nils-herrmann/clean_scirepeval huggingface dataset:
So-BRIEF: Fast recognition of rectangular objects

Much research has been conducted in computer vision about feature extraction. In recent years, binary descriptors have been proved to be extremely fast and yet highly discriminative. So-BRIEF aims at bringing the benefits of this kind of local descriptors to the recognition of rectangular objects, such as books, CD covers, boxes, paints, boards and cell phones. It takes advantage of the special geometry of these objects to recognize very efficiently an image, even rotated or distorted. Our main contribution is this new descriptor along with the search for the optimal values of parameters leading to an extremely fast image matching process. We compare it to 2D-DCT based description techniques. This paper also encompasses a discussion about a new method for efficient detection of rectangular structures.
Generalized Commuting Matrices and Their Eigenvectors for DFTs, Offset DFTs, and Other Periodic Operations

It is well known that some matrices (such as Dickinson-Steiglitz's matrix) can commute with the discrete Fourier transform (DFT) and that one can use them to derive the complete and orthogonal DFT eigenvector set. Recently, Candan found the general form of the DFT commuting matrix. In this paper, we further extend the previous work and find the general form of the commuting matrix for any periodic, quasi-periodic, and offset quasi-periodic operations. Using the general commuting matrix, we can derive the complete and orthogonal eigenvector sets for offset DFTs, DCTs of types 1, 4, 5, and 8, DSTs of types 1, 4, 5, and 8, discrete Hartley transforms of types 1 and 4, the Walsh transform, and the projection operation (the operation that maps a whole vector space into a subspace) successfully. Moreover, several novel ways of finding DFT eigenfunctions are also proposed. Furthermore, we also extend our theories to the continuous case, i.e., if a continuous transform is periodic, quasi-periodic, or offset quasi-periodic (such as the FT and some cyclic operations in optics), we can find the general form of the commuting operation and then find the complete and orthogonal eigenfunctions set for the continuous transform.
Eigenfunctions of the canonical transform and the self-imaging problems in optical system

The affine Fourier transform (AFT) also called as the canonical transform. It generalizes the fractional Fourier transform (FRFT), Fresnel transform, scaling operation, etc., and is a very useful tool for signal processing. We derive the eigenfunctions of the AFT. The eigenfunctions seems hard to be derived, but since the AFT can be represented by the time-frequency matrix (TF matrix), we can use the matrix operations to derive its eigenfunctions. Then, because many optical systems can be represented as a special case of the AFT, the eigenfunctions of the AFT are just the light distributions that will cause the self-imaging phenomena for some optical systems. We use the eigenfunctions we derive to discuss the self-imaging phenomena.
A region adaptive encoding algorithm for simple image compression

In this paper, an effective algorithm for compressing simple images, such as cartoons and man-drawn images, is proposed. Compared to existing methods, the proposed algorithm applies several new techniques. First, we classify the regions of an image into 4 classes (uniform, semi-uniform, multiple DCs, and non-uniform). For different classes, different coding algorithms are applied. Second, instead of calculating the average, we apply majority voting to determine DC terms. Moreover, a dividing and 2nd order polynomial approximation scheme is applied for boundary encoding. Simulations show that, when compressing simple images, the proposed algorithm much outperforms other state-of-the-art algorithms, especially in perception.
Generalized eigenvectors and fractionalization of offset DFTs and DCTs

The offset discrete Fourier transform (DFT) is a discrete transform with kernel exp[-j2/spl pi/(m-a)(n-b)/N]. It is more generalized and flexible than the original DFT and has very close relations with the discrete cosine transform (DCT) of type 4 (DCT-IV), DCT-VIII, discrete sine transform (DST)-IV, DST-VIII, and discrete Hartley transform (DHT)-IV. In this paper, we derive the eigenvectors/eigenvalues of the offset DFT, especially for the case where a+b is an integer. By convolution theorem, we can derive the close form eigenvector sets of the offset DFT when a+b is an integer. We also show the general form of the eigenvectors in this case. Then, we use the eigenvectors/eigenvalues of the offset DFT to derive the eigenvectors/eigenvalues of the DCT-IV, DCT-VIII, DST-IV, DST-VIII, and DHT-IV. After the eigenvectors/eigenvalues are derived, we can use the eigenvectors-decomposition method to derive the fractional operations of the offset DFT, DCT-IV, DCT-VIII, DST-IV, DST-VIII, and DHT-IV. These fractional operations are more flexible than the original ones and can be used for filter design, data compression, encryption, and watermarking, etc.
Muscle injury determination by image segmentation

