nils-herrmann/clean_scirepeval huggingface dataset:
A novel approach to fingerprint ridge line extraction

Extraction of fingerprint ridge lines is a critical pre-processing step in the identification of poor quality fingerprint images. This paper presents a new fingerprint ridge line extraction approach by way of ridge line tracing. In our research, the fingerprint ridge line in a gray scale image is viewed as a track of a ridge segment moving along the ridge. The curve tracing problem is solved by the target tracking technique in computer vision. We first formulate the model of fingerprint ridge line segments and then apply a target tracking method to trace each of the ridge lines. In addition, a feedback technique is adopted to correct the fingerprint directional image in each tracing step in order to improve tracing accuracy. By connecting all the traced ridge line segments, a polyline extraction of the ridge line can be obtained. Compared to conventional method, our approach exhibits superior performance.
Visual Tracking via Part-Based Model with Auxiliary Objects

In most model-free tracking algorithms, context of the target is usually taken as source of negative examples for training the appearance model and thus not fully utilized. In fact, the target is embedded in its context, and there are spatial constraints between them with the potential motion correlations and relative locations. This paper presents a part-based model to describe the spatial constraints between the target object and its context. Auxiliary objects are selected as parts of the part based model to represent the context information. These auxiliary objects should have some properties: (i) co-occurrence with the target, (ii) having distinct appearance, and (iii) having homogeneous motion as an invisible visual region. A template-based model concerning about the object appearance is implemented to help estimate the reliability of tracking and make the part-based model updated properly. We test the proposed method on a recent benchmark with 50 video sequences evaluated, and a superior performance is achieved compared with state-of-the-arts.
A bayesian network approach to traffic flow forecasting

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data
Generalized Manifold-Ranking-Based Image Retrieval

In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR , our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques
Smoothness Maximization via Gradient Descents

The recent years have witnessed a surge of interest in graph based semi-supervised learning. However, despite its extensive research, there has been little work on graph construction. In this study, employing the idea of gradient descent, we propose a novel method called iterative smoothness maximization (ISM), to learn an optimal graph automatically for a semi-supervised learning task. The main procedure of ISM is to minimize the upper bound of semi-supervised classification error through an iterative gradient descent approach. We also prove the convergence of ISM theoretically, and finally experimental results on two real-world data sets are provided to demonstrate the effectiveness of ISM.
Audio tonality mode classification without tonic annotations

Traditional tonality mode (major or minor) classification or audio key finding algorithms often rely on tonic annotations (key names) of the training songs. However, unlike classical music whose keys are usually explicitly labeled in their titles, the keys of numerous popular music are hard to obtain. In contrast, it is much easier to only label the mode for each song. With only modes labeled, traditional approaches to key or mode classification cannot be directly applied, due to the lack of the reference point to transpose and align the chroma features with different keys. In this paper, we present an alignment approach to transpose chroma features within each mode to a reference (but unknown) tonic. Then several methods, including Single Profile Correlation, Multiple Profile Correlation and Support Vector Machine, are exploited to address mode learning and classification. Experimental results show the feasibility of the proposed approach.
Multiple Fundamental Frequency Estimation by Modeling Spectral Peaks and Non-Peak Regions

This paper presents a maximum-likelihood approach to multiple fundamental frequency (F0) estimation for a mixture of harmonic sound sources, where the power spectrum of a time frame is the observation and the F0s are the parameters to be estimated. When defining the likelihood model, the proposed method models both spectral peaks and non-peak regions (frequencies further than a musical quarter tone from all observed peaks). It is shown that the peak likelihood and the non-peak region likelihood act as a complementary pair. The former helps find F0s that have harmonics that explain peaks, while the latter helps avoid F0s that have harmonics in non-peak regions. Parameters of these models are learned from monophonic and polyphonic training data. This paper proposes an iterative greedy search strategy to estimate F0s one by one, to avoid the combinatorial problem of concurrent F0 estimation. It also proposes a polyphony estimation method to terminate the iterative process. Finally, this paper proposes a postprocessing method to refine polyphony and F0 estimates using neighboring frames. This paper also analyzes the relative contributions of different components of the proposed method. It is shown that the refinement component eliminates many inconsistent estimation errors. Evaluations are done on ten recorded four-part J. S. Bach chorales. Results show that the proposed method shows superior F0 estimation and polyphony estimation compared to two state-of-the-art algorithms.
Seeing the Arrow of Time

