear-time-invariant and symmetric multichannel complex systems can be easily analyzed by RBs. For color image processing, we define a simplified RB polar form to represent the color image. This representation is useful to process color images in the brightness-hue-saturation color space. Many different types of color template matching and color-sensitive edge detection (brightness, hue, saturation, and chromaticity matched edges) can be performed simultaneously by RBs.
Improved structural similarity measurement for vocal signals

In recent years, the SSIM was proposed for image and vocal signal assessments to match human perception. The existing SSIMs for vocal signals are similar to those for images. However, the human perceptions for voices and images are different. If two vocal signals differ only by phase, delay, or logistic frequency shift, they are heard similarly. In this paper, we propose the non-uniform sampling frequency mean SSIM (NUS-FMSSIM) to highly match the human perception for voices. Simulations show that it is more robust to phase change, time shift, and logistic frequency shift than the existing SSIMs for vocal signals.
Saliency detection improved by Principle Component Analysis and boundary scoring approach

Salient region detection is useful for many image processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. In this paper, we propose a novel method to determine salient regions in images. Principle Component Analysis (PCA) is served as preprocessing for dimensionality reduction. It can reduce computational complexity and attenuate noise and translation error. Then, the local-global contrast is used to calculate distinctiveness. Finally, we take advantage of image segmentation to achieve full resolution saliency maps. Our proposed method is compared with the state-of-art saliency detection methods and yields higher precision and better recall rate.
Short response Hilbert transform for edge detection

In this paper, we define the short-response Hilbert transform (SRHLT) and use it for edge detection. The SRHLT has a parameter b. When b = 0, it becomes the Hilbert transform (HLT). When b is infinite, it becomes differentiation. Many edge detection algorithms are based on differentiation. However, they are sensitive to noise. By contrast, when using the HLT for edge detection, the noise is reduced but the resolution is poor. The proposed SRHLT in this paper can compromise the advantages of differentiation and HLTs. It is robust to noise and can simultaneously distinguish edges from non-edge regions very successfully.
Closed-Form Orthogonal DFT Eigenvectors Generated by Complete Generalized Legendre Sequence

In this paper, we propose a new method for deriving the closed-form orthogonal discrete Fourier transform (DFT) eigenvectors of arbitrary length using the complete generalized Legendre sequence (CGLS). From the eigenvectors, we then develop a novel method for computing the DFT. By taking a specific eigendecomposition to the DFT matrix, after proper arrangement, we can derive a new fast DFT algorithm with systematic construction of an arbitrary length that reduces the number of multiplications needed as compared with the existing fast algorithm. Moreover, we can also use the proposed CGLS-like DFT eigenvectors to define a new type of the discrete fractional Fourier transform, which is efficient in implementation and effective for encryption and OFDM.
A real-time detection algorithm for freezing of gait in Parkinson's disease

Freezing of gait (FOG) is a symptom of Parkinson's disease (PD). In this paper, we develop a real-time FOG detection algorithm. To achieve the goal of real time, we adopt the generalized spectrogram with asymmetric windows and the asymmetric smooth filter. Furthermore, instead of using only the frequency domain information, we apply the features in both the time domain and the frequency domain. Simulations show that the proposed FOG detection algorithm has higher accuracy than other methods. Moreover, the time delay of the proposed algorithm is only 1 second, which is 2.25-3.75 times less than that of existing methods.
Fractional cosine, sine, and Hartley transforms

In previous papers, the Fourier transform (FT) has been generalized into the fractional Fourier transform (FRFT), the linear canonical transform (LCT), and the simplified fractional Fourier transform (SFRFT). Because the cosine, sine, and Hartley transforms are very similar to the FT, it is reasonable to think they can also be generalized by the similar way. We introduce several new transforms. They are all the generalization of the cosine, sine, or Hartley transform. We first derive the fractional cosine, sine, and Hartley transforms (FRCT/FRST/FRHT). They are analogous to the FRFT. Then, we derive the canonical cosine and sine transforms (CCT/CST). They are analogous to the LCT. We also derive the simplified fractional cosine, sine, and Hartley transforms (SFRCT/SFRST/SFRHT). They are analogous to the SFRFT and have the advantage of real-input-real-output. We also discuss the properties, digital implementation, and applications (e.g., the applications for filter design and space-variant pattern recognition) of these transforms. The transforms introduced in this paper are very efficient for digital implementation. We can just use one half or one fourth of the real multiplications required for the FRFT and LCT to implement them. When we want to process even, odd, or pure real/imaginary functions, we can use these transforms instead of the FRFT and LCT. Besides, we also show that the FRCT/FRST, CCT/CST, and SFRCT/SFRST are also useful for the one-sided (t /spl isin/ [0, /spl infin/]) signal processing.
Reversible integer color transform with bit-constraint