Clinical examination of Congenital Muscular Torticollis (CMT) is often carried out by ultrasound equipments. However, a variety of subjective factors during diagnosis may result in wrong decision. Thus, we propose an image processing algorithm to derive the objective judgment on the healthiness of muscle in this paper. We first apply image segmentation technique, such as the fast scanning algorithm, for ultrasonic muscle image segmentation. Then, the proposed algorithms are applied to determine the healthiness of muscle fibers. We furthermore propose a score criterion to evaluate the degree of injury. The experimental results show that the injury score measured by the proposed methods can successfully determine whether the muscle is hurt and infer the extent of fibrosis.
Lossless contour compression using morphology, chain coding, and distribution transform

Chain coding is widely used in image compression to encode the boundaries of objects efficiently. Although chain codes are effective, they still need large amount of memory to store the codes. Therefore, an efficient encoding technique for chain codes is required. In this paper, we propose an algorithm to encode contours losslessly. First, the morphological operation is applied to shrink the contours if the process is invertible. Then, the modified Angle Freeman chain code is used to represent the contours, and the distribution transform is applied to rearrange the binary stream and the proposed improved adaptive arithmetic code is adopted for encoding. Simulations show that the proposed algorithm can much reduce the data size required for encoding contours.
High quality and efficient light field image rendering based on white image compensation

Image rendering is an important technique in light filed image processing. It is to reconstruct the entire scene by combining micro images. However, due to the imperfect optical design of the light field camera, micro images suffer from the attenuation effect, which causes server blocking artifacts in the final rendered image. In this work, a high quality and efficient image rendering algorithm based on the linear blending and white image compensation is proposed. First, the compensation model is estimated from the white image. Then, an adaptive modified function is adopted to adjust the compensation rate. Finally, the light field image can be reconstructed by applying the linear blending technique. Simulations show that the proposed light field image rendering algorithm is able to reconstruct a high quality light field image with less artifacts than existing methods.
Image retrieval based on classified vector quantization using color local thresholding classifier

A method of natural image classification by an effective color quadtree segmentation together with a more effective codebook with the color local thresholding classifier for content-based image retrieval (CBIR) is proposed. The vector quantization (VQ) based image retrieval schemes have good performance, but the importance of color edge intensive blocks is neglected. Our proposed method has two main improvements. First, quadtree segmentation based on both hue and gray-level information is applied to classify the blocks into the homogeneous and high-detail ones. Second, a color local thresholding classifier is proposed to further classify the high-detail blocks based on edge information. Simulation results show that our proposed scheme outperforms the existing methods, including the Quadtree CVQ-based scheme, the VQ-based scheme, and other methods.
Discrete Gyrator Transforms: Computational Algorithms and Applications

As an extension of the 2D fractional Fourier transform (FRFT) and a special case of the 2D linear canonical transform (LCT), the gyrator transform was introduced to produce rotations in twisted space/spatial-frequency planes. It is a useful tool in optics, signal processing and image processing. In this paper, we develop discrete gyrator transforms (DGTs) based on the 2D LCT. Taking the advantage of the additivity property of the 2D LCT, we propose three kinds of DGTs, each of which is a cascade of low-complexity operators. These DGTs have different constraints, characteristics, and properties, and are realized by different computational algorithms. Besides, we propose a kind of DGT based on the eigenfunctions of the gyrator transform. This DGT is an orthonormal transform, and thus its comprehensive properties, especially the additivity property, make it more useful in many applications. We also develop an efficient computational algorithm to significantly reduce the complexity of this DGT. At the end, a brief review of some important applications of the DGTs is presented, including mode conversion, sampling and reconstruction, watermarking, and image encryption.
Real time accelerometer-based gait recognition using adaptive windowed wavelet transforms

This paper presents a real time gait recognition system using the wavelet transform. The activity signal is acquired from three-axis accelerometers on mobile phones. It is first decomposed into wavelet coefficients with eight levels. Several statistical measures, such as power, mean, variance, energy, and the energy of neighbor difference, are calculated from these coefficients. Furthermore, the adaptive window size is adopted to well fit the footstep of each person. The selected features are also adjusted adaptively to improve the accuracy. The simulation results show that the proposed method has reliable recognition accuracy both in the real-time and the long-term cases.
The generalized radial Hilbert transform and its applications to 2D edge detection (any direction or specified directions)

It is well-known that the Hilbert transform (HLT) is useful for generating analytic signals, and saving the bandwidth required, in communication. However, it is less known that the HLT is also a useful tool for edge detection. We introduce the generalized radiant Hilbert transform (GRHLT), and illustrate how to use it for edge detection. The GRHLT is the general form of the two-dimensional HLT. Together with some other techniques (such as section dividing and shorter impulse response modification), we can use the GRHLT to detect the edges of images exactly. The GRHLT used for edge detection has a higher capability for noise immunity than other edge detection algorithms. Besides, we can also use the GRHLT for directional edge detection, i.e., detecting edges with certain directions.
Scaled Lifting Scheme and Generalized Reversible Integer Transform