We explore whether we can observe Time's Arrow in a temporal sequence - is it possible to tell whether a video is running forwards or backwards? We investigate this somewhat philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time's Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards/backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time.
Energy predictability to blind source separation

A blind source separation (BSS) method based on the energy (square) predictability of original sources is proposed. The method exploits the nonstationarity of sources in the sense that the variance of each source signal can be assumed to change smoothly against time. In contrast to linear predictability, it is shown that nonlinear predictability can also be used for BSS. Simulations verify the efficient implementation of the proposed method, especially its robustness to the outliers.
Nonlinear Dimensionality Reduction with Local Spline Embedding

This paper presents a new algorithm for nonlinear dimensionality reduction (NLDR). Our algorithm is developed under the conceptual framework of compatible mapping. Each such mapping is a compound of a tangent space projection and a group of splines. Tangent space projection is estimated at each data point on the manifold, through which the data point itself and its neighbors are represented in tangent space with local coordinates. Splines are then constructed to guarantee that each of the local coordinates can be mapped to its own single global coordinate with respect to the underlying manifold. Thus, the compatibility between local alignments is ensured. In such a work setting, we develop an optimization framework based on reconstruction error analysis, which can yield a global optimum. The proposed algorithm is also extended to embed out of samples via spline interpolation. Experiments on toy data sets and real-world data sets illustrate the validity of our method.
Boosting Web image search by co-ranking

To maximally improve the precision among top-ranked images returned by a Web image search engine without putting extra burden on the user, we propose in this paper a novel co-ranking framework which re-ranks the retrieved images to move the irrelevant ones to the tail of the list. The characteristics of the proposed framework can be summarized as follows: (1) making use of the decisions from multi-view of images to boost retrieval performance; (2) generalizing present multi-view algorithms which need labeled data for initialization to the unsupervised case so that no extra interaction is required. To implement the framework, we use one-class support vector machines to train the basic learner, and propose different schemes for combination. Experimental results demonstrate the effectiveness of the proposed framework.
No-reference quality assessment for JPEG2000 compressed images

No-reference quality assessment is a relatively new topic and has been attracting more and more attention in recent years. Due to the limited understanding of the human vision system, most of the existing methods focus on measuring to what extent the image has been distorted. In this paper, by viewing all edge points in JPEG2000 compressed images as 'distorted' or 'un-distorted', we propose using principal component analysis (PCA) to extract the local feature of a given edge point, which indicates both blurring and ringing. We also propose using the probabilities of the given edge point being 'distorted' and 'un-distorted' to model the local distortion metric, which is straightforward and can be easily applied to any type of local feature. Experimental results demonstrate the effectiveness of our scheme.
Content-Based Information Fusion for Semi-Supervised Music Genre Classification

In this paper, we propose an information fusion framework for the semi-supervised distance-based music genre classification problem. We make use of the regularized least-square framework as the basic classifier, which only involves the similarity scores among different music tracks. We present a similarity score that multiplies different scores based on different distance measures. Particularly the distance measures are not restricted to the Euclidean distance. By adding a weight to each single distance based score, we propose an expectation-maximization (EM) algorithm to adaptively learn the fusion scores. Experiments on real music data set show that our approach can give promising results.
Classifier combination based on active learning

In this paper, we propose classifier combination based on active learning, which deals with the design of classifier combination systems as training a combiner at the aggregation level and introduces SVM active learning into the design of this multi-category decision combiner. This algorithm greatly reduces the number of labeled data the classifier system needs in order to achieve satisfactory performance. This algorithm consists of two main steps: firstly, designing and training first level classifiers which can output posterior probability vectors as the input of the second level combiner, secondly, designing second level combiner based on SVM active learning and classifying testing samples with this combiner. Experiments on standard database show that our algorithm performs better than current classifier combination rules when considering both labeling cost and classification accuracy.
Regression Reformulations of LLE and LTSA With Locally Linear Transformation

