nils-herrmann/clean_scirepeval huggingface dataset:
Human gesture classification by brute-force machine learning for exergaming in physiotherapy

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.
Point Triangulation through Polyhedron Collapse Using the l&#x221E; Norm

Multi-camera triangulation of feature points based on a minimisation of the overall ℓ<sub>2</sub> reprojection error can get stuck in suboptimal local minima or require slow global optimisation. For this reason, researchers have proposed optimising the ℓ<sub>∞</sub> norm of the ℓ<sub>2</sub> single view reprojection errors, which avoids the problem of local minima entirely. In this paper we present a novel method for ℓ<sub>∞</sub> triangulation that minimizes the ℓ<sub>∞</sub> norm of the ℓ<sub>∞</sub> reprojection errors: this apparently small difference leads to a much faster but equally accurate solution which is related to the MLE under the assumption of uniform noise. The proposed method adopts a new optimisation strategy based on solving simple quadratic equations. This stands in contrast with the fastest existing methods, which solve a sequence of more complex auxiliary Linear Programming or Second Order Cone Problems. The proposed algorithm performs well: for triangulation, it achieves the same accuracy as existing techniques while executing faster and being straightforward to implement.
An imaging system with calibrated color image acquisition for use in dermatology

The authors propose a novel imaging system useful in dermatology, more precisely, for the follow-up of patients with an increased risk of skin cancer. The system consists of a Pentium PC equipped with an RGB frame grabber, a three-chip charge coupled devices (CCD) camera controlled by the serial port and equipped with a zoom lens and a halogen annular light source. Calibration of the imaging system provides a way to transform the acquired images, which are defined in an unknown color space, to a standard, well-defined color space called SRGB, sRGB has a known relation to the CIE/sup 1/ XYZ and CIE L*a*b* colorimetric spaces. These CIE color spaces are based on the human vision, and they allow the computation of a color difference metric called CIE /spl Delta/E/sub ab/*, which is proportional to the color difference, as seen by a human observer. Several types of polynomial RGB to sRGB transforms will be tried, including some optimized in perceptually uniform color spaces. The use of a standard and well-defined color space also allows meaningful exchange of images, e.g., in teledermatology. The calibration procedure is based on 24 patches with known color properties, and it takes about 5 minutes to perform. It results in a number of settings called a profile that remains valid for tens of hours of operation. Such a profile is checked before acquiring images using just one color patch, and is adjusted on the fly to compensate for short-term drift in the response of the imaging system. Precision or reproducibility of subsequent color measurements is very good with =0.3 and /spl Delta/E/sub ab/*<1.2. Accuracy compared with spectrophotometric measurements is fair with =6.2 and /spl Delta/E/sub ab/*><13.3.
Unsupervised Classification of Multilook Polarimetric SAR Data Using Spatially Variant Wishart Mixture Model with Double Constraints

This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods.
A filter design technique for improving the directional selectivity of the first scale of the Dual-Tree complex wavelet transform

The dual-tree complex wavelet transform (DT-CWT) uses approximate Hilbert transform pairs of wavelet, which requires that the filters of each tree of the dual-tree structure should be delayed approximately one half sample from each other. However, the filters of the first (finest) scale of the transform do not obey this condition, resulting in a poor directional selectivity for the first scale. In this paper, we describe a design technique for first-scale infinite impulse response (IIR) wavelet filters, that solves this problem. Results demonstrate that a much better directional selectivity is obtained, which indicates a performance improvement for many applications that use the DT-CWT where the preservation of high-frequency information is important.
Automatic detection of sleep stages using the EEG

We present a method for the detection of sleep stages using the EEG (electroencephalogram). The method consists of four steps: segmentation; parameter extraction; cluster analysis; and classification. The parameters we compared were the parameters of Hjorth, the harmonic parameters and the relative band energy. For cluster analysis we used a modified version of the K-means algorithm. It is shown that the investigated parameters are capable of, extracting information from the EEG relevant for sleep stage scoring. Using the modified K-means algorithm it is possible to find 'similar' segments and hence automate the detection of sleep stages. However, extra information, e.g. the ECG (electrocardiogram) or the EOG (electrooculogram), is probably necessary for a clear discrimination between the different sleep stages.
Multi-resolution ICP for the efficient registration of point clouds based on octrees

