nils-herrmann/clean_scirepeval huggingface dataset:
The non degenerate source condition: Support robustness for discrete and continuous sparse deconvolution

In this paper, we study discrete and continuous versions of the LASSO in order to solve the deconvolution problem. We shed light on the Non Degenerate Source Condition, a property which yields support robustness for both the continuous and discrete problems. More precisely, we show that this property yields exact support recovery in the continuous case and the estimation of twice the number of spikes in the discrete case.
Image compression with anisotropic triangulations

We propose a new image compression method based on geodesic Delaunay triangulations. Triangulations are generated by a progressive geodesic meshing algorithm which exploits the anisotropy of images through a farthest point sampling strategy. This seeding is performed according to anisotropic geodesic distances which force the anisotropic Delaunay triangles to follow the geometry of the image. Geodesic computations are performed using a Riemannian Fast Marching, which recursively updates the geodesic distance to the seed points. A linear spline approximation on this triangulation allows to approximate faithfully sharp edges and directional features in images. The compression is achieved by coding both the coefficients of the spline approximation and the deviation of the geodesic triangulation from an Euclidean Delaunay triangulation. Numerical results show that taking into account the anisotropy improves the approximation by isotropic triangulations of complex images. The resulting encoder competes well with wavelet-based encoder such as JPEG-2000 on geometric images.
Unbiased risk estimation for sparse analysis regularization

In this paper, we propose a rigorous derivation of the expression of the projected Generalized Stein Unbiased Risk Estimator (GSURE) for the estimation of the (projected) risk associated to regularized ill-posed linear inverse problems using sparsity-promoting ℓ1 penalty. The projected GSURE is an unbiased estimator of the recovery risk on the vector projected on the orthogonal of the degradation operator kernel. Our framework can handle many well-known regularizations including sparse synthesis- (e.g. wavelet) and analysis-type priors (e.g. total variation). A distinctive novelty of this work is that, unlike previously proposed ℓ1 risk estimators, we have a closed-form expression that can be implemented efficiently once the solution of the inverse problem is computed. To support our claims, numerical examples on ill-posed inverse problems with analysis and synthesis regularizations are reported where our GSURE estimates are used to tune the regularization parameter.
A Review of Adaptive Image Representations

Improving the modeling of natural images is important to go beyond the state-of-the-art for many image processing tasks such as compression, denoising, inverse problems, and texture synthesis. Natural images are composed of intricate patterns such as regular areas, edges, junctions, oriented oscillations, and textures. Processing efficiently such a wide range of regularities requires methods that are adaptive to the geometry of the image. This adaptivity can be achieved using sparse representations in a redundant dictionary. The geometric adaptivity is important to search for efficient representations in a structured dictionary. Another way to capture this geometry is through non-local interactions between patches in the image. The resulting non-local energies can be used to perform an adaptive image restoration. This paper reviews these emerging technics and shows the interplay between sparse and non-local regularizations.
The classification system with the evolutionary computation

The purpose of this paper is to obtain the object. In particular, we focus on the face detection. By using the proposed method, we recognize faces with a near-infrared (NIR) camera. The face detection that used images from the NIR camera is comparatively difficult to be done, because they are gray scale images. In this paper, the filter by using the GA is designed for the purpose of getting the faces. The method of detecting faces and the position from the NIR images is proposed in the on-line image. It is demonstrated that our approach is effective for vehicle driver monitoring. Finally, it was confirmed that the proposed method works well.
Head pose tracking with individual adjustment for communication tools

In this paper, we propose a novel method to automatically estimate diffusion parameters within particle filter for efficient head state tracking. Recently, many head pose and facial expression tracking methods have been developed. However, most of the conventional methods are not designed as communication tools. We therefore propose a new head state tracking method which can be applied to various applications such as avatar's facial expression control. The proposed method optimizes the diffusion parameters found within particle filter which is utilized for tracking via the maximum-likelihood approach. Moreover, we propose an update number function that is utilized for incrementally updating the data set. Although the diffusion parameters vary according to the head motion, accurate tracking with the suitable parameters can be realized.
Proposal for the Extraction Method of Personal Comfort and Preference by the EEG Maps

