nils-herrmann/clean_scirepeval huggingface dataset:
A Multiview Multimodal System for Monitoring Patient Sleep

Clinical observations indicate that during critical care at the hospitals, a patient's sleep positioning and motion have a significant effect on recovery rate. Unfortunately, there is no formal medical protocol to record, quantify, and analyze motion of patients. There are very few clinical studies that use manual analysis of sleep poses and motion recordings to support medical benefits of patient positioning and motion monitoring. Manual processes do not scale, are prone to human errors, and put strain on an already taxed healthcare workforce. This study introduces multimodal, multiview motion analysis and summarization for healthcare (MASH). MASH is an autonomous system, which addresses these issues by monitoring healthcare environments and enabling the recording and analysis of patient sleep-pose patterns. MASH uses three RGB-D cameras to monitor patients in a medical intensive care unit (ICU) room. The proposed algorithms estimate pose direction at different temporal resolutions and use keyframes to efficiently represent pose transition dynamics. MASH combines deep features computed from the data with a modified version of hidden Markov model (HMM) to flexibly model pose duration and summarize patient motion. The performance is evaluated in ideal (BC: bright and clear/occlusion-free) and natural (DO: dark and occluded) scenarios at two motion resolutions and in two environments: a mock-up and a medical ICU. The usage of deep features is evaluated and their performance compared with engineered features. Experimental results using deep features in DO scenes increase performance from $\text{86.7}\%$ to  $\text{93.6}\%$, while matching the classification performance of engineered features in BC scenes. The performance of MASH is compared with HMM and C3D. The overall overtime tracing and summarization error rate across all methods increased when transitioning from the mock-up to the the medical ICU data. The proposed keyframe estimation helps achieve a  $\text{78}\%$ transition classification accuracy.
Efficient and Robust Detection of Duplicate Videos in a Large Database

We present an efficient and accurate method for duplicate video detection in a large database using video fingerprints. We have empirically chosen the color layout descriptor, a compact and robust frame-based descriptor, to create fingerprints which are further encoded by vector quantization (VQ). We propose a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and experimentally show its superior performance over other distance measures for accurate duplicate detection. Efficient search cannot be performed for high-dimensional data using a nonmetric distance measure with existing indexing techniques. Therefore, we develop novel search algorithms based on precomputed distances and new dataset pruning techniques yielding practical retrieval times. We perform experiments with a database of 38 000 videos, worth 1600 h of content. For individual queries with an average duration of 60 s (about 50% of the average database video length), the duplicate video is retrieved in 0.032 s, on Intel Xeon with CPU 2.33 GHz, with a very high accuracy of 97.5%.
Interactive graph cut segmentation of touching neuronal structures from electron micrographs

A novel interactive segmentation framework comprising of a two stage s-t mincut is proposed. The framework has been designed keeping in mind the need to segment touching neuronal structures in Electron Micrograph (EM) images. The first stage undersegments the image, and groups touching structures into a single class. The second stage accepts user interaction to separate touching structures. The technique introduces user feedback through a Markov Random Field formulation. Furthermore, a method for constructing interaction potentials using an edge response function is proposed. Encouraging results, and a comparison to state of the art methods is presented.
Extracting Dynamic Microtubule Features from Image Sequences

Microtubules (MT) are filamentous cytoskeletal structures composed of tubulin protein subunits. Through their dynamic behaviors, they participate in many essential cellular functions. MT dynamics are traditionally analyzed from time lapse images by manual techniques that are laborious, approximate and often of limited analytical value. Recently, computational methods have been applied to the problems of detection and tracking of MTs in live cell images. In this paper, we discuss computational methods for extracting dynamics features from live cell MT image sequences. We provide examples of how these features can be used in MT dynamics analysis and improve the manual analytical capabilities.
Image segmentation using curve evolution

A novel scheme for image segmentation is presented. The technique is based on the integration of ideas from geodesic active contours and a previously proposed edgeflow segmentation. Given an image a 2-D vector is constructed at each pixel location. This vector points in the direction of potential boundary pixels. The computation of the 2-D vector field is based on image intensity, color and texture gradients. Following this, an initial curve is instantiated and propagated to separate the image into foreground and background regions. The curve propagation is guided by the above mentioned vector field. The proposed approach thus utilizes an edge-based segmentation method and extends traditional PDE based curve evolution methods to texture image segmentation, and avoids the post processing problems in edge linking and boundary detection.
Determining Achievable Rates for Secure, Zero Divergence, Steganography