In color image processing, the RGB color coordinate is usually transformed into another one (e.g., YIQ or KLA) for system fitting or other purposes. Most of the color transforms are done by 3/spl times/3 matrices. However, these matrices are always not fixed-point. In this paper, we use a systematic algorithm to convert every 3/spl times/3 color transform into a reversible integer-to-integer transform. The resulted transform can be implemented with only fixed-point processor and no floating-point processor is required. Moreover, with the use of ladder-truncation technique, we can make least bit of the output the same as that of the input, and the long bit-length problem that always occurs for other integer transforms can be avoided. We derive the integer color transforms of RGB-to-KLA, IV/sub 1/V/sub 2/, YCrCb, DCT, and YIQ successfully.
Advanced query by humming system using diffused hidden Markov model and tempo based dynamic programming

Query by humming (QBH) is a content-based system to identify which song a person sang. In this paper, we proposed a note-based QBH system which apply the hidden Markov model and dynamic programming to find the most possible song. Also, we proposed several techniques to improve the QBH system performance. First, we propose a modified method for onset detection. The frequency information is also used in this part By time-frequency analysis, we can find out the onset points which are difficult to be picked up in the time domain. Besides the pitch feature, the beat information and possible pitch and humming errors are also considered for melody matching. The tempo feature is also an important part for a song. Even though the pitch sequences of two songs are the same, if the tempo is clearly different, then they are complete different songs. Also the possible singing errors are considered. Simulations show that the performance can be much improved by our proposed methods.
Prediction techniques for wavelet based 1-D signal compression

This paper proposes a novel one-dimensional (1-D) signal compression technique. We first perform beat-alignment to transform a 1-D signal into 2-D, then use 2-D discrete wavelet transform (DWT) to further decompose the 2-D signal into multiple subbands. These coefficients in certain subbands are then coded using a simple differential pulse code modulation (DPCM). After which, we construct neural networks one for each subband (except the LL subband) to perform prediction. Based on the prediction results, we construct a type of pixel-wise context A to determine the activity of a given pixel. At last, the DWT coefficients and residues from DPCM are bit-plane coded using the Embedded Block Coding with Optimized Truncation (EBCOT) from JPEG2000. We analyzed our results using a well- known 1D signal, the ECG signals in the MIT-BIH database, and it demonstrated significant improvement over existing methods.
Quaternion matrix singular value decomposition and its applications for color image processing

In this paper, we first discuss the singular value decomposition (SVD) of a quaternion matrix and propose an algorithm to calculate the SVD of a quaternion matrix using its equivalent complex matrix. The singular values of a quaternion matrix are still real and positive, but the two unitary matrices are quaternion matrices with quaternion entries. Then, applications for color image processing by the SVD of a quaternion matrix are given. Since a quaternion matrix can represent a color image, so we can use the SVD of a quaternion matrix to decompose a color image. Therefore, many useful image processing methods by SVD, such as eigen-images, image compression, image enhancement and denoise, can be extended to color image processing without separating the color image into three channel images.
Haze detection and haze degree estimation using dark channels and contrast histograms

Haze and mist always affect the quality of vision. If an image is suffered from haze or mist, then the object is unclear and the image seems whiter than the original one. There are several haze removal algorithms that can reduce the effect of haze and mist. However, if an image is not suffered from the haze and mist, applying the haze removal algorithm may darken the image. Therefore, in computer vision, it is important to determine whether an image is suffered from haze or mist. In this paper, we propose an algorithm that applies the histograms of contrast and dark channels together with the support vector machine to determine whether an image is interfered by haze or mist and the degree of the interference. Simulations show that the proposed algorithm can well distinguish the haze/ mist image from a normal image and accurately determine the haze degree of each image.
Morphology-based disparity estimation and rendering algorithm for light field images

A light field camera can retrieve the depth information and reconstruct a stereoscopic image. The quality of light field image processing is highly dependent on whether the disparity of two adjacent micro images can be determined accurately. In this paper, we apply an algorithm based on dilating image segments to determine disparity precisely. With the dilation operation, the edge and corner information, which are important features for template matching, can be well preserved for each segment. Moreover, we also apply the techniques of superpixel-based object extraction and intensity compensation to further improve the performance. Simulations show that, with the proposed algorithm, a more accurate depth map and a rendering image with higher quality can be achieved.
Saving the bandwidth in the fractional domain by generalized Hilbert transform pair relations