Recently, many researches of the extraction of personal comfort and preference have been studied. Personal comfort and preference depend on a person, therefore the index is needed for the measurement. Then, we aim to understand personal comfort and preference by using the EEG as an index. The purpose of this study is to make the EEG map that makes visual the relationship between the EEG and external stimuli. We think that analyzing the distribution in the EEG map leads to the extraction of personal preference. First of all, we measure EEG patterns in 3 conditions; neutral which is the state relaxing without external stimuli, listening to favorite music, and smelling favorite fragrance. Then, we make the data matrix from the obtain data. Then, we extract features from the matrix by using neural networks. Finally, we make the EEG map to clarify the location of each EEG feature. In order to show the effectiveness of the proposed method, we demonstrate the simulation examples
Pheromone-based Kohonen Self-Organizing Map (PKSOM) in clustering of tropical wood species: Performance and scalability

Clustering is proposed to cluster the high dimensional data, into clusters of data that exhibit some similarities. Due to this ability, it has been chosen to solve many problems in various areas, including tropical wood species classification. It has ease the recognition process which has been done manually before. Pheromone-based Kohonen Self-Organizing Map (PKSOM) is proposed a clustering tool to cluster the wood datasets; the filtered and raw datasets. This paper discusses the performance and scalability of modified Kohonen Self-Organizing Map (KSOM), named Pheromone-based KSOM (PKSOM). The PKSOM algorithm is trained and tested using two types of wood datasets; the raw wood dataset and filtered dataset using Genetic Algorithm (GA). The results are then being compared with two conventional clustering methods; KSOM and standard K-Mean. As a conclusion, PKSOM has produced 97.5% of accuracy for the raw wood dataset and 97.55% for the filtered wood dataset, slightly higher compared to the other two methods.
Presentation of an SMR-based BCI using auditory feedback based on pitch

Brain-Computer Interfaces (BCIs) aim to translate the users cerebral activity into a control signal, allowing him to interact with an external device without the use of conventional muscled-based pathways. These alternative communication pathways have already been employed under various forms to allow severely handicapped people to use assistive devices. Given that most of currently developed BCIs only provide a visual feedback to the user, the aim of this study was to observe the result provided by an auditory feedback based on pitch. The BCI developed to this end classifies two different commands using sensorimotor rhythm (SMR) frequency features. The experiment consisted in sessions of trials during which the subjects were asked to perform motor imagery or to rest. Except for the first session, the system provided the subjects with a realtime feedback based on the learning data of the previous sessions. This feedback was generated by a control signal resulting of the classification of the extracted features by a Support Vector Regression (SVR) algorithm. Half of the 16 subjects were given visual feedback, consisting in a cursor movement, and the other half received auditory feedback, consisting in a pitch variation. Although the performance of the visual feedback BCI was superior during the first sessions, the final third session classification rates were 69.7 and 70.3% for respectively visual and auditory conditioning. These results tend to show that there might be no significant difference between the two types of feedback after long enough training. Moreover, this study also demonstrate the usability of a SVR classifier.
Blind image restoration method by PCA-based subspace generation

Principal Component Analysis (PCA) has been effectively applied for image restoration. Original idea underlying PCA approach has two different roots. One is from the fact that PCA is relevant to variance of pixel intensity by which the missing high frequency components in blurred image should be recovered. The other comes from the idea of source separation based on PCA. In the light of PCA approach we have proposed an image restoration algorithm which contains the following three novel aspects: iterative application of PCA, Gaussian smoothing filtering for image ensemble creation, and no-reference image quality index for iteration number management. This paper aims to investigate and propose a non-iterative PCA-based image restoration with some generalizations. First, through conducted experiments the variance of Gaussian filters as well as the number of created images by them are appropriately determined. Second, weights are introduced to the principal component images. Finally, optimal weights are determined by maximizing the image quality index with no reference. Experimental results by the proposed method provide higher PSNR than the previous iterative PCA approach.
A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state

This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the human's nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A k-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the human's nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.
Blind restoration of single-channel image using iterative PCA

This paper proposes a single-channel image blind restoration by using iterative principal components analysis (PCA). Previously we proposed the iterative PCA approaches for blind restoration and proved its superiority over conventional methods. Still, there are some problems to be solved. One of them is precise and automatic way to determine the iteration number. This study tries to solve this by applying a blind image quality assessment for automatic optimization of the iterative number. For a verification example of atmospheric turbulence-degraded imagery our proposed method provides better improved restoration quality than conventional methods. In addition, experiments of simulations are conducted for real images. From the results, we can confirm that the proposed method gives higher PSNR as well as SSIM than the conventional methods even in real environments.
Analysis of the EEG during the sleeping by the rhythmic component extraction

