nils-herrmann/clean_scirepeval huggingface dataset:
PCA modeling of the L-shell Copper X-pinch plasma produced by the compact generator of ecole polytechnique

Principal Component Analysis is applied and compared with the line ratios of special Ne-like transitions for investigating the electron beam effects on the L-shell Cu synthetic spectra<sup>1</sup>. The database for the principal component extraction is created over a non-LTE collisional radiative L-shell Copper model<sup>2</sup>. The extracted principal components are used as a database for Artificial Neural Network in order to estimate the plasma electron temperature, density and beam fractions from a representative time integrated spatially resolved L-Shell Cu X-pinch plasma spectrum<sup>3,4</sup>. The spectrum is produced by the explosion of 25-μm Cu wires on a compact LC (40 kV, 200 kA, 200 ns) generator<sup>5</sup>. The modeled plasma electron temperatures are about T<sub>e</sub>~125 eV and N<sub>e</sub>=7×10<sup>19</sup> cm<sup>-3</sup> in the presence of the fraction of the beams with f~0.01 and centered energy of ~10 keV.
Gabor wavelet transform based facial expression recognition using PCA and LBP

This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis (PCA) and Local binary pattern (LBP) algorithms. Experiments were carried out of using Japanese female facial expression (JAFFE) database. In all experiments conducted using JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with an average recognition rate of 90% under the same experimental setting.
Discrete wavelet transform-based Ant Colony Optimization for edge detection

Ant Colony Optimization (ACO) is used to obtain the edges of an image which is acquired from sampling and quantization of a continuous image. Such techniques generate a pheromone matrix that represents the edge information at each pixel position on the routes formed by ants dispatched on the image. However, when the image is buried in noise, ACO performance deteriorates. In this paper, we propose to use discrete wavelet transform (OWT) as a preprocessing step with ACO to enhance image edge detection. The proposed algorithm creates a pheromone matrix to stand for the edges of the low frequency component obtained from the OWT decompositions, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of ants are driven by the local variation of the image's intensity values. The proposed approach shows a significant performance and capability of detecting edges superior to existing techniques.
Weighted majority voting for face recognition from low resolution video sequences

In this paper a new system for recognizing faces from video sequences using weighted majority voting (WMV) method is proposed. In the training phase, the system uses principle component analysis (PCA) based single eigenspace generated by sequences of faces of all subjects with the same resolution. For the testing phase, the system employs several preprocessing tasks whereby for all subjects' videos, the face images with varying resolutions in different frames are automatically extracted, histogram equalized to alleviate the effects of changing illumination, and upsampled to the resolution of the eigenfaces. For the recognition phase, each recognized subject is assigned a weight based on a measure of information capacity of each tested frame. Finally the subject with highest cumulative weight, through the video sequence is declared to be the recognized person. The proposed WMV system is robust to scale changes and effectively addresses the problem of recognition from low resolution video sequences.
Dual-tree and single-tree complex wavelet transform based face recognition

In this paper we construct complex approximately analytic wavelets in the single and dual tree context, which posses Gabor like characteristics. We investigate the recently developed dual tree complex wavelet transform (DT-CWT) and the single tree wavelet transform (ST-CWT) for the face recognition problem. Experiments are carried out on FERET and ORL databases. The resulting complex wavelet based feature vectors are as discriminating as the Gabor wavelets derived features and at the same time are of lower dimension when compared with that of Gabor wavelets. In all experiments, complex wavelets equaled or surpassed the performance of Gabor wavelets in recognition rate when equal number of orientations and scales are used. Obtained results indicate that complex wavelets can provide a successful alternative to Gabor wavelets for face recognition.
PCA-based face recognition from video using super-resolution

In this paper, we propose a method to recognize faces from a set of consecutive video frames instead of a single image using super-resolution (SR). The SR process uses multiple frames acquired from video and combines information coming from them into a single image in higher resolution. As expected, a single low resolution image would contain less amount of information, than the same image taken from a video sequence with multiple other images with temporal changes from consecutive frames. the proposed method uses SR to generate a super resolved video sequences from a low resolution video sequences and uses frames acquired from the high resolution video sequences to train and test the performance of the principal component analysis based face recognition system. Entropy and MSE were used to check the performance of the system and the results showed the robustness of SR as a preprocessing step for recognition.
Face recognition using SIFT descriptors extracted from multiresolution images