In this paper we propose a multiresolution scheme based on hierarchical octrees for the registration of point clouds acquired by lidar scanners. The point density of these point clouds is generally sparse and inhomogeneous, a property that can yield a risk for correct alignment. Experiments demonstrate that our multiresolution technique is a lot faster than the traditional iterative closest point (ICP) algorithm while it is more robust, e.g. in case of abrupt movements of the sensor. We can report a speed-up factor of more than 30, without jeopardizing the level of accuracy. In scenarios for which the level of detail is less critical, e.g. in case of navigation for autonomous robots, we can even achieve a larger speed-up by trading speed for quality.
A multi-modal 2D/3D registration scheme for preterm brain images

Periventricular leukomalacia (PVL) is a neonatal brain pathology occuring on preterms with a very low birth weight (<1500 g). Next to ultrasound (US) imaging, which is the first and most common step, magnetic resonance image (MRI) volumes are used for the inspection of this pathology. Since on both modalities, up to now, we still lack a golden standard for the quantification of the pathology cross-validation through a multi-modal registration is highly beneficial to the clinical diagnosis. In this article we present a semi-automatic 2D US-3D MRI registration scheme combining an interactive initialization step, B-spline image interpolation, a mutual information based metric and an evolutionary algorithm optimization scheme
An improved HDR image synthesis algorithm

In high dynamic range (HDR) imaging, multiple photographs with different exposure times are combined into a radiance map, which reflects the radiance in real-life scenes. This involves recovering the response function of the imaging process. The technique proposed by Debevec and Malik is a well-known HDR image synthesis algorithm, but the computational complexity is relatively high, which limits the possible image size and the reconstruction quality. In this paper we present an improved joint optimization technique for estimating the camera response function (CRF) and the radiance map and a new sequential two-step optimization technique, which first estimates the CRF and then reconstructs the radiance map, resulting in better visual results and a higher SNR in remarkably less computation time.
Wavelet-Domain Video Denoising Based on Reliability Measures

This paper proposes a novel video denoising method based on nondecimated wavelet band filtering. In the proposed method, motion estimation and adaptive recursive temporal filtering are performed in a closed loop, followed by an intra-frame spatially adaptive filter. All processing occurs in the wavelet domain. The paper introduces new wavelet-based motion reliability measures. We make a difference between motion reliability per orientation and reliability per wavelet band. These two reliability measures are employed in different stages of the proposed denoising scheme. The reliability per orientation (horizontal and vertical) measure is used in the proposed motion estimation scheme while the reliability of the estimated motion vectors (MVs) per wavelet band is utilized for subsequent adaptive temporal and spatial filtering. We propose a novel cost function for motion estimation which takes into account the spatial orientation of image structures and their motion matching values. Our motion estimation approach is a novel wavelet-domain three-step scheme, where the refinement of MVs in each step is determined based on the proposed motion reliabilities per orientation. The temporal filtering is performed separately in each wavelet band along the estimated motion trajectory and the parameters of the temporal filter depend on the motion reliabilities per wavelet band. The final spatial filtering step employs an adaptive smoothing of wavelet coefficients that yields a stronger filtering at the positions where the temporal filter was less effective. The results on various grayscale sequences demonstrate that the proposed filter outperforms several state-of-the-art filters visually (as judged by a small test panel) as well as in terms of peak signal-to-noise ratio
Lossless quantization of Hadamard transform coefficients

This paper shows that an n x 1 integer vector can be exactly recovered from its Hadamard transform coefficients, even when 0.5 n log(2)(n) of the (less significant) bits of these coefficients are removed. The paper introduces a fast "lossless" dequantization algorithm for this purpose. To investigate the usefulness of the procedure in data compression, the paper investigates an embedded block image coding technique called the "LHAD" based on the algorithm. The results show that lossless compression ratios close to the state of the art can be achieved, but that techniques such as CALIC and S+P still perform better.
Dynamic subsampling for image registration speedup using the mutual information variance estimate

