nils-herrmann/clean_scirepeval huggingface dataset:
Recognizing human actions using curvature estimation and NWFE-based histogram vectors

This paper presents a novel scheme for human action recognition. First of all, we employ the curvature estimation to analyze human posture patterns and to yield the discriminative feature sequences. The feature sequences are further represented into sets of strings. Consequently, we can solve human action recognition problem by the string matching technique. In order to boost the performance of string matching, we apply the NonparametricWeighted Feature Extraction (NWFE) to compact the string representation. Finally, we train a Bayes classifier to perform action recognition. Unlike traditional approaches using the nearest neighbor rule, our proposed scheme can classify the human actions more efficiently while maintaining high accuracy. The experiment results show that the proposed scheme is efficient and accurate in human action recognition.
Automatic characterization and segmentation of classic choroidal neovascularization using Adaboost for supervised learning

Age-related macular degeneration (AMD) with the complication of choroidal neovascularization (CNV) has been the major cause of severe, irreversible vision loss in many developed countries. The imaging modality that is commonly used to reveal the vascular abnormalities of CNV is fluorescein angiography (FA). Analysis and interpretation of FA sequences are largely performed by skilled observers on single angiographic frames with considerable observer variability since much of the temporal information is lost. To facilitate accurate diagnosis of CNV, we propose a computer-aided diagnosis system which automatically characterizes regions of classic CNV in terms of severity by exploiting the characteristics of the temporal profile of fluorescence leakage, followed by automated delineation of CNV lesions. Our system is a supervised learning system which is trained with sequences of classic CNV to recognize the typical leakage characteristics of CNV lesions described using intensity values and intensity changes of a number of time intervals. The learning strategy is Adaboost, which effectively combines a number of weak classifiers to substantially improve the accuracy. Images of a FA sequence are first aligned and mapped to a global space, and the result of Adaboost classification is a severity map for the sequence — each point in the global space is assigned a degree of severity. CNV lesions are segmented using Random Walks algorithm, and the seed points are automatically generated using the severity map and labeled as either background or foreground based on the severity. We validated our algorithm using 16 classic CNV sequences. The best accuracy is 94% and the average is 75.5% with the major disagreement with the groundtruth coming from regions of fibrosis, which are treated as inactive CNV by our system but labeled as CNV by the retina specialist. If cases with high percentage of fibrosis are removed from the dataset, the average accuracy is 80.7%.
Image bit-planes representation for moving object detection in real-time video surveillance

In video surveillance, moving object detection has become one of the core techniques to understand video content. Many detection methods exist to detect objects, but cannot effectively compensate for the effect of shadows or incomplete shapes of moving objects. In this paper, we apply image bit-planes and hysteresis thresholding to compensate for the loss of spatial and temporal information. The experimental results show that the proposed method is valid and suitable for real-time systems.
Summation Invariant Features for 3D Face Recognition

A novel summation invariant feature under transformation group action for 3D surface recognition is proposed, and its application to 3D face recognition is investigated. Based on a systematic mathematical procedure called moving frame, we derived the summation invariant feature that is invariant under affine transformation. Compared with classical differential invariants, such as the mean curvature or the Gaussian curvature, summation invariant feature is far less sensitive to observation noise in the data. A further enhancement leads to a new type of invariant 3D surface shape descriptor called a semi-local summation invariant. We demonstrate one important, potential application of this new feature to 3D human face recognition
Summation invariant multi-region fusion comparison

Applications of summation invariant features to multi-region face recognition are explored in this work. Earlier, we have demonstrated the potential benefits of this approach. In this paper, we provide a systematic, thorough comparison of all the summation invariant features derived to-date, and propose a new multi-feature fusion approach to further improve the overall performance. We also identify summation invariant features that yield superior performance for face recognition applications. Special attention is given to the implementation of 3D summation invariants. Extensive experimental results with the FRGC (Face Recognition Grand Challenge) 2.0 data set confirms the advantage of summation invariant features for 3D face recognition.
Automatic quality assessment and preprocessing for three-dimensional face recognition

The quality of biometric samples plays an important role in biometric authentication systems because it has a direct impact on verification or identification performance. In this paper, we present a novel 3D face recognition system which performs quality assessment on input images prior to recognition. More specifically, a reject option is provided to allow the system operator to eliminate the incoming images of poor quality, e.g. failure acquisition of 3D image, exaggerated facial expressions, etc. Furthermore, a fully automatic preprocessing method is presented in this paper. The experimental results show that the 3D face recognition performance is significantly improved by taking the quality of 3D facial images into account. The proposed system achieves the verification rate of 97.09% on the FRGC v2.0 data set.
Active battery balancing circuit with AC-link technique based on current-fed-push-pull converter