In this work, we developped a technique for face recognition using the idea of multiresolution face recognition. The multiresolution subbands are generated by using discrete wavelet transform (DWT). We then apply scale invariant feature transform (SIFT) to extract the salient feature descriptors at each subband using the resulting low frequency subband of the image. The descriptors are used to perform the recognition of the faces in each subband with different resolutions. Then decisions comming from each subband are combined by using simple majority voting to increase the recognition performance. Proposed, multiresolution SIFT approach shows promising results and outperforms the conventional SIFT approaches.
Compressive sensing based facial expression recognition

In this paper a new approach is proposed. The new approach is inspired by the compressive sensing theory used for data compression. Feature vectors from the facial data were extracted using compressive sensing technique. The compressive measurements were obtained at different feature vector lengths. Comparison of recognition rates with Local Binary Pattern (LBP) feature extractor were made using different classifiers. The results obtained by using three distance measures and Support Vector Machines (SVM) validate the performance of our proposed approach as it outperforms the best result obtained using LBP algorithms at different combinations of cells and bins numbers. Moreover, performance comparison using Sparse Representation Classifier (SRC) shows that LBP can only matches the proposed method performance with a very large feature vectors size which is computationally expensive for SRC.
Illumination invariant face recognition system

One of the main issues that degrades the performance of a face recognition system is the illumination problem. In this paper, we investigated different algorithms as a preprocessing step to overcome the illumination problem. Histogram equalization, discrete cosine transforms and steerable Gaussian filters were applied to face images from Yale face database B for illumination normalization. PCA algorithm was used as a feature extractor. Using this preprocessing step the performance of the system shows significant improvements.
Face Recognition using Dual-Tree Wavelet Transform

This paper introduces a face recognition method based on the Dual-Tree Complex Wavelet Transform (DT-CWT), which is used to extract features from face images. DT-CWT uses similar kernels with Gabor wavelets and is a computationally cheaper way of extracting Gabor-like features. Principal Component Analysis (PCA) which is a linear dimensionality reduction technique, that attempts to represent data in lower dimensions, is used to perform the face recognition. The results demonstrate that using DT-CWT in the preprocessing phase and then applying PCA on the features extracted from the DT-CWT instead of raw face images, improves the recognition performance.
Character recognition using correlation & hamming distance

In this paper, character recognition is carried out using template matching scheme using correlation. In our approach, in addition to correlation, the hamming distance was applied in the scenarios where the correlation fails. In this approach, text image is segmented into lines and then characters by basic pre-processing techniques. After that, the character image is classified and converted into text by template matching scheme using either correlation or hamming distance. For test stage, four text images were created with total of 560 characters. The system works successfully with average recognition rates of 72.39% and 94.90% for correlation only and correlation plus hamming distance, respectively.
Co-occurrence based statistical approach for face recognition

This paper introduces a new face recognition method based on the gray-level co-occurrence matrix (GLCM). Both distributions of the intensities and information about relative position of neighbourhood pixels are carried by GLCM. Two methods have been used to extract feature vectors from the GLCM for face classification. The first, method extracts the well-known Haralick features to form the feature vector, where the second method directly uses GLCM by converting the matrix into a vector which can be used as a feature vector for the classification process. The results demonstrate that using the GLCM directly as the feature vector in the recognition process outperforms the feature vector containing the statistical Haralick features. Additionally, the proposed GLCM based face recognition system outperforms the well-known techniques such as principal component analysis and linear discriminant analysis.
A multi-resolution approach for edge detection using Ant Colony Optimization

In this paper we aim at providing a robust and more compact approach for detecting edges compared to the traditional edge detection algorithms like Roberts, Sobel, Prewitt and evolutionary-inspired Ant Colony Optimization (ACO) techniques. In this proposed approach, an ACO is used alongside Dual-Tree Complex Wavelet Transform (DT-CWT) to detect and emphasize edges that would have been difficult to obtain with directly applying ACO or conventional edge detection algorithms. Initially the image is decomposed using DT-CWT to obtain the oriented wavelets and approximation versions of the original image. ACO is applied to each of the decomposed images and then image is reconstructed to get the processed image with the detected edges. The results obtained reveal superior, more detailed and emphasized edges than directly applying ACO or other conventional techniques. The proposed approach is also capable of identifying edges in slightly varying intensity regions.
Facial expression recognition using Support Vector Machines

This paper propose a facial expression recognition approach based on Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. Experiments were carried out on the Japanese Female Facial Expression (JAFFE) database and our recently introduced Mevlana University Facial Expression (MUFE) database. Support Vector Machine (SVM) was used as classifier. In all conducted experiments on JAFFE and MUFE databases, obtained results reveal that PCA+SVM has an average recognition rate of 87% and 77%, respectively.
A MAP IIR filter for 3D ultrasound

