nils-herrmann/clean_scirepeval huggingface dataset:
DCT-Based Image Protection using Dual-Domain Bi-Watermarking Algorithm

A dual-domain bi-watermarking algorithm is proposed in this paper. This algorithm embeds bi-watermark in DCT domain, but bi-watermark can be extracted in both spatial and DCT domains. Therefore, it can implement on the DCT-based compressed image/frame. The dual-domain bi-watermarking algorithm is the extension of the spatial-domain bi-watermarking algorithm, which implements quantization index modulation with two quantization step sizes. These quantization step sizes construct the non-uniform quantization intervals. Additionally, the luminance quantization table of JPEG compression is considered in the algorithm. In the experimental result, two extracted watermarks show the capability for various compression rates, and the watermarks also reveal the different robustness against the global and the regional attacks.
Generalized polynomial wigner spectrogram for high-resolution time-frequency analysis

A good time-frequency (TF) analysis method should have the advantages of high clarity and no cross term. However, there is always a trade-off between the two goals. In this paper, we propose a new TF analysis method, which is called the generalized polynomial Wigner spectrogram (GPWS). It combines the generalized spectrogram (GS) and the polynomial Wigner-Ville distribution (PWVD). The PWVD has a good performance for analyzing the instantaneous frequency of a high order exponential function. However, it has the cross term problem in the multiple component case. By contrast, the GS can avoid the cross term problem, but its clarity is not enough. The proposed GPWS can combine the advantages of the PWVD and the GS. It can achieve the goals of high clarity, no cross term, and less computation time simultaneously. We also perform simulations to show that the proposed GPWS has better resolution than other TF analysis methods.
Efficient image segmentation algorithm using SLIC superpixels and boundary-focused region merging

An effective graph-based image segmentation using superpixel-based graph representation is introduced. The techniques of SLIC superpixels, 5-D spectral clustering, and boundary-focused region merging are adopted in the proposed algorithm. With SLIC superpixels, the original image segmentation problem is transformed into the superpixel labeling problem. It makes the proposed algorithm more efficient than pixel-based segmentation algorithms and other superpixel-based segmentation methods. With the proposed methods of 5-D spectral clustering and boundary-focused region merging, the position information is used for clustering and the threshold for region merging can be adaptive. These techniques make the segmentation result more consistent with human perception. The simulations on Berkeley segmentation database show that our proposed method outperforms state-of-the-art methods.
Exemplar-embed complex matrix factorization for facial expression recognition

This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wirtinger's calculus was employed to determine derivatives. The gradient descent method was utilized to solve the complex optimization problem. Experiments on facial expression recognition verified the effectiveness of the proposed EE-CMF. It provides consistently better recognition results than standard NMFs.
Color pattern recognition by quaternion correlation

It is popular to use the conventional correlation for pattern recognition. But when using the conventional correlation, the pattern should be the gray-level pattern. In this paper, we discuss how to use discrete quaternion correlation (DQCR) for the application of color pattern recognition. With the algorithm introduced here, we can detect the objects that have the same shape, color, and brightness as the reference pattern. Besides, we can also detect (a) the objects with the same shape, color, but different brightness, (b) the objects with the same shape, brightness, but different color, and (c) the objects just have the same shape as the reference. Our algorithm can classify the objects into 5 classes due to whether their shape, brightness, and color match those of the reference pattern. Besides, with our algorithm, the difference of the brightness and color can also be calculated at the same time.
Eigenfunctions of linear canonical transform

The linear canonical transform (the LCT) is a useful tool for optical system analysis and signal processing. It is parameterized by a 2/spl times/2 matrix {a, b, c, d}. Many operations, such as the Fourier transform (FT), fractional Fourier transform (FRFT), Fresnel transform, and scaling operations are all the special cases of the LCT. We discuss the eigenfunctions of the LCT. The eigenfunctions of the FT, FRFT, Fresnel transform, and scaling operations have been known, and we derive the eigenfunctions of the LCT based on the eigenfunctions of these operations. We find, for different cases, that the eigenfunctions of the LCT also have different forms. When |a+d| 2, the eigenfunctions become the chirp multiplication and chirp convolution of self-similar functions (fractals). Besides, since many optical systems can be represented by the LCT, we can thus use the eigenfunctions of the LCT derived in this paper to discuss the self-imaging phenomena in optics. We show that there are usually varieties of input functions that can cause the self-imaging phenomena for an optical system.
Global sea level change in the past century