In this paper, a charge equalizer for series-connected battery strings based on current-fed-push-pull converter using AC-link technique is presented. The proposed charge equalizer can redistribute the energy directly between battery modules with AC-link technique and achieve balance quickly by simple control. The circuit efficiency can be improved because the rectifier diodes are eliminated. In addition, only one driving circuit for the power switches is required and can expand the equalizer according to the number of batteries. The proposed charge equalizer has the advantages of small size, modular design, low-cost. Finally, the simulation results are provided to verify the feasibility of the proposed charge equalizer.
Vascular Tree Construction with Anatomical Realism for Retinal Images

In this paper, we present a method to automatically extract the vessel segments and construct the vascular tree with anatomical realism from a color retinal image. The significance of the work is to assist in clinical studies of diagnosis of cardio-vascular diseases, such as hypertension,which manifest abnormalities in either venous and/or arterial vascular systems. To maximize the completeness of vessel extraction, we introduce vessel connectiveness measure to improve on an existing algorithm which applies multiscale matched filtering and vessel likelihood measure.Vessel segments are grouped using extended Kalman filter to take into consideration continuities in curvature, width,and color changes at the bifurcation or crossover point. The algorithm is tested on five images from the DRIVE database,a mixture of normal and pathological images, and the results are compared with the ground truth images provided by a physician. The preliminary results show that our method reaches an average success rate of 92.1%.
Human action recognition based on non-negative matrix factorization

In this paper, we propose a method to recognize human behavior by combining motion history images (MHI) and non-negative matrix factorization (NMF). The MHI preserves the temporal information of a behavior by holding the temporal motion appearance. Then, NMF is applied to extract the middle-level features of the moving object. The experimental results show that the proposed scheme can achieve robust recognition results using a public dataset.
Temporally Coherent Illumination Normalization for Indocyanine Green Video Angiography

Indocyanine green (ICG) angiography is an imaging method for doctors to observe choroidal abnormalities in human eyes. The ICG angiograms typically exhibit inhomogeneous illumination, which poses serious difficulties for the development of computer-aided diagnostic tools. In this paper, we propose a novel illumination normalization method to alleviate the inhomogeneous illumination in ICG video angiograms. In particular, we first align the viewpoint of the input ICG video angiogram using an image registration method. Then, we acquire temporal information using time-dependent intrinsic image and compute the corresponding illumination image. Finally, we correct inhomogeneous illumination from the illumination image by estimating contrast and luminosity distortion. We have conducted extensive evaluation using ICG video angiograms of 60 patients. Two video quality assessment methods are utilized to evaluate the performance of our proposed illumination normalization method. The results show that our proposed method can help improve the visual quality of ICG video angiogram. Visual evaluation by a human expert also confirms that our method yields better illumination normalization results.
Machine Learning in Financial Crisis Prediction: A Survey

For financial institutions, the ability to predict or forecast business failures is crucial, as incorrect decisions can have direct financial consequences. Bankruptcy prediction and credit scoring are the two major research problems in the accounting and finance domain. In the literature, a number of models have been developed to predict whether borrowers are in danger of bankruptcy and whether they should be considered a good or bad credit risk. Since the 1990s, machine-learning techniques, such as neural networks and decision trees, have been studied extensively as tools for bankruptcy prediction and credit score modeling. This paper reviews 130 related journal papers from the period between 1995 and 2010, focusing on the development of state-of-the-art machine-learning techniques, including hybrid and ensemble classifiers. Related studies are compared in terms of classifier design, datasets, baselines, and other experimental factors. This paper presents the current achievements and limitations associated with the development of bankruptcy-prediction and credit-scoring models employing machine learning. We also provide suggestions for future research.
The voice communication switch with peak power analysis on smart phone systems

This study is a numerical analysis with the peak power of sound channel under its numerical range of the region as the signal of voice communication can automatically start Bluetooth on iOS smart phone systems. Besides, the voice communication link using Bluetooth protocol. At the same time, the entire system is implemented in the intelligent mobile devices. In this study, the intelligent mobile devices use microphone and mobile device operating system's built-in audio API to collect the data from the peak power channel of voice. The collected data is trended and noise reduction processing, so that it is easy to be used to identify trends in data. From the data analysis of the experimental results show that the significant differences in peak power generated by the people blowing sound and spoken voice data. Then, it can use the peak power value as a voice call to control the start signal of Bluetooth. The user can automatically open the call process by the people blowing sound without any manually processes. Besides, the peak power identification model is also a universal model, it can be easily extended into other operating systems on smart phone.
Trajectory-based sign language recognition using Discriminant Analysis in higher-dimensional feature space