Bayesian reconstruction methods are slow and have been avoided in 3D ultrasound, since fast algorithms are required. The paper tries to overcome this difficulty by presenting a fast reconstruction algorithm designed in a Bayesian framework, based on data dependent IIR filtering. Thus the benefits of Bayesian volume reconstruction and the computational efficiency of IIR recursive filtering may be obtained. The proposed filter has space varying coefficients which depend on the observed data in each region. The proposed method is evaluated using synthetic and real data. The results are compared with the MAP estimates obtained using a nonlinear optimization method. The proposed IIR recursive filtering algorithm achieves comparable results with the MAP algorithm and is twenty five times faster.
Liver tumor assessment with DCE-MRI

Dynamic-contrast enhanced MRI (DCE-MRI) is used in clinical practice to assess liver tumor malignancy. An algorithm to get information for automatic classification of tumors is presented. The Maximum value and WashIn and WashOut rates, obtained from the perfusion curves measured from the DCE-MRI images, are used in the classification process. The perfusion curves are described by a linear discrete pharmacokinetic (PK) model, based on multi-compartment paradigm where the input is the bolus injection. The arterial input function (AIF) that is usually estimated in the closest artery is assumed here to be the response of a second order linear system to the bolus injection. Therefore, the complete chain is modeled as a third order system with a single zero. The alignment procedure is performed by using the Mutual Information (MI) criterion with a non-rigid transformation to compensate the displacements occurred during the acquisition process. It is shown that the Maximum values and the WashIn and WashOut rates of the perfusion curves in malignant tumors are higher than in healthy tissues. This fact is used to classify them. Furthermore, it is also shown, that inside the tumor, the parameters associated with the perfusion curves for each pixel (time courses) present a higher variance than in the healthy tissues, which may also be used to increase the accuracy of the classifier. Examples using real data are presented.
Assessment of Obstructive Sleep Apnea Syndrome by spectral analysis of physiological parameters

Obstructive Sleep Apnea Syndrome (OSAS) is a very common sleep disorder that is associated with cardiovascular and neurocognitive impairments. The present study aims to assess the electroencephalographic (EEG) power before, during and after obstructive apnea episodes, in four frequency bands: delta (δ), theta (θ), alpha α and beta (β). Furthermore, Autonomic Nervous System (ANS) activity was assessed through the spectral analysis of heart rate variability (HRV) components in the three mentioned periods. For that purpose, continuous wavelet transform was applied to the EEG and RR (obtained from the ECG signal) signals. A new approach was carried out for assessing EEG power changes during an OSA episode: topographic EEG brain mapping. This technique was found to be a powerful tool to better visualize spectral changes during OSA episodes across the whole brain. The results demonstrate that there is a significant decrease in the EEG δ power during OSA, that is not totally recovered immediately after the episode. Since δ waves are linked to learning and synaptic plasticity processes, it is hypothesized that decreased δ power during OSA may contribute to the cognitive deterioration in patients with OSAS. The HRV analysis results show the presence of a pattern during OSA episodes, which is related to an impaired autonomic control of the heart in these patients.
Classifier-assisted metric for chromosome pairing

Cytogenetics plays a central role in the detection of chromosomal abnormalities and in the diagnosis of genetic diseases. A karyogram is an image representation of human chromosomes arranged in order of decreasing size and paired in 23 classes. In this paper we propose an approach to automatically pair the chromosomes into a karyogram, using the information obtained in a rough SVM-based classification step, to help the pairing process mainly based on similarity metrics between the chromosomes. Using a set of geometric and band pattern features extracted from the chromosome images, the algorithm is formulated on a Bayesian framework, combining the similarity metric with the results from the classifier. The solution is obtained solving a mixed integer program. Two datasets with contrasting quality levels and 836 chromosomes each were used to test and validate the algorithm. Relevant improvements with respect to the algorithm described by the authors in  were obtained with average paring rates above 92%, close to the rates obtained by human operators.
Fluorescence Confocal Microscopy Imaging denoising with photobleaching

The Fluorescence Confocal Microscopy (FCM) is nowadays one of the most important tools in biomedical and pharmaceutic research. The main advantage of this technique over the traditional bright field optical microscopy is the fact that it allows the selection of a thin cross-section of the sample by rejecting the visual information coming from the out-of-focus planes. However, the small amount of energy radiated by the fluorophore and the huge light amplification performed by the photon detector to capture this visual information introduces a type of multiplicative noise described by a Poisson distribution. Additionally, the radiation efficiency of the fluorophore decreases with the time, an effect called photobleaching, leading to a decrease in the image intensity along the time.
Plaque Tissue Characterization and Classification in Ultrasound Carotid Scans: A Paradigm for Vascular Feature Amalgamation