In steganography (the hiding of data into innocuous covers for secret communication) it is difficult to estimate how much data can be hidden while still remaining undetectable. To measure the inherent detectability of steganography, Cachin suggested the ϵ-secure measure, where ϵ is the Kullback Leibler (K-L) divergence between the cover distribution and the distribution after hiding. At zero divergence, an optimal statistical detector can do no better than guessing; the data is undetectable. The hider's key question then is, what hiding rate can be used while maintaining zero divergence? Though work has been done on the theoretical capacity of steganography, it is often difficult to use these results in practice. We therefore examine the limits of a practical scheme known to allow embedding with zero-divergence. This scheme is independent of the embedding algorithm and therefore can be generically applied to find an achievable secure hiding rate for arbitrary cover distributions.
Variable Length Open Contour Tracking Using a Deformable Trellis

This paper focuses on contour tracking, an important problem in computer vision, and specifically on open contours that often directly represent a curvilinear object. Compelling applications are found in the field of bioimage analysis where blood vessels, dendrites, and various other biological structures are tracked over time. General open contour tracking, and biological images in particular, pose major challenges including scene clutter with similar structures (e.g., in the cell), and time varying contour length due to natural growth and shortening phenomena, which have not been adequately answered by earlier approaches based on closed and fixed end-point contours. We propose a model-based estimation algorithm to track open contours of time-varying length, which is robust to neighborhood clutter with similar structures. The method employs a deformable trellis in conjunction with a probabilistic (hidden Markov) model to estimate contour position, deformation, growth and shortening. It generates a maximum a posteriori estimate given observations in the current frame and prior contour information from previous frames. Experimental results on synthetic and real-world data demonstrate the effectiveness and performance gains of the proposed algorithm.
Quantitative analysis of immunofluorescent retinal images

We present a novel method to quantitatively analyze confocal microscope images of retinas. We automatically detect nuclei within the outer nuclear layer (ONL) in a retinal image. Based on nuclei detection results, we also automatically measure the thickness of the ONL and the local cell density within the ONL. These measurements provide the first thorough quantitative analysis of retinal images. Our results not only verify previous conclusions about retinal restructuring during detachment, but also provide biologists with significant information about the regional responses in the ONL
Stochastic and deterministic networks for texture segmentation

Several texture segmentation algorithms based on deterministic and stochastic relaxation principles, and their implementation on parallel networks, are described. The segmentation process is posed as an optimization problem and two different optimality criteria are considered. The first criterion involves maximizing the posterior distribution of the intensity field given the label field (maximum a posteriori estimate). The posterior distribution of the texture labels is derived by modeling the textures as Gauss Markov random fields (GMRFs) and characterizing the distribution of different texture labels by a discrete multilevel Markov model. A stochastic learning algorithm is proposed. This iterated hill-climbing algorithm combines fast convergence of deterministic relaxation with the sustained exploration of the stochastic algorithms, but is guaranteed to find only a local minimum. The second optimality criterion requires minimizing the expected percentage of misclassification per pixel by maximizing the posterior marginal distribution, and the maximum posterior marginal algorithm is used to obtain the corresponding solution. All these methods implemented on parallel networks can be easily extended for hierarchical segmentation; results of the various schemes in classifying some real textured images are presented.
Online parameter estimation in dynamic Markov Random Fields for image sequence analysis

Markov Random Fields (MRF) have proven to be extremely useful models for efficient and accurate image segmentation.Recent literature points to an increased effort towards incorporating useful priors (shape, geometry, context) in a MRF framework. However, topological priors, considered extremely crucial in biological and natural image sequences have been less explored. This work proposes a strategy wherein free parameters of the MRF are used to make it topology aware using a semantic graphical model working in conjunction with the MRF. Estimation of free parameters is constrained by prior knowledge of an object's topological dynamics encoded by the graphical model. Maximizing a regional conformance measure yields parameters for the frame under consideration. The application motivating this work is the tracing of neuronal structures across 3D serial section Transmission Electron Micrograph (ssTEM) stacks. Applicability of the proposed method is demonstrated by tracing 3D structures in ssTEM stacks.
Steganalysis of quantization index modulation data hiding