In this experiment, we record the EEG data during the sleeping and the awakening for the drowsiness cognition. As is well known, analyzing the frequency components of the EEG is important for the sleeping. There are many studies to analyze the EEG frequency data for recognizing the sleeping quality, and so on. However, there is no established method for the analysis of the sleeping EEG. From these reason, we are going to apply the rhythmic component extraction (RCE), proposed by Tanaka et al., to extract the rhythmic component in the brain. RCE finds a component extracted by using a weighted sum of observed channel signals. The weights are optimized by maximizing the power in a certain frequency range of interest. In the experiment, the subjects lie back, relax in a chair, and sleep. We take the EEG recording at 30 channels. From the results, the EEG features, such as the alpha and the theta wave, are different between the sleeping and the waking. These rhythmic components are extracted by the Fourier transform and the RCE. By using the RCE, we confirmed the alpha wave and the theta wave when they are not extracted in a single channel signal. Furthermore, we confirmed the effective measurement locations by analyzing the RCE weights.
Automatic extraction of a kidney region by using the Q-learning

At present, owing to an aging population and Western-style food, the number of kidney disease patients in Japan is increasing. It is difficult for people to recover fully from kidney disease. Early detection of a kidney disease is therefore needed. But diagnosis based on CT images has faults that are time-consuming and a great labor is required since the quantity of CT images is huge. We propose a method that automatically extracts the kidney region as a preprocessing of kidney failure detection. The kidney region is detected based on contour information that is extracted from the CT image using a dynamic gray scale value refinement method by Q-learning. It is demonstrated that the proposed method can stably detect the kidney from CT images of any patients.
Analytical methods for web form usability

In this study, we introduce analytical methods for Web form usability. Since web forms are widely prevalent in society, one of key business challenges has been to create a Web form with user satisfaction. Hence, we investigate users' feelings and their behavior using a questionnaire, eye tracking, and an electroencephalogram (EEG) during the completion of a Japanese web form. Moreover, we quantify and visualize data obtained from these three ways, which helps us to promote the understanding of web form usability. In addition, we quantify and visualize web form features such as color and characters. As a result, we were able to extract essential information on usability, which made it possible to suggest a web form that users can complete more easily. Thus, we have developed analytical methods to comprehend web form usability.
A feature extraction method in face image for personal identification

Recently in the world, many researches of personal identification method using biometrics are widely done. Especially, personal identification using the face is used because of needless for physical contact. In this paper, we propose the new method to identify the personal using the SPCA and the NN. The SPCA has been proposed in order for the PCS to achieve high-speed processing. Moreover, the personal identification system needs to have not only ability to recognize registrant correctly but also ability to reject un-registrant certainly. Therefore, we perform simulation using un-registrant. Furthermore, we analyzed and examined about the individual feature in a face by using the GA and the SPCA. Then, in order to show the effectiveness of the proposed method, we show computer simulations by suing the real image. From these results, we show the effectiveness.
Automatic decision method of parameters in the maximum distance algorithm

The maximum distance algorithm has been considered to be effective for an image segmentation of color scenery images. However, in the maximum distance algorithm, the parameter which decides the end term of the clustering is set in advance. The applicable value of this parameter depends on the individual image. Therefore, we propose the automated adjustment method of the maximum distance algorithm's parameter for the image segmentation of scenery images. First, "image density" is defined as the measure to evaluate the complexity of each image. Image density is calculated by difference between average of color value and color value of each pixel. Then, the relation of the image density and applicable value of the maximum distance algorithm is investigated. This investigation enables us the automated adjustment method of maximum distance algorithm's parameter fitting the image density of individual image. In this paper, the computer simulation is done for the purpose of comparing the conventional method and proposed method. There is the regulation between appropriate parameter in maximum distance algorithm. The experiment with about 100 images shows the effectiveness of the proposed method.
Intelligent medical diagnosis system using the fuzzy and neural networks