Study results derived from global tide gauge data indicate that the global waters were simultaneously subject to the modulation of lunar nodal tide showing a period of 18.6 years and rose at a rate of about 2 mm per year in the past century. In contrast to other regions, those sitting along the east Atlantic Ocean coastal lines show strong semi-diurnal pattern and seemly do not suffer from extraordinary sea level rise problems. Without considering possibilities from local climatic and geological uplift or subsidence problems, the most severe sea level rise phenomenon of 13.24 mm per year happened at Manila, Philippines, a considerable contrast with the most extreme sea level falling rate of 10.11 mm/year at Churchill in Canada. From this study, we are not only able to identify the sea level rise rate for every gauge station from the global data but also be able to predict the timing of the next peak sea level of the 18.6-year tide, which is predicted in June 2025.
Horizontal adaptive disparity estimation scheme for stereoscopic images

Stereo image compression is more and more important because of new display technologies and the needs of 3D movies. As a video sequence, a pair of stereo images is very similar to each other. Therefore, there are usually a lot of redundancies between them. To improve the compression efficiency, an effective method to estimate the target image from the reference image is needed. In this paper, based on some peculiar properties of stereoscopic images, a new 3-state fast block searching algorithm for disparity estimation is proposed. It applies the horizontal search with a fat rectangular search window and uses the thresholding and prediction scheme. Moreover, the technique of variable block size mode is also adopted. This novel algorithm can achieve a performance close to (even a little better than) that of full search, while only 2.7% computation cost is required. In addition, simulations show that the entropy for coding disparity vectors can also be greatly reduced compared to other state-of-the-art searching methods.
Improved reversible integer transform

Integer transform are the discrete transforms whose entries are summations of 2-k. If for an integer transform, we can perfectly recover the input from the output, we call it the reversible integer transform. In 2001, Hao and Shi developed an algorithm that can convert any reversible non-integer transform into a reversible integer transform. In this paper, we improve their works. First, we simplify the way of derivation. Then, we analyze the approximation error and introduce the way to reduce it. We also discuss the problem of bit constraint and how to reduce the number of time cycle in implementation
Closed-form discrete fractional and affine Fourier transforms

The discrete fractional Fourier transform (DFRFT) is the generalization of discrete Fourier transform. Many types of DFRFT have been derived and are useful for signal processing applications. We introduce a new type of DFRFT, which are unitary, reversible, and flexible; in addition, the closed-form analytic expression can be obtained. It works in performance similar to the continuous fractional Fourier transform (FRFT) and can be efficiently calculated by the FFT. Since the continuous FRFT can be generalized into the continuous affine Fourier transform (AFT) (the so-called canonical transform), we also extend the DFRFT into the discrete affine Fourier transform (DAFT). We derive two types of the DFRFT and DAFT. Type 1 is similar to the continuous FRFT and AFT and can be used for computing the continuous FRFT and AFT. Type 2 is the improved form of type 1 and can be used for other applications of digital signal processing. Meanwhile, many important properties continuous FRFT and AFT are kept in the closed-form DFRFT and DAFT, and some applications, such as filter design and pattern recognition, are also discussed. The closed-form DFRFT we introduce has the lowest complexity among all current DFRFTs that is still similar to the continuous FRFT.
Flipping and blending based highly robust in-plane and out-of-plane color face detection

Face detection is very important for video surveillance, human-computer interaction, and face recognition. In this paper, a very robust face detection algorithm that can well detect rotated, in-plane, and out-of-plane faces without large amount of training data is proposed. First, several techniques, including the entropy rate superpixel (ERS) and the skin filter, are applied to obtain face candidate regions. Then, angle compensation and non-maximum suppression are applied to improve the accuracy of face detection. Moreover, to find out-of-plane faces, one can apply the flipping-and-blending technique, i.e., blending the face candidate with its flipping version to create a face that is similar to the frontal one. With it, even if there are no training data for out-of-plane faces, one can successfully detect the faces in the out-of-plane case. Simulations on the FEI dataset and the BaoFace dataset show that the proposed algorithm is efficient and outperforms state-of-the-art face detection approaches.
Eigenfunctions of Fourier and Fractional Fourier Transforms With Complex Offsets and Parameters

In this paper, we derive the eigenfunctions of the Fourier transform (FT), the fractional FT (FRFT), and the linear canonical transform (LCT) with (1) complex parameters and (2) complex offsets. The eigenfunctions in the cases where the parameters and offsets are real were derived in literature. We extend the previous works to the cases of complex parameters and complex offsets. We first derive the eigenvectors of the offset discrete FT. They approximate the samples of the eigenfunctions of the continuous offset FT. We find that the eigenfunctions of the offset FT with complex offsets are the smoothed Hermite-Gaussian functions with shifting and modulation. Then we extend the results for the case of the offset FRFT and the offset LCT. We can use the derived eigenfunctions to simulate the self-imaging phenomenon for the optical system with energy-absorbing component, mode selection, encryption, and define the fractional Z-transform and the fractional Laplace transform.
Adaptive Golomb Code for Joint Geometrically Distributed Data and Its Application in Image Coding