In this paper, we develop some methods to save the bandwidth required in the fractional domain. The fractional domain is the transformed domain of the fractional Fourier transform (FRFT). It is the intermediate of the time domain and the frequency domain. We find that, with the aid of the fractional Hilbert transform and other techniques, we can save 1/2 or 3/4 of the bandwidth in the fractional domain if the signal is causal, real, a real signal multiplied by chirp, a fractal, or a finite duration signal. The efficiency of the FRFT can hence be improved.
Integer discrete Fourier transform and its extension to integer trigonometric transforms

DFT has good quality of performance and fast algorithms. But when we implement the DFT, we require the floating-point multiplication. In this paper, we introduce the integer Fourier transform (ITFT). ITFT is approximated to the DFT, but all the entries in the transform matrix are integer numbers. So it only requires fixed-point multiplication, and the implementation can be much simplified, especially for VLSI. This new transform will work similarly to the original DFT, for example, the transform results are similar and the shifting-invariant property is also preserved for ITFT. We also introduce the general method to derive the integer transform. By this approach, we can derive many types of integer transforms (such as integer cosine, sine, and Hartley transforms).
ECG baseline extraction by gradient varying weighting functions

The electrocardiogram (ECG) signal is important for diagnosing cardiovascular diseases. However, in realistic scenario, the measured ECG signal is prone to be interfered by the artifacts caused from the respiration and the movement of patients. This artifact is called baseline wandering or baseline drifting and will lead to misdiagnosis if it is severe. Thus, pre-processing the measured ECG signal is necessary to make correct diagnosis. In this paper, we proposed a robust pre-processing method for extracting the baseline of ECG signals by the gradient varying weighting function. Our approach is adaptive to the input signal and is able to preserve the features of the ECG signal precisely. Simulation results show that our method outperforms other frequently used baseline extraction methods and has a good performance even if the input ECG signal is severely interfered by baseline drifting.
Local prediction based adaptive scanning for JPEG and H.264/AVC intra coding

In this paper, a new adaptive scanning scheme, which is called local prediction based adaptive scanning (LPBAS), is proposed for discrete cosine transform (DCT) based image compression techniques including JPEG and H.264/AVC intra coding. The conventional zigzag scan order is widely used in image and video coding standards, but it ignores the statistical properties of DCT blocks and has limited performance. In this paper, the LPBAS scheme is proposed to achieve the entropy coding gain, where the scan order patterns are adaptively generated and updated based on the statistics of local neighboring DCT blocks. The proposed scheme improves the efficiency of the two image coding systems, JPEG and the H.264/AVC intra coding system. Simulation results showed that the proposed scheme indeed outperforms the zigzag scanning method and other existing adaptive scanning methods.
Reversible Integer Color Transform

In this correspondence, we introduce a systematic algorithm that can convert any 3 times 3 color transform into a reversible integer-to-integer transform. We also discuss the ways to improve accuracy and reduce implementation complexity. We derive the integer RGB-to-KLA, IV<sub>1</sub>V<sub>2</sub>, YC<sub>b</sub>C<sub>r</sub>, DCT, YUV, and YIQ transforms that are optimal in accuracy
Adaptive growing and merging algorithm for image segmentation

Image Segmentation plays an important role in image processing as it is at the foundation of many high-level computer vision tasks, such as scene understanding and object recognition. In this paper, an adaptive growing and merging algorithm is proposed to segment an image accurately. First, mean shift is applied to produce superpixels, and then superpixels grow according to their lab histograms and textures under the constraint of the edge's intensity. In adaptive region merging, we use the proposed dissimilarity measures, which are based on colors, textures, region sizes and multi-scale contour maps with non-constant weights that are adaptive to the region features. Furthermore, we take account of the contact rate of two adjacent regions to avoid over-merging. We also exploit the saliency map to maintain the main objects when the number of regions is small. The simulations on Berkeley segmentation database show that our proposed method outperforms state-of-the-art methods.
Low complexity time-frequency analysis methods for efficient implementation

Since the time-frequency distribution can analyze how the instantaneous frequency of a signal varies with time, it is very effective for signal processing. However, due to that the time-frequency distribution has a two-dimensional output, it has a higher complexity and requires more computation time. In this paper, we find that if the window of the short-time Fourier transforms (STFTs) has some specific form, then they can be implemented in very efficient ways. Their performances are similar to that of the conventional STFT but the complexity is reduced from O(TNlog2N) to O(TF) and have much lower cost for hardware implementation.
Fractional Fourier Transforms and Wigner Distribution Functions for Stationary and Non-Stationary Random Process