This paper presents a method for recognizing sign language using hand movement trajectory. By applying Kernel Principal Component Analysis (KPCA), the motion trajectory data are firstly mapped into a higher-dimensional feature space for analysis. The advantage of using high dimensionality is that it allows a more flexible decision boundary and thus helps us to achieve better classification accuracy. Then we perform the Nonparametric Discriminant Analysis (NDA) in the higher-dimensional feature space, so that the most helpful information can be extracted. We have tested the proposed method using the Australian Sign Language (ASL) data set. The results demonstrate that our approach outperforms the current state-of-the-art for trajectory-based sign language recognition.
A composite data model in object-oriented data warehousing

A data warehouse is an information provider that collects necessary data from individual source databases to support the analytical processing of decision support functions. In the past, research on data warehouses primarily focused on relational data models. A novel data model called the composite data model is proposed to store the data in the object oriented data warehouse. The data model forms new classes, consisting of the attributes listed in the definitions of views, and copies necessary class structure from the data source. The query performance of the data warehouse can thus be improved. The corresponding view creation and deletion algorithms are also proposed.
Automatic Brain Extraction for T1-Weighted Magnetic Resonance Images Using Region Growing

In this paper, we propose a novel method for extracting brain region from T1-weighted magnetic resonance imaging. Our proposed method is based on finding a seed point which is located on brain tissue and then perform region growing. In particular, we firstly analyze intensity distributions of different cerebral tissues (including brain, cerebrospinal fluid, scalp and marrow) at middle height. We also measure the distance from image center to brain edge at the same height of brain scan. In the experimental validation, we have conducted experiment on the OASIS dataset and perform comparison with the state-of-the-art methods. Our propose method achieves the best performance on the OASIS dataset.
Action recognition by learning locally adaptive classifiers

In this paper, we propose a novel framework for video-based human action recognition, which can effectively resolve the difficulty caused by large variations within each action category. We first use the cloud of interest points to represent human action, due to its effectiveness in extracting spatio-temporal information necessary to reliability distinguish each action. Then, we perform an efficient local learning on the extracted features to learn locally adaptive classifiers. Specifically, a local classifier is specifically trained for each training sample. A local classifier could better describe the local data distribution and thus adopting multiple local classifiers would lead to better classification accuracy. We conduct several experiments on the KTH dataset and obtain very inspiring results. In particular, our approach achieves comparable performance to that of the state-of-the-art methods. Compared with a global learning method, i.e., the AdaBoost, the local learning provides significantly better accuracy with little additional cost in training time.
U-Garden: An interactive control system for multimodal presentation in museum

In this rapid development of advanced computer technology era, the trend for interaction with the input devices has shifted toward the incorporation of intelligent technology gradually. To facilitate the intuitive paradigm for human to use digital equipment, the traditional input devices such as keyboards and mice are augmented or even replaced by various novel mechanisms. The provision of the intuitive functions by using a variety of end devices such as camera, RFID, WiiMote enhances the interaction and playfulness of information presentation. This paper presents a set of tools to help developers create interactive presentations with machine intelligence. The design rationale of the set of tools called the “U-Garden” was described. We also illustrate how the U-Garden could be adopted to synchronize the hardware devices and media content to build an intelligent interaction. To test the performance of the U-Garden, the tools were applied to interactive presenting applications to demonstrate the content presenting power for digital archives. Finally, experiments with user experience on our proposed content presenting system were conducted and discussed. The results reveal positive responses that users are able to appreciate this intelligent presenting of the content of digital archives through our tools.
Moving object detection based on image bit-planes and co-occurrence matrix in video surveillance

The development of moving object detection systems has become of great interest in the video surveillance field. Although many foreground detection methods have been proposed, the problems of incomplete object shapes and misclassified shadow regions remain. We propose a new approach by combining bit-planes representation with gray-level co-occurrence matrix (GLCM). This allows the system to exploit the motion history and remove the shadow, respectively. As shown by experiments in Section 3, our approach is efficient and robust for detecting moving objects in real-time.
An integral-based curvature estimator and its application in face recognition