This paper proposes joint-probability-based adaptive Golomb coding (JPBAGC) to improve the performances of the Golomb family of codes, including Golomb coding (GC), Golomb–Rice coding (GRC), exp-Golomb coding (EGC), and hybrid Golomb coding (HGC), for image compression. The Golomb family of codes is ideally suited to the processing of data with geometric distribution. Since it does not require a coding table, it has higher coding efficiency than Huffman coding. In this paper, we find that there are many situations in which the probability distribution of data is not only geometric, but also depends on the probability distribution of the other data. Accordingly, we used the joint probability of generalizing the Golomb family of codes and exploiting the dependence between neighboring image data. The proposed JPBAGC improves the efficiency of many image and video compression standards, such as the joint photographic experts group (JPEG) compression scheme and the H.264-intra JPEG-based image coding system. Simulation results demonstrate the superior coding efficiency of the proposed scheme over those of Huffman coding, GC, GRC, EGC, and HGC.
Improved frequency table adjusting algorithms for context-based adaptive lossless image coding

Lossless image compression can preserve all information of the original image and is useful for security and medical image processing. In this paper, we apply improved adaptive arithmetic coding to further improve the coding efficiency of context-based adaptive lossless image coding (CALIC), which is a well-known lossless image compression algorithm. We apply the techniques of mutual learning, initialization for the frequency table, and the increasingly adjusting step to improve adaptive arithmetic coding and apply them in the CALIC algorithm. Simulations show that, with the proposed coding techniques, the performance of CALIC for lossless image compression can be obviously improved.
Natural images phase encoding and encryption with the same spectrum amplitude

Different images have different amplitude spectrums. However, in this paper, with the proposed phase-key algorithm, we can make a series of images have the same amplitude spectrum but different phase key in the frequency domain. It is useful for image encryption, data compression, image transformation, and reducing the distortion in communication. Our algorithm can be applied for not only the FT but also all the operations with conjugate symmetric kernels. Moreover, we can also reverse the direction of the algorithm, i.e., make a series of images have the same phase spectrum and use the amplitude spectra to distinguish different images.
An efficient sky detection algorithm based on hybrid probability model

Sky is one of the most significant subject matter commonly seen in outdoor photos. We propose a highly efficient sky detection algorithm. First, we detect a rough sky-ground boundary. Then, we calculate the parameters related to appearance of sky. Finally, we use these parameters to construct a hybrid probability model that indicates how possible a pixel belongs to sky. Moreover, an image processing library with parallel processing techniques is embedded in the proposed algorithm. The proposed algorithm has both high accuracy and high efficiency and can process a video image in real time. If the input is a VGA size image, the computation time of the proposed algorithm is less than 35ms when using a common desktop.
An improved approach for face detection using superpixels, moment-based matching, and isosceles triangle matching

Face detection serves as a crucial step for a wide range of applications in computer vision. In this paper, we delve into the task of face detection. An algorithm is proposed for colour images to be robust to varied illumination, background setting, head pose and skin colour. Taking advantage of the superpixel segmentation followed by our trained SVM classifier, we are able to identify different skin-tone faces and generate face candidates. The moment-based elliptic shape matching is performed to remove invalid facial regions. Based on chroma and luma components, our scheme establishes the Eyemap and the Mouthmap to yield a pool of candidates for facial features. A delicate examination procedure considering the texture, colour and spatial relations with respect to the eyes-mouth pair is employed to verify each face candidate. Experimental results demonstrate better detection on the Caltech database in terms of F-measure. Results also show that our proposed algorithm more effectively rules out non-human faces than state-of-the-art algorithms.
Relations Between Gabor Transforms and Fractional Fourier Transforms and Their Applications for Signal Processing

Many useful relations between the Gabor transform (GT) and the fractional Fourier transform (FRFT) have been derived. First, we find that, like the Wigner distribution function (WDF), the FRFT is also equivalent to the rotation operation of the GT. Then, we show that performing the scaled inverse Fourier transform (IFT) along an oblique line of the GT of f(t) can yield its FRFT. Since the GT is closely related to the FRFT, we can use it for analyzing the characteristics of the FRFT. Compared with the WDF, the GT does not have the cross-term problem. This advantage is important for the applications of filter design, sampling, and multiplexing in the FRFT domain. Moreover, we find that if the GT is combined with the WDF, the resultant operation [called the Gabor-Wigner transform (GWT)] also has rotation relation with the FRFT. We also derive the general form of the linear distribution that has rotation relation with the FRFT.
Multilayer image disparity estimation and blending for light field cameras