In this paper, we generalize the lifting scheme and the triangular matrix scheme. For the existing lifting scheme and the triangular matrix scheme, the entries on the diagonal line must be 1 or 2k. In this paper, we find that this constraint can be relaxed and the lifting or the triangular matrix is still reversible. Thus, the constraint that det(A) = 2L is not required and we can convert a matrix into a reversible integer transform without pre-scaling even when det(A) ne 2L. Moreover, the proposed scaled schemes are also helpful for improving the accuracy and reducing the implementation complexity.
2-D affine generalized fractional Fourier transform

The 2-D Fourier transform has been generalized into the 2-D separable fractional Fourier transform (replaces the 1-D Fourier transform by the l-D fractional Fourier transform for each variable) and the 2-D separable canonical transform (further replaces the fractional Fourier transform by canonical transform) of Sahin, Ozaktas and Mendlovic (see Appl. Opt., vol.37, no.11, p.2130-41, 1998). It also has been generalized into the 2-D unseparable fractional Fourier transform with 4 parameters of Sahin et al. ). In this paper, we introduce the 2-D affine generalized fractional Fourier transform (AGFFT). These 2-D transforms has been further generalized. We show it can deal with many problems that can not be dealt with by these 2-D transforms and extend their utility.
Eigenvalues and Singular Value Decompositions of Reduced Biquaternion Matrices

In this paper, the algorithms for calculating the eigenvalues, the eigenvectors, and the singular value decompositions (SVD) of a reduced biquaternion (RB) matrix are developed. We use the SVD to approximate an RB matrix in the least square sense and define the pseudoinverse matrix of an RB matrix. Moreover, the RB SVD is employed to implement the SVD of a color image. The computational complexity for the SVD of an RB matrix is only one-fourth of that for the SVD of conventional quaternion matrices. Therefore, many useful image-processing methods using the SVD can be extended to a color image without separating the color image into three channels. The numbers of the eigenvalues of an n times n RB matrix, the nth roots of an RB, and the zeros of an RB polynomial with degree n are all finite and equal to n2 , not infinite as those of conventional quaternions.
Approximation and design methods for efficient filters with less multiplication requirement

In consumer electronics, it is very important to design a filter with very less computation loading. In this paper, I try to approximate a filter by several geometric segments, geometric polynomial segments, or rectangular segments. With the approximation, the filter can be approximated by some IIR filter and implemented in a recursive way with very less multiplication requirement. I will introduce the way to design a filter whose number of multiplications is fixed to a small constant but the approximate error can be minimized. Several examples are given to show how to design the digital filter that can well approximate the original ideal filter but the number of real multiplications is much less.
Adaptive 3D cell segmentation and tracing algorithm using convex separation and histogram information for vivo images

In biology and medicine, it is critical to study the amount, distribution, movement, status, and behavior of cells. To do this, one should well segment cells from three dimensional (3D) cell images in prior and precisely extract the moving path of each cell. In this work, an advanced 3D cell segmentation and tracing algorithm is proposed. It applies local histograms to perform adaptive thresholding. Moreover, based on the observation that a cell is always nearly convex, the technique of shortest path segmentation is applied to disconnect the over-merged cells. Furthermore, the overlapping cell areas between two adjacent layers are adopted to refine segmentation results. Then, the moving path of a cell can be traced by determining the cell with the shortest distance and the most similar characteristics in the next 3D image. With the proposed algorithm, 3D cell images can be accurately segmented and each cell can be well traced.
Structural similarity-based nonlocal edge-directed image interpolation

Image interpolation is important for computer vision. Most of the existing image interpolation methods are based on the optimization in the mean square error (MSE) sense. In this paper, we incorporate the structural similarity (SSIM) based metric into the framework of the nonlocal edge-directed image interpolation (NLEDI) method. In the proposed algorithm, a missing pixel is interpolated using the weighted average of neighboring patches where the weights are determined by the SSIM-based metric instead of the MSE measurement. Simulations show that our proposed structural similarity-based NLEDI (SSNLEDI) scheme outperforms existing image interpolation methods and has higher PSNR values and better visual qualities.
Relations between fractional operations and time-frequency distributions, and their applications

The fractional Fourier transform (FRFT) is a useful tool for signal processing. It is the generalization of the Fourier transform. Many fractional operations, such as fractional convolution, fractional correlation, and the fractional Hilbert transform, are defined from it. In fact, the FRFT can be further generalized into the linear canonical transform (LCT), and we can also use the LCT to define several canonical operations. In this paper, we discuss the relations between the operations described above and some important time-frequency distributions (TFDs), such as the Wigner distribution function (WDF), the ambiguity function (AF), the signal correlation function, and the spectrum correlation function. First, we systematically review the previous works in brief. Then, some new relations are derived and listed in tables. Then, we use these relations to analyze the applications of the FRPT/LCT to fractional/canonical filter design, fractional/canonical Hilbert transform, beam shaping, and then we analyze the phase-amplitude problems of the FRFT/LCT. For phase-amplitude problems, we find, as with the original Fourier transform, that in most cases, the phase is more important than the amplitude for the FRFT/LCT. We also use the WDF to explain why fractional/canonical convolution can be used for space-variant pattern recognition.
Conjugate Symmetric Discrete Orthogonal Transform