Various imaging diagnostic technologies are studied and used in practical. It is necessary to develop the automatic diagnosing processing system for detecting the internal organ. In Japan, cardiac disease is one of the most common cause of death. Therefore, it is necessary to measure cardiac function quantitatively and evaluate the motions of continuous cardiac muscle. Moreover, we propose the developing the system to detect the asynergy in the left ventricle. The processing images are X-ray photograms of the left ventricle by cardiac catheterization. In this paper, we propose the detection system of the asynergy in the left ventricle by using neural networks and the fuzzy inference. Furthermore, in order to show the effectiveness of the proposed method, we show the simulation example by using the real data.
Single image Super Resolution by no-reference image quality index optimization in PCA subspace

Principal Component Analysis (PCA) has been effectively applied for solving atmospheric-turbulence degraded images. PCA-based approaches improve the image quality by adding high-frequency components extracted using PCA to the blurred image. The PCA-based restoration process is similar with conventional single-frame Super-Resolution (SR) methods, which perform SR process by improving the edges portion of low-resolution images. This paper aims to introduce PCA-based restoration to solve SR problem with additive white Gaussian noise. We conducted experiments using standard image database and show comparative result with the latest deep-learning SR approach.
Optimising the performance of soft computing agents for classification of unstained mammalian cell images

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. In this paper we describe a multi-agent distributed control system model for image processing of unstained human (mammalian) cell images. The control system model develops a symbiotic relationship between soft computing agents like neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.
Mean shift based direction finding and separation of multiple sources using microphone array in the presence of spatial aliasing

This paper proposes a mean shift based method of blind source separation as well as source direction estimation through time-frequency (T-F) decomposition. The basic cue of T-F cell adopted here is the wave propagation vector and the method is applicable to arbitrary array configuration in 3-D space. In addition, it also applicable to wide extent array, thus the method cope with spatial aliasing problem. In order to solve such aliasing problem causing ambiguity of phase, norm normalization property of propagation direction vector is employed. By applying mean shift for both mode search and clustering of probability density estimation, efficient separation procedure is realized as a binary masking in T-F domain. The proposed approach is implemented in four microphones with wide sensor spacing where spatial aliasing may occur. Efficient separation results even for multiple sources are demonstrated.
Construction of the interest prediction models for nursery school child using a single-channel electroencephalograph

This paper aims to construct the interest prediction models for nursery school child using a single-channel electroencephalograph (EEG). Recently, the number of dual income households who leave their children in nursery schools have been increasing in Japan. Such parents are not able to grasp their children's behavior in daily life. Considering these issues, the researches related to child behavioral analysis have been proceeded by using image data taken from digital cameras. However, it is difficult to acquire the behavioral information from the digital cameras at anytime, anywhere. Therefore, we are focusing on wearable systems for keeping an eye on a child. Specifically, we adopt the EEG to design the constructing system. In this paper, we acquire single-channel EEG recordings from nursery school children when they watch picture-story shows. Furthermore, we apply a non-negative matrix factorization (NMF) to artifactitious rejection and a genetic algorithm-partial least squares (GA-PLS) regression to detect important frequency components and design the interest prediction models for the child using a single-channel EEG. As a result, we showed that over 60% estimation accuracy could be obtained all except one subject and the specific combinations of the frequency components selected by the GA-PLS, and we also could confirm that the NMF could remove the eye blink artifacts.
A detection method of face regions in color images by using evolutionary computation

This paper presents a new scheme to design a face detection system by using a genetic algorithm (GA). In particular, the object of this paper is to design a fast face detection system. 5 human emotions are considered; neutrality, happiness, sadness, anger and surprise. This paper pays attention to lip forms in human characteristics. Furthermore, in order to decrease the detection time, GA is used for the purpose of focusing on the lips in images. Finally, in order to demonstrate the effectiveness of the proposed scheme, we show simulation examples.
The music analysis method based on melody analysis

Recently, we can have large amounts of music thanks to the development of the computer technology. However, as the data of the music becomes larger, it is a hassle to classify the music based on the music content manually. We think that it is necessary to categorize the music automatically based on our mood. In this paper, we propose a novel method to analyze the music automatically based on melody analysis. The proposed method considers music genres as the measure of music analysis. We extract the acoustic features to characterize the music. Then, we classify music using multi-class classifiers based on the support vector machine (SVM).We adopt two approaches to the multi-class classification method. Furthermore, we propose a visualization method to specify the musical structure on the music genres from the classification result. Finally, computer simulations are done by using real music data in order to prove the effectiveness of the proposed method.