The selection of carotid atherosclerosis patients for surgery or stenting is a crucial task in atherosclerosis disease management. In order to select only those symptomatic cases who need surgery, we have, in this work, presented a computer-aided diagnostic technique to effectively classify symptomatic and asymptomatic plaques from B-mode ultrasound carotid images. We extracted several grayscale features that quantify the textural differences inherent in the manually delineated plaque regions and selected the most significant among these extracted features. These features, along with the degree of stenosis (DoS), were used to train and test a support vector machine (SVM) classifier using threefold stratified cross-validation using a data set consisting of 160 (50 symptomatic and 110 asymptomatic) images. Using 32 features in an SVM classifier with a polynomial kernel of order 1, we obtained the best accuracy of 90.66%, sensitivity of 83.33%, and specificity of 95.39%. The DoS was found to be a valuable feature in addition to other texture-based features. We have also proposed the plaque risk index (PRI) made up of a combination of significant features such that the PRI has unique ranges for both plaque classes. PRI can be used in monitoring the variations in features over a period of time which will provide evidence on how and which features change as asymptomatic plaques become symptomatic.
Comparison of two different approaches for brain activity detection in fMRI: SPM-MAP and SPM-GLM

The functional MRI {Magnetic Resonance Imaging), fMRI, is today a widespread tool to study and evaluate the brain from a functional point of view. The blood-oxygenation-level-dependent (BOLD) signal is currently used to detect the activation of brain regions with a stimulus application, e.g., visual or auditive. In a block design approach the stimuli (called paradigm in the fMRI scope) are designed to detect activated and non activated brain regions with maximized certainty. However, corrupting noise in MRI volumes acquisition, patient motion and the normal brain activity interference makes this detection a difficult task. The most used activation detection fMRI algorithm, here called SPM-GLM  uses a conventional statistical inference methodology based on the t-statistics In this paper we propose a new Bayesian approach, by modeling the data acquisition noise as additive white Gaussian noise (AWGN) and the activation indicators as binary unknowns that must be estimated. Monte Carlo tests using both methods have shown that the Bayesian method, here called SPM-MAP, outperforms the traditional one, here called SPM-GLM, for almost all conditions of noise and number of paradigm epochs tested.
Automatic liver tumor diagnosis with Dynamic-Contrast Enhanced MRI

Dynamic-contrast enhanced MRI is currently used as a complementary diagnosis tool to assess the malignancy of the liver tumor, called hepatoma or adult primary liver cancer. This paper proposes a set of features and computation methods to extract them in order to design a classifier for automatic diagnosis of the hepatoma. A dynamic discrete linear pharmacokinetic model is used to estimate the perfusion curves from the noisy observations, based on the multi-compartment paradigm. The arterial response to the contrast agent bolus injection, called arterial input function, is also estimated since no arteries are available in the neighborhood of the tumor. The compensation of involuntary movements of the patient, such as the respiratory activity, during the acquisition process is performed using a mutual information based registration algorithm with non-rigid transformations. The tumor is isolated in a small region of interest in order to speed up the analysis and the tumor itself is segmented using an active contour algorithm. The classification of the tumor can be based on the mean and variance values of the Maximum, Washln and WashOut rates of the perfusion curves inside the tumor as it is shown that these rates are adequate discriminative features to automatically classify the tumors with respect to its malignancy. Tests using real data are used to illustrate the ability of using these features to classify the tumor with respect to its malignancy.
fMRI binary detection of brain activated regions with graph-cuts

The functional Magnetic Resonance Imaging (fMRI) is a technique with increasing applications in studying the brain function. The blood-oxygenation-level-dependent (BOLD) is a fMRI method that allows the detection of brain activated regions after the application of an external stimulus, e.g., visual or auditive. This technique is based on the assumption that the metabolism increases in activated areas as well as the oxygen uptake. Analising this information is a challenging problem because the BOLD signal is very noisy and its changes due to the application of a stimulus are very weak. Therefore, the detection of temporal correlations with the applied stimulus requires sophisticated statistical algorithms to understand if the changes on the BOLD signal are pure noise or are related with the applied stimulus, called paradigm in the fMRI scope.
Headset Bluetooth and cell phone based continuous central body temperature measurement system

The accurate measure of the central temperature is a very important physiologic indicator in several clinical applications, namely, in the characterization and diagnosis of sleep disorders. In this paper a simple system is described to continuously measure the body temperature at the ear. An electronic temperature sensor is coupled to the microphone of a common commercial auricular Bluetooth device that sends the temperature measurements to a mobile phone to which is paired. The measurements are stored at the mobile phone and periodically sent to a medical facility by email or SMS (short messaging service).