A new conjugate symmetric discrete orthogonal transform (CS-DOT)-generating method is proposed. The spectra of the CS-DOT for real input signals are conjugate symmetric so that we only need half memory size to store data. Meanwhile, the proposed CS-DOT also has a radix-2 fast algorithm so that it is suitable for hardware implementation. The CS-DOT generalized the existing transforms such that the discrete Fourier transform (DFT) and the conjugate symmetric sequency-ordered complex Hadamard transform (CS-SCHT) are special cases of the CS-DOT. The CS-DOT-generating method is more systematic and generalized than that of the original CS-SCHT. We can use the same implementation structure but only adjust the twiddle factors to construct the CS-SCHT and DFT so that it is easy to switch the behaviors between these transforms.
A novel compression algorithm for IMFs of Hilbert-Huang transform

The intrinsic mode function (IMF) derived from empirical mode decomposition (EMD) of the Hilbert-Huang transform process is useful for time-variant signal analysis. In this paper, a novel algorithm for IMF compression is proposed. Instead of recording all of the extreme points, we just encode a part of the extreme points while the locations and amplitudes of the other extreme points are predicted. Moreover, the residue of interpolation is encoded by the prediction technique and the adaptive arithmetic coding scheme. Simulations show that the proposed algorithm is efficient for encoding the IMF and very effective for compressing vocal signals and biomedical signals.
Integer multichannel transform

In this paper, we develop an algorithm that can convert any reversible multichannel system into a reversible integer multichannel transform. The integer transform means the operation whose inputs and outputs are all sums of powers of two. Recently, the triangular matrix scheme was shown to be able to convert any nonsingular discrete transform into a reversible discrete integer transform. Since as other discrete transform, the multichannel system can also be expressed as a matrix form, we suggest that the triangular matrix scheme can also be applied for converting a multichannel system into a reversible integer transform. The proposed methods are useful for multirate signal processing, wavelet analysis, communication, and image processing.
Fractional Fourier Transform, Wigner Distribution, and Filter Design for Stationary and Nonstationary Random Processes

In this paper, we derive the relationship among the fractional Fourier transform (FRFT), the linear canonical transform (LCT), and the stationary and nonstationary random processes. We find many interesting properties. For example, if we perform the FRFT for a stationary process, although the result is no longer stationary, the amplitude of the autocorrelation function is still independent of time. We also find that the LCT of a white noise is still a white one. For the FRFT of a stationary process, the ambiguity function (AF) is a tilted line and the Wigner distribution function (WDF) is invariant along a certain direction. We also define the “fractional stationary random process” and find that a nonstationary random process can be expressed by a summation of fractional stationary random processes. In addition, after performing the filter designed in the FRFT domain for a white noise, we can use the segment length of the ω -axis on the WDF plane to estimate the power of the noise and use the area circled by cutoff lines to estimate its energy. Thus, in communication, to reduce the effect of the white noise, the “area” of the WDF of the transmitted signal should be as small as possible.
Efficient image deblurring via blockwise non-blind deconvolution algorithm

Image deblurring aims to restore the quality of degraded images and plays an important role in many multimedia applications. Implementing a deblurring algorithm directly, however, is usually computationally expensive, especially for high-resolution images. In this paper, we propose to first partition the image in blocks (sub-images) and then implement the deblurring algorithm on a block basis for deblurring high-resolution or large-dimension images. Overlap regions among blocks are linearly filtered to reduce the distortion at the edge due to the block-based implementation. Simulations demonstrate that the proposed method achieves satisfactory result with much less computation time.
Disparity estimation using adaptive superpixel assignment and gradient weight, binary cost, and dilation based local matching algorithm

Disparity estimation is important for depth information retrieval, stereoscopic image construction, and light filed image rendering. To develop a high accurate disparity estimation algorithm, in this work, several techniques are adopted. First, unlike existing methods, which adopt feature point matching, n by n block matching, or global matching, we apply superpixels together with an automatic adjusting mechanism to customize the suitable number, size, and shape of windows for each part of an image. Second, since edge should play a more important role for matching, a larger weight is assigned to the pixel with high gradient. Moreover, the dilation operation is applied to consider the surrounding information and the binary cost function is used to achieve an even better result. Simulations show that the proposed algorithm much outperforms existing methods for disparity matching. It can work very well even when an image contains many occluded patterns or tiny objects.