This paper addresses an old, yet challenging issue — curvature estimation from discrete sampling points over a curve. We introduce a novel algorithm based on performing line integrals. The proposed method is computationally more efficient than the previous integration-based methods because of the constant computation time. Qualitative tests on synthesized shapes in the presence of noise are performed, which show the robustness of our approach. Also, the discriminative capability of the estimated curvature is evaluated by conducting experiments on the FRGC (Face Recognition Grand Challenge) v2.0 dataset which contains 4007 3D facial images recorded from 466 subjects. The results show that recognition performance is significantly improved by using our curvature estimation method. This novel approach presents potential for a broad class of multimedia applications.
Retinal Vascular Tree Reconstruction With Anatomical Realism

Motivated by the goals of automatically extracting vessel segments and constructing retinal vascular trees with anatomical realism, this paper presents and analyses an algorithm that combines vessel segmentation and grouping of the extracted vessel segments. The proposed method aims to restore the topology of the vascular trees with anatomical realism for clinical studies and diagnosis of retinal vascular diseases, which manifest abnormalities in either venous and/or arterial vascular systems. Vessel segments are grouped using extended Kalman filter which takes into account continuities in curvature, width, and intensity changes at the bifurcation or crossover point. At a junction, the proposed method applies the minimum-cost matching algorithm to resolve the conflict in grouping due to error in tracing. The system was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images. The dataset contained a mixture of normal and pathological images. In addition, six pathological fluorescein angiogram sequences were also included in this study. The results were compared against the groundtruth images provided by a physician, achieving average success rates of 88.79% and 90.09%, respectively.
Video-based person authetication with random passwords

The proposed system aims at providing a novel framework of speaker authentication using lip-motion data. The system is divided into three steps: feature extraction and modeling, model synthesis, and probabilistic model matching. First, the visual features are obtained by locating a region-of-interest and extracting geometric and textural features from the region. The extracted feature vectors are then classified by K-means clustering to obtain a reduced number of observation vectors. These observation vectors are fed into a set of Hidden Markov Models (HMMs) classifiers to capture the temporal characteristics of the features. The main contribution of this paper lies in the introduction of using random passwords in performing speaker verification. Our results have demonstrated that random passwords provide useful information for speaker verification. Also, by using the proposed method, we observe a significant improvement in verification rate.
Optimal Linear Combination of Facial Regions for Improving Identification Performance

This paper presents a novel 3D multiregion face recognition algorithm that consists of new geometric summation invariant features and an optimal linear feature fusion method. A summation invariant, which captures local characteristics of a facial surface, is extracted from multiple subregions of a 3D range image as the discriminative features. Similarity scores between two range images are calculated from the selected subregions. A novel fusion method that is based on a linear discriminant analysis is developed to maximize the verification rate by a weighted combination of these similarity scores. Experiments on the Face Recognition Grand Challenge V2.0 dataset show that this new algorithm improves the recognition performance significantly in the presence of facial expressions.
Identifying leaves with watershed and CSS: Implementation of segmentation and object matching

This article looks at combining the Curvature Scale Space (CSS) shape matching algorithm and the Watershed Algorithm with the aim of creating a simple yet effective leaf identification software. The CSS shape matching algorithm solves the problems of: Leaf self-intersection - Parts of a leaf overlap. ; Leaf rotation - Images that are rotated. ; Leaf variation - No two leaves are the same. The Watershed Algorithm provides an effective way of segmenting a leaf from a non-ideal background, which provides a method of creating pristine binary images. The result is a leaf identification software that has good accuracy, the ability to deal with non-ideal backgrounds, and large room for improvement.
Fusion of Multiple Facial Regions for Expression-Invariant Face Recognition

In this paper, we describe a fusion-based face recognition method that is able to compensate for facial expressions even when training samples contain only neutral expression. The similarity metric between two facial images are calculated by combining the similarity scores of the corresponding facial regions, e.g. the similarity between two mouths, the similarity between two noses, etc. In contrast with other approaches where equal weights are assigned on each region, a novel fusion method based on linear discriminant analysis (LDA) is developed to maximize the verification performance. We also conduct a comparative study on various face recognition schemes, including the FRGC baseline algorithm, the fusion of multiple regions by sum rule, and the fusion of multiple regions by LDA. Experiments on the FRGC (Face Recognition Grand Challenge) V2.0 dataset, containing 4007 face images recorded from 266 subjects, show that the proposed method significantly improves the verification performance in the presence of facial expressions.