Due to the development of hand-held Plenoptic cameras, the light filed camera has become more and more popular in recent years. The light field camera can capture the angular and depth information and is able to construct a stereoscopic image. To obtain a high quality reconstructed image, the disparity should be estimated accurately. However, for the sub-images taking from the light field camera, especially for those of the microscope case, it usually happens that there is no feature point within a patch and the disparity is hard to be determined accurately. In this paper, to improve the efficiency and the accuracy of disparity estimation for light field cameras, we propose a multilayer matching architecture. Moreover, a gradient map is applied to assign the weight for disparity matching. In addition, we apply the segmentation method to the rendered final image and refine the disparity estimation result. Simulations show that a more accurate depth estimation result can be achieved when using the proposed architecture.
Freezing of gaits detection for Parkinson's disease patients using fast time-frequency analysis methods and onset detection

Parkinson's disease (PD) patients often suffer from the freezing of gait (FOG) problem, which interferes their daily life. In this paper, we develop an algorithm which uses fast time-frequency analysis methods and onset detection to detect FOG in real time. Simulations show that the specificity, the sensitivity, and the accuracy of the proposed algorithm is 81.83%, 82.66%, and 82.83%, respectively, which are better than existing FOG detection algorithms. Furthermore, since the asymmetric and shorter response smooth filter is applied, the time latency of the proposed algorithm is only 0.95 second, which is less than other methods. Our algorithm can help PD patients overcome the difficulty of walking and is easier to be implemented in hardware.
FasterMDNet: Learning model adaptation by RNN in tracking-by-detection based visual tracking

Recently in VOT competitions, trackers based on tracking-by-detection and deep neural network discriminators reached impressive accuracy. However, these trackers require time-consuming model adaptation methods like online learning to handle target appearance changes, which tremendously increases the time complexity and becomes an obstruction in realworld applications for these tracking algorithms. In this paper, we propose an efficient RNN-based model adaptation method which extremely decreases the time complexity of trackers. The proposed model learns the relations of relative model change in RNN training and predicts the score and the model adaptation state at the same time in testing, which nearly removes the finetuning time in the cost of additional RNN training. The proposed method is applicable to any tracker based on neural network discriminator. The RNN branch can be further designed with more complicated model under the condition of enough training videos. We apply the proposed algorithm to MDNet and create a new tracker: Faster-MDNet. According to the experiment, using our method can nearly remove the time of finetuning and reduce the bottleneck of time-complexity down to the prediction time.
Context-based adaptive zigzag scanning for image coding

Coefficient scanning plays an important role in block-based image and video coding standards, such as JPEG, MPEG, and the latest H.264/AVC. In these coding standards, the zigzag scanning method is used for image / frame coding and the field scanning method is used for field coding. Although these scanning methods can achieve acceptable coding efficiency, they do not take the statistical properties of the quantized coefficients of each block into consideration. Therefore, coding redundancy still exists. In this paper, we propose a novel adaptive zigzag scanning scheme, which is called the context-based adaptive coefficient scanning method, and apply it in the lossy JPEG baseline algorithm. Simulation results show that our proposed method achieves better coding efficiency than both the conventional zigzag scanning method and the two-stage zigzag scanning (TSZS) method adopted in JPEG.
Compression for the feature points with binary descriptors

Feature points, such as SIFT, BRISK, ORB, and FREAK, are effective for template matching, pattern recognition, and object alignment. However, since an image usually has 200-4000 feature points and the size of each descriptor is 512 or 256, an efficient way for encoding the descriptors and locations of feature points is required. In this paper, we propose an algorithm to encode the descriptors, locations, and angles of BRISK, ORB, and FREAK points efficiently. We apply both the global and local statistical characteristics and apply different reference points for the cases where the previous bit is 1 or 0. Moreover, the facts that feature points do not uniformly distribute and that two feature points with a short distance always have a small angle difference are also applied for compression. Simulations show that the proposed algorithm can much reduce the data sizes required for encoding feature points.
Edge adaptive hybrid norm prior method for blurred image reconstruction

Image deblurring is important for photography and biomédical engineering. There are many existing deblurring methods. However, it is still a challenge to reconstruct images clearly without increasing the effect of noise. In this paper, based on that the probability distributions of gradients vary for different parts of an image, we apply edge adaptation and hybrid norm prior and propose a new deblurring algorithm. Simulations for blurred and noisy images and the biological images acquired from electron microscopes show that, with the proposed algorithm, the reconstructed images can achieve the goals of high clarity and noise robustness at the same time.