In this paper, we discuss the relations among the random process, the Wigner distribution function, the ambiguity function, and the fractional Fourier transform (FRFT). We find many interesting properties. For example, if we do the FRFT for a stationary process, although the result in no longer stationary, the amplitude of its covariance function is still independent of time. Moreover, for the FRFT of a stationary random process, the ambiguity function will be a radiant line passing through (0, 0) and the Wigner distribution function will be invariant along a certain direction. We also define the fractional stationary random process and find that a non-stationary random process can be expressed a summation of fractional stationary random processes. The proposed theorems will be useful for filter design, noise synthesis and analysis, system modeling, and communication
Improved reversible integer-to-integer color transforms

The integer color transform is a reversible operation that can transform one color coordinate into another one and both the inputs and the outputs are of integer forms. In this paper, we improve the integer color transforms derived in previous works. First, we relax the constraint that the scaling for each row should be the same. From this, the method of deriving the integer color transform becomes more flexible and we can derive the integer color transform with less implementation time and higher accuracy. Moreover, we use the new criterion, bit extension, to measure the performance of the integer color transform and propose a new way for accuracy analysis. With the proposed method, we derive the reversible integer RGB-to-YCbCr, KLA, XYZ, UVW, RcGcBc, and YUV transforms with even higher accuracy successfully.
Nonlocal means image denoising based on bidirectional principal component analysis

In this paper, a very efficient image denoising scheme, which is called nonlocal means based on bidirectional principal component analysis, is proposed. Unlike conventional principal component analysis (PCA) based methods, which stretch a 2D matrix into a 1D vector and ignores the relations between different rows or columns, we adopt the technique of bidirectional PCA (BDPCA), which preserves the spatial structure and extract features by reducing the dimensionality in both column and row directions. Moreover, we also adopt the coarse-to-fine procedure without performing nonlocal means iteratively. Simulations demonstrated that, with the proposed scheme, the denoised image can well preserve the edges and texture of the original image and the peak signal-to-noise-ratio is higher than that of other methods in almost all the cases.
Noise removing for time-variant vocal signal by generalized modulation

Since the instantaneous frequencies of vocal signals always vary with time, it is inconvenient to use the conventional filter to remove the noise of vocal signals. In this paper, we propose a method that uses the generalized modulation to reshape and minimize the areas of the spectrograms of vocal signals. Instead of multiplying an exponential function with the first order phase, the generalized modulation is to multiply an exponential function whose phase is a higher order polynomial. With the proposed noise-removing algorithm based on generalized modulation, the signal part and the noise part of a vocal signal can be well separated and the effect of noise can be significantly reduced.
Collagen second harmonic generation image analysis for diabetes determination

A collagen second harmonic generation (SHG) and third harmonic generation (THG) image analysis algorithm based on feature extraction and classification for diabetes disease determination is developed in the paper. It is designed to find the early symptoms of diabetes by using the proposed method to calculate diabetes estimation indicator. The algorithm detects the basic diabetes featured elements on images of human dermis skin. The proposed algorithm consists of the processes of smoothing, adaptive three-region thresholding, binarization, segmentation, feature extraction, and diabetes disease determination. This feature classification criterion provides a new viewpoint to help diabetes diagnosis.
New feature extraction methods in the time-frequency plane for Chinese tone analysis

Since the Chinese tone is highly related to the variation of instantaneous frequency, it is effective to use time-frequency analysis to identify the Chinese tone. In this manuscript, we find that, in addition to the change of instantaneous frequency, the variation of power, the normalized powers in the first and the second harmonics, the frequency turn, and the information of the previous tone are also useful for distinguishing the tone. We also propose a way to identify the 5th tone. In sum, we adopt 18 features and apply the technique of the support vector machine. Simulations show that the proposed algorithm can accurately identify the Chinese tone.
Facial age estimation based on label-sensitive learning and age-specific local regression

In this paper, a new age estimation framework considering the intrinsic properties of human ages is proposed, which improves the dimensionality reduction techniques to learn the connections between facial features and aging labels. To enhance the performance of dimensionality reduction, a distance metric adjustment step is introduced in advance to achieve a suitable metric in the feature space. In addition, to further exploit the ordinal relationship of human ages, the “label-sensitive” concept is proposed, which regards the label similarity during the learning phase of distance metric and dimensionality reduction. Finally, an age-specific local regression algorithm is proposed to capture the complicated aging process for age determination. From the simulation results, the proposed framework achieves the lowest mean absolute error against the existing methods.