Locally linear embedding (LLE) and local tangent space alignment (LTSA) are two fundamental algorithms in manifold learning. Both LLE and LTSA employ linear methods to achieve their goals but with different motivations and formulations. LLE is developed by locally linear reconstructions in both high- and low-dimensional spaces, while LTSA is developed with the combinations of tangent space projections and locally linear alignments. This paper gives the regression reformulations of the LLE and LTSA algorithms in terms of locally linear transformations. The reformulations can help us to bridge them together, with which both of them can be addressed into a unified framework. Under this framework, the connections and differences between LLE and LTSA are explained. Illuminated by the connections and differences, an improved LLE algorithm is presented in this paper. Our algorithm learns the manifold in way of LLE but can significantly improve the performance. Experiments are conducted to illustrate this fact.
Kernel GMM and its application to image binarization

Gaussian mixture model (GMM) is an efficient method for parametric clustering. However, traditional GMM can't perform clustering well on data set with complex structure such as images. In this paper, kernel trick, successfully used by SVM and kernel PCA, is introduced into EM algorithm for solving parameter estimation of GMM, which is so called kernel GMM (kGMM). The basic idea of kernel GMM is to apply kernel based GMM in feature space instead of in input data space. In order to avoid the curse of dimension, the proposed kGMM also embeds a step to automatically select discriminative features in feature space. kGMM is employed for the task of image binarization. Result shows that the proposed approach is feasible.
Optimal Dimensionality Discriminant Analysis and Its Application to Image Recognition

Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, linear discriminant analysis (LDA) is one of the most popular methods and has been successfully applied in many classification problems. However, there are several drawbacks in LDA. First, it suffers from the singularity problem, which makes it hard to preform. Second, LDA has the distribution assumption which may make it fail in applications where the distribution is more complex than Gaussian. Third, LDA can not determine the optimal dimensionality for discriminant analysis, which is an important issue but has often been neglected previously. In this paper, we propose a new algorithm and endeavor to solve all these three problems. Furthermore, we present that our method can be extended to the two-dimensional case, in which the optimal dimensionalities of the two projection matrices can be determined simultaneously. Experimental results show that our methods are effective and demonstrate much higher performance in comparison to LDA.
Single Remote Sensing Image Dehazing

Remote sensing images are widely used in various fields. However, they usually suffer from the poor contrast caused by haze. In this letter, we propose a simple, but effective, way to eliminate the haze effect on remote sensing images. Our work is based on the dark channel prior and a common haze imaging model. In order to eliminate halo artifacts, we use a low-pass Gaussian filter to refine the coarse estimated atmospheric veil. We then redefine the transmission, with the aim of preventing the color distortion of the recovered images. The main advantage of the proposed algorithm is its fast speed, while it can also achieve good results. The experimental results demonstrate that our algorithm produces visually appealing dehazing images and retains the very fine details. Moreover, for images containing partly clear and partly hazy areas, our algorithm can also achieve good results.
Active Morphable Model: an efficient method for face analysis

Multidimensional Morphable Model is a powerful model to analyze and synthesize human faces. However, the stochastic gradient descent algorithm adopted to match the Morphable Model to a novel face image is not efficient enough. In this paper, a very efficient optimization method devised for Morphable Model matching is proposed, called Active Morphable Model (AMM). The kernel of AMM is an iterative algorithm directly utilizing the heuristic information provided by the novel image, and updating the model parameters in a computationally economic fashion. AMM is more efficient than general optimization methods in matching a Morphable Model, it has much higher convergent rate and matching speed. Furthermore, it is insensitive to the initial estimation of the face pose, and is robust when used to match novel faces with large variations in translation, rotation and scaling. Experimental results are given to validate the efficiency and robustness of the proposed method.
Fast Branch Convolutional Neural Network for Traffic Sign Recognition