Intensity based image registration methods are necessary in situations where feature point based methods fail, e.g multispectral images or images with repetitive textures. However the higher robustness comes at the cost of being slower than the feature point alternative. This problem can be alleviated by subsampling the pixels used to calculate the intensity matching criterion. Unfortunately there are no guidelines for the amount of allowed subsampling. In this paper we present a model for a lower bound on the number of pixel samples in image sets to keep a certain confidence about the correctness of the result, using the variance on the estimated mutual information value. With this model we can speed up the intensity based registration while remaining confident about getting correct results.
Reconstruction of High Dynamic Range images with poisson noise modeling and integrated denoising

In this paper, we present a new method for High Dynamic Range (HDR) reconstruction based on a set of multiple photographs with different exposure times. While most existing techniques take a deterministic approach by assuming that the acquired low dynamic range (LDR) images are noise-free, we explicitly model the photon arrival process by assuming sensor data corrupted by Poisson noise. Taking the noise characteristics of the sensor data into account leads to a more robust way to estimate the non-parametric camera response function (CRF) compared to existing techniques. To further improve the HDR reconstruction, we adopt the split-Bregman framework and use Total Variation for regularization. Experimental results on real camera images and ground-truth data show the effectiveness of the proposed approach.
A Canonical Correlation Analysis based motion model for probabilistic visual tracking

Particle filters are often used for tracking objects within a scene. As the prediction model of a particle filter is often implemented using basic movement predictions such as random walk, constant velocity or acceleration, these models will usually be incorrect. Therefore, this paper proposes a new approach, based on a Canonical Correlation Analysis (CCA) tracking method which provides an object specific motion model. This model is used to construct a proposal distribution of the prediction model which predicts new states, increasing the robustness of the particle filter. Results confirm an increase in accuracy compared to state-of-the-art methods.
Fast and Memory Efficient 2-D Connected Components Using Linked Lists of Line Segments

In this paper we present a more efficient approach to the problem of finding the connected components in binary images. In conventional connected components algorithms, the main data structure to compute and store the connected components is the region label image. We replace the region label image with a singly-linked list of line segments (or runs) for each region. This enables us to design a very fast and memory efficient connected components algorithm. Most conventional algorithms require (at least) two raster scans. Those that only need one raster scan, require irregular and unbounded image access. The proposed algorithm is a single pass regular access algorithm and only requires access to the three most recently processed image lines at any given time. Experimental results demonstrate that our algorithm is considerably faster than the fastest conventional algorithm. Additionally, our novel region coding data structure uses much less memory in typical cases than the traditional region label image. Even in worst case situations the processing time of our algorithm is linear with the number of pixels in an image.
Extending the Depth of Field in Microscopy Through Curvelet-Based Frequency-Adaptive Image Fusion

Limited depth of field is an important problem in microscopy imaging. 3D objects are often thicker than the depth of field of the microscope, which means that it is optically impossible to make one single sharp image of them. Instead, different images in which each time a different area of the object is in focus have to be fused together. In this work, we propose a curvelet-based image fusion method that is frequency-adaptive. Because of the high directional sensitivity of the curvelet transform (and consequentially, its extreme sparseness), the average performance gain of the new method over state-of-the-art methods is high.
Lane identification based on robust visual odometry

Many important intelligent vehicle systems rely on lane-level position estimates. We propose a novel lane identification approach based on robust monocular visual odometry. The images obtained from the visual odometry camera as well as the trajectory estimate are used to construct a linearized representation of the world plane near the vehicle trajectory. This linear section is classified into road surface and non-road surface using a Gaussian Mixture Model. The width of the available road surface on either side is measured to detect extra drivable lanes. Coupled with road map annotations describing the number of lanes, this allows to determine the lane index of the vehicle. Preliminary experiments on a test set of 32 segments of 120m each prove the viability of the method.
Linked geometric features for modeling the fluid flow in developing embryonic vertebrate hearts