Quantization index modulation (QIM) techniques have been gaining popularity in the data hiding community because of their robustness and information-theoretic optimally against a large class of attacks. In this paper, we consider detecting the presence of QIM hidden data, which is an important consideration when data hiding is used for covert communication, or steganography. For a given host distribution, we are able to quantify detectability compactly in terms of a parameter related to the robustness of the hiding scheme to attacks. Using detection theory we show that QIM quickly transitions from easily detectable to virtually undetectable as this parameter varies. We also obtain performance benchmarks for QIM hiding in images, indicating that a scheme designed to be robust to say a moderate degree of JPEG compression, should be easily detectable. While practical application of detection theory to images is difficult because of statistical variations across images, we employ supervised learning to show that standard QIM schemes for images are indeed quite easily detectable. However, it remains an open issue as to whether it is possible to devise QIM variants that are less vulnerable to steganalysis.
Secure Steganography: Statistical Restoration of the Second Order Dependencies for Improved Security

We present practical approaches for steganography that can provide improved security by closely matching the second-order statistics of the host rather than just the marginal distribution. The methods are based on the framework of statistical restoration, wherein a fraction of the host symbols available for hiding is actually used to restore the statistics; thus reducing the rate, but providing security against steganalysis. We establish correspondence between steganography and the earth-mover's distance (EMD), a popular distance metric used in computer vision applications. The EMD framework can be used to define the optimum flow (modifications) of the host symbols for compensation. This formulation is used for image steganography by restoring the second-order statistics of the blockwise discrete cosine transform (DCT) coefficients. Some practical limitations of this approach (such as computational complexity and difficulty in dealing with overlapping coefficient pairs) are noted, and a new method is proposed that alleviates these deficiencies by identifying the coefficients to modify based on a local compensation criterion. Experimental results on several thousand natural images demonstrate the utility of the presented methods.
Progressive-resolution transmission and lossless compression of color images for digital image libraries

Progressive-resolution transmission is of significant practical importance for online image libraries. When combined with reversible image compression, it provides a particularly promising framework which not only contributes to lower storage overhead but also to smaller transmission costs. We propose an efficient lossless compression scheme for RGB color images. It consists of a modified reversible subband transformation which is followed by a reversible color decorrelation technique. Switching the (traditional) order of wavelet and spectral transform offers the opportunity to support progressive-resolution transmission of spectrally decorrelated wavelet coefficients without compromising the compression performance.
Image adaptive high volume data hiding based on scalar quantization

Information-theoretic analysis for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. We consider a suboptimal implementation of this prescription, with a view to hiding high volumes of data in images with low perceptual degradation. The three main findings are as follows. (i) Scalar quantization based data hiding schemes incur about 2 dB penalty from the optimal embedding strategy, which involves vector quantization of the host. (ii) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use local perceptual criteria in addition to information-theoretic guidelines. (iii) Powerful erasure and error correcting codes provide a flexible framework that allows the data-hider freedom of choice of where to embed without requiring synchronization between encoder and decoder.
Uniform and non-uniform breakdown in compressed air under lightning and switching impulse voltages

The authors describe measurements of breakdown potentials (V/sub 50/) in air over the pressure range of 0.09 to 0.6 MPa (absolute) under lightning impulse (1.2/50 mu s) and switching surge (100/2500 mu s and 250/2500 mu s) voltages for both positive and negative polarities in uniform fields (d=4 to 50 mm), and in nonuniform fields (5-mm-dia hemispherically ended rod-plane geometry) for gap lengths of 30, 40 and 50 mm. It is shown that the breakdown strength of air exhibits a slight saturation at pressures above 0.6 MPa under both types of voltages and polarities. Under uniform fields the V/sub 50/ for the switching surge voltage is generally lower than the corresponding lightning impulse data. Under nonuniform fields it is observed that, for a given gap, the critical pressure effect is seen for certain risetimes only. The breakdown voltage at any given gap also varies with the risetime of the waveform and appears to reach a maximum for about 100 mu s.<<ETX>>
Image segmentation using curve evolution and region stability

A novel scheme for image segmentation is presented An image segmentation criterion is proposed that gathers similar pixels together to form regions and creates boundaries between two dissimilar regions. This criterion is formulated as a cost function. This cost function is minimized by using gradient-descent methods, which leads to a curve evolution equation that segments the image. The proposed method generalizes previous methods to more complex similarity and distance measures and can be applied to vector valued images such as texture and color images.
Contour-based multisensor image registration

A contour-based multisensor image registration scheme which uses region boundaries and other strong edges as matching primitives is presented. Chain code correlation and other shape similarity criteria such as moments are used to match closed contours. For the open contours, salient segments such as corners are detected first and then used in the matching process. Experimental results on several sets of Landsat, SPOT, and Seasat images indicate that this approach is quite robust in matching the images with large rotation.<<ETX>>
Panoramic capturing and recognition of human activity