In this paper, we propose a novel framework for speeding up the test-time of traffic sign recognition, which is named Branch Convolution Neural Network. It is the first time to introduce a branch-output mechanism into a deep Convolution Neural Network. Our model has an accuracy as high as a deep convolution neural network model, while it performs faster at the same condition during test stage. It is a significantly accelerated framework for designing a real-time deep neural network system. We present a detail process to change a regular pre-trained Convolution Neural Network into a Branch Convolution Neural Network: train several simple branch classifiers, bias classifiers and optimize branches. Experiment applied on GTSRB shows that large number of traffic signs are unnecessary to go through all layers in a deep model and they can be separated out in a relative shallow neural network. This framework speeds up the recognition progress, while keeping the accuracy within an extremely minor drop.
Separation of Voice and Music by Harmonic Structure Stability Analysis

Separation of voice and music is an interesting but difficult problem. It is useful for many other researches such as audio content analysis. In this paper, the difference between voice and music signals is carefully studied. It is proposed that the harmonic structure stability is the key difference between them. A separation algorithm based on this theory is proposed. The main idea is to learn the average harmonic structure of the music, and then separate signals by using it to distinguish voice and music harmonic structures. Experimental results show that the algorithm can separate mixed signals and obtains not only a very high signal-to-noise ratio (SNR) but also a rather good subjective audio quality
Patterns classification of nonlinear multi-dimensional time series based on manifold learning

The multi-sensor signals in industrial process is essentially a nonlinear multi-dimensional time series, so the process fault diagnosis can be implemented by pattern classification of nonlinear multi-dimensional time series. To conquer the effects of nonlinearity, correlation and high dimensionality in the time series, a supervised locally linear embedding (S-LLE) based method is proposed in this paper, in which the locally linear embedding algorithm is improved via the label information and a linear propagation method is applied to deal with the out-of-sample problem. Subsequently, the classifier can be designed by using support vector machines (SVM) and k-nearest neighbor algorithm (knn) in the low dimensional space. The proposed method can greatly preserve the consistency of data local neighborhood structure, effectively extract the low dimensional manifold features embedded in the multi-dimensional time series, and obviously improve the performance of the pattern classification. The experimental results on Tennessee Eastman (TE) process demonstrate the feasibility and effectiveness of the proposed method.
Blind separation of superimposed images with unknown motions

We consider the blind separation of source layers from superimposed mixtures thereof, involving unknown motions and unknown mixing coefficients of layers in each mixture. Previous blind separation approaches for such problems assume motions to be uniform translations, and hence are limited for real world applications. In this paper, we develop a sparse blind separation algorithm to estimate both parameterized motions and mixing coefficients. Then, a novel reconstruction approach is presented to recover all layers, by utilizing not only the mixing model but also the statistical properties of natural images. The whole method can handle more general motions than translations, including scalings, rotations and other transformations. In addition, the number of layers is automatically identified, and all layers can be recovered even in the under-determined case where mixtures are fewer than layers. The effectiveness of this technology is shown in the experiments on two simulated mixtures of four layers, real photos containing transparency and reflections, and real crossfade images from videos.
Hands-On Learning Through Racing: Signal processing and engineering education through the China National Collegiate Intelligent Model Car Competition

The Intelligent Model Car Competition (IMCC) of China is an annual collegiate contest where student teams design, build, and race a model car around a track, and the fastest car that completes the track without failure wins . The IMCC is in collaboration with the global NXP Cup Challenge, which was formerly known as the Freescale Cup Challenge until the acquisition of Freescale Semiconductor Inc. by NXP Semiconductors . Creating this smart, autonomous car requires students to develop the hardware and software of motor control to propel and steer their model cars. It provides a collaborative, competitive, and hands-on way for students to learn about and make a synergistic use of theories and techniques from undergraduate engineering studies, such as sensing and control, circuit design and implementation, and embedded system and software programming. The first competition, formerly known as the Smart Car Race, began in 2003 in South Korea with 80 student teams. Since then, the NXP Cup has expanded to China, India, Malaysia, Latin America, North America, and Europe, engaging hundreds of schools and tens of thousands of students a year [2], .