The embryonic vertebrate heart starts pumping long before the formation of valves and chambers. Zebrafish embryonic hearts are morphologically comparable to human embryonic hearts in early stage. Since they are optically transparent, they are of obvious scientific use to study the beating heart. This paper proposes a fast and reliable algorithm based on tracking of linked geometric features to derive the motion of the heart walls and the velocity in the fluid flow. In contrast to active contour methods, we model the heart walls with parabola segments. This is both simpler and accurate enough to model heart walls. An original contribution in this paper is that we can track the heart walls and the blood cells using the same method and provide consistent estimates of both. Moreover, due to the pumping mechanism of the heart, the tracking of the heart walls is used to determine the region of interest for the tracking of the blood cells. This relation decreases the false matches of blood cells by more than 50%, which results in a much improved estimation of the velocity in the fluid flow.
Quantitative microwave tomography from sparse measurements using a robust huber regularizer

In statistical theory, the Huber function yields robust estimations reducing the effect of outliers. In this paper, we employ the Huber function as regularization in a challenging inverse problem: quantitative microwave imaging. Quantitative microwave tomography aims at estimating the permittivity profile of a scattering object based on measured scattered fields, which is a nonlinear, ill-posed inverse problem. The results on 3D data sets are encouraging: the reconstruction error is reduced and the permittivity profile can be estimated from fewer measurements compared to state-of-the art inversion procedures.
Skeleton calculation for automatic extraction of arteriovenous malformation in 3-D CTA images

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture which can cause severe brain damage or even death. For planing of embolization procedure of an AVM, the accurate knowledge of the location and size of the malformation is of utmost importance. We propose in this paper a novel AVM delineation approach using ordered thinning-based skeletonization. The main contribution is a new method for creating the graph-type skeleton from the result of the ordered skeletonization, and an automatic method for AVM detection and extraction. The main idea in our work is to use the structural (anatomical) vessel differences and the inhomogeneities in distribution of pixel gray values to locate and extract the AVM. The algorithm takes the segmentation result as an input to perform AVM delineation. It determines the AVM region automatically, without any user interaction, independently of the used segmentation algorithm. The proposed approach is validated on brain blood vessel CTA images before and after embolization. The results obtained using the Dice coefficient comparisons, the volume relative error and the AVM center position show high accuracy of our method and indicate potentials for use in surgical planning.
A Bayesian approach to nonlinear diffusion based on a Laplacian prior for ideal image gradient

We study the relationships between diffusivity functions in a nonlinear diffusion scheme and probabilities of edge presence under a marginal prior on ideal, noise-free image gradient. In particular we impose a Laplacian-shaped prior for the ideal gradient and we define the diffusivity function explicitly in terms of edge probabilities under this prior. The resulting diffusivity function has no free parameters to optimize. Our results demonstrate that the new diffusivity function, automatically, i.e., without any parameter adjustments, satisfies the well accepted criteria for the goodness of edge-stopping functions. Our results also offer a new and interesting interpretation of some widely used diffusivity functions, which are now compared to edge-stopping functions under a marginal prior for the ideal image gradient
Classification of Hyperspectral Data Over Urban Areas Using Directional Morphological Profiles and Semi-Supervised Feature Extraction

When using morphological features for the classification of high resolution hyperspectral images from urban areas, one should consider two important issues. The first one is that classical morphological openings and closings degrade the object boundaries and deform the object shapes. Morphological openings and closings by reconstruction can avoid this problem, but this process leads to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction. The second one is that the morphological profiles (MPs) with different structuring elements and a range of increasing sizes of morphological operators produce high-dimensional data. These high-dimensional data may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. In this paper, we first investigate morphological profiles with partial reconstruction and directional MPs for the classification of high resolution hyperspectral images from urban areas. Secondly, we develop a semi-supervised feature extraction to reduce the dimensionality of the generated morphological profiles for the classification. Experimental results on real urban hyperspectral images demonstrate the efficiency of the considered techniques.