This paper presents a unified approach to human activity capturing and recognition. It targets applications such as a speaker walking, turning around, sitting and getting up from a chair in a classroom setting. A panoramic camera capturing system is designed for video capture. Virtual camera control outputs the region of interest (ROI) video that covers the speaker. Given an ROI sequence, the virtual camera control parameters are used for the recognition of activities like walking, and the motion parameters of each frame are used for the recognition of other activities like turning around, sitting down and getting up etc. For motion parameter based recognition, the likelihood of the motion parameters is represented using a multivariate Gaussian model. The temporal change of the likelihood is characterized using a continuous density hidden Markov model (HMM). Experimental results show that the method works well in recognizing the above mentioned human body activities.
Modeling object classes in aerial images using hidden Markov models

A canonical model is proposed for object classes in aerial images. This model is motivated by the observation that geographic regions of interest are characterized by collections of texture motifs corresponding to geographic processes. Furthermore, the spatial arrangement of the motifs is an important discriminating characteristic. In our approach, the states of a hidden Markov model (HMM) correspond to the texture motifs and the state transitions correspond to the spatial arrangement of the motifs. A one-dimensional approach reduces the computational complexity. The model is shown to be effective in characterizing objects of interest in spatial datasets in terms of their underlying texture motifs. The potential of the model for identifying the classes of unlabeled objects is demonstrated.
Reference-based probabilistic segmentation as non-rigid registration using Thin Plate Splines

In this paper we demonstrate the effectiveness of reference (or atlas)-based non-rigid registration for the segmentation of medical and biological imagery. In particular we introduce a segmentation functional exploiting feature information about the reference image and we minimize it with respect to the parameters of the non-rigid transformation, akin to a region-based maximum likelihood estimation process. The warping transformation is modeled using thin plate splines, which incorporate information about the global rigid motion and the non-rigid local displacements. Extensive experimental evaluations and comparisons with other segmentation techniques on a complex biological dataset are presented. The proposed algorithm outperforms the others in both classification rate and, in particular, localization accuracy.
Total Variation Denoising Via the Moreau Envelope

Total variation denoising is a nonlinear filtering method well suited for the estimation of piecewise-constant signals observed in additive white Gaussian noise. The method is defined by the minimization of a particular nondifferentiable convex cost function. This letter describes a generalization of this cost function that can yield more accurate estimation of piecewise constant signals. The new cost function involves a nonconvex penalty (regularizer) designed to maintain the convexity of the cost function. The new penalty is based on the Moreau envelope. The proposed total variation denoising method can be implemented using forward–backward splitting.
Laplace Random Vectors, Gaussian Noise, and the Generalized Incomplete Gamma Function

Wavelet domain statistical modeling of images has focused on modeling the peaked heavy-tailed behavior of the marginal distribution and on modeling the dependencies between coefficients that are adjacent (in location and/or scale). In this paper we describe the extension of the Laplace marginal model to the multivariate case so that groups of wavelet coefficients can be modeled together using Laplace marginal models. We derive the nonlinear MAP and MMSE shrinkage functions for a Laplace vector in Gaussian noise and provide computationally efficient approximations to them. The development depends on the generalized incomplete Gamma function.
A Subband Adaptive Iterative Shrinkage/Thresholding Algorithm

We investigate a subband adaptive version of the popular iterative shrinkage/thresholding algorithm that takes different update steps and thresholds for each subband. In particular, we provide a condition that ensures convergence and discuss why making the algorithm subband adaptive accelerates the convergence. We also give an algorithm to select appropriate update steps and thresholds for when the distortion operator is linear and time invariant. The results in this paper may be regarded as extensions of the recent work by Vonesch and Unser.
Magnitude squared design of recursive filters with the Chebyshev norm using a constrained rational Remez algorithm

We describe a Remez type exchange algorithm for the design of stable recursive filters for which the Chebyshev norm of H(/spl omega/)-F(/spl omega/) is minimized, where H(/spl omega/) and F(/spl omega/) are the realized and desired magnitude squared frequency responses. The number of poles and zeros can be chosen arbitrarily and the zeros do not have to lie on the unit circle. The algorithm allows us to design filters with non-conventional frequency responses with arbitrary weighting functions. It also gives optimal minimum phase FIR filters and elliptic recursive filters as special cases. We discuss three main difficulties in the use of the Remez algorithm for recursive filter design and give ways to overcome them.<<ETX>>