nils-herrmann/clean_scirepeval huggingface dataset:
3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions

A mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliable segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.
Inferring body pose without tracking body parts

A novel approach for estimating articulated body posture and motion from monocular video sequences is proposed. Human pose is defined as the instantaneous two dimensional configuration (i.e. the projection onto the image plane) of a single articulated body in terms of the position of a predetermined sets of joints. First, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. The goal is to be able to map these generally low level visual features to body configurations. The system estimates different mappings, each one with a specific cluster in the visual feature space. Given a set of body motion sequences for training, unsupervised clustering is obtained via the Expectation Maximization algorithm. For each of the clusters, a function is estimated to build the mapping between low-level features to 2D pose. Given new visual features, a mapping from each cluster is performed to yield a set of possible poses. From this set, the system selects the most likely pose given the learned probability distribution and the visual feature of the proposed approach is characterized using real and artificially generated body postures, showing promising results.
Exploiting phonological constraints for handshape inference in ASL video

Handshape is a key linguistic component of signs, and thus, handshape recognition is essential to algorithms for sign language recognition and retrieval. In this work, linguistic constraints on the relationship between start and end handshapes are leveraged to improve handshape recognition accuracy. A Bayesian network formulation is proposed for learning and exploiting these constraints, while taking into consideration inter-signer variations in the production of particular handshapes. A Variational Bayes formulation is employed for supervised learning of the model parameters. A non-rigid image alignment algorithm, which yields improved robustness to variability in handshape appearance, is proposed for computing image observation likelihoods in the model. The resulting handshape inference algorithm is evaluated using a dataset of 1500 lexical signs in American Sign Language (ASL), where each lexical sign is produced by three native ASL signers.
Modal Matching for Correspondence and Recognition

Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2D contour and point feature data are shown, and recognition experiments with 2D images of hand tools and airplanes are described.
Fast, reliable head tracking under varying illumination

An improved technique for 3D head tracking under varying illumination conditions is proposed. The head is modeled as a texture mapped cylinder. Tracking is formulated as an image registration problem in the cylinder's texture map image. To solve the registration problem in the presence of lighting variation and head motion, the residual error of registration is modeled as a linear combination of texture warping templates and orthogonal illumination templates. Fast and stable on-line tracking is then achieved via regularized weighted least squares minimization of the registration error. The regularization term tends to limit potential ambiguities that arise in the warping and illumination templates. Tracking does not require a precise initial of the model; the system is initialized automatically using a simple 2D face detector. The only assumption is that the target is facing the camera in the first frame of the sequence. Experiments in tracking are reported.
Closed-form solutions for physically-based shape modeling and recognition

An efficient, physically based solution for recovering a 3-D solid model from collections of 3-D surface measurements is presented. Given a sufficient number of independent measurements, the solution is overconstrained and unique except for rotational symmetries. A physically based object recognition method that allows simple, closed-form comparisons of recovered 3-D solid models is given. The performance of these methods is evaluated using both synthetic and real laser rangefinder data.<<ETX>>
Tracking, analysis, and recognition of human gestures in video

An overview of research in automated gesture spotting, tracking and recognition by the Image and Video Computing Group at Boston University is given. Approaches for localization and tracking human hands in video, estimation of hand shape and upper body pose; tracking head and facial motion, as well as efficient spotting and recognition of specific gestures in video streams are summarized. Methods for efficient dimensionality reduction of gesture time series, boosting of classifiers for nearest neighbor search in pose space, and model-based pruning of gesture alignment hypotheses are described. Algorithms are demonstrated in three domains: American sign language, hand signals like those employed by flight-directors on airport runways, and gesture-based interfaces for severely disabled users. The methods described are general and can be applied in other domains that require efficient detection and analysis of patterns in time-series, images or video.
Closed-Form Solutions for Physically Based Shape Modeling and Recognition

The authors present a closed-form, physically based solution for recovering a three-dimensional (3-D) solid model from collections of 3-D surface measurements. Given a sufficient number of independent measurements, the solution is overconstrained and unique except for rotational symmetries. The proposed approach is based on the finite element method (FEM) and parametric solid modeling using implicit functions. This approach provides both the convenience of parametric modeling and the expressiveness of the physically based mesh formulation and, in addition, can provide great accuracy at physical simulation. A physically based object-recognition method that allows simple, closed-form comparisons of recovered 3-D solid models is presented. The performance of these methods is evaluated using both synthetic range data with various signal-to-noise ratios and using laser rangefinder data.
Fast globally optimal 2D human detection with loopy graph models

This paper presents an algorithm for recovering the globally optimal 2D human figure detection using a loopy graph model. This is computationally challenging because the time complexity scales exponentially in the size of the largest clique in the graph. The proposed algorithm uses Branch and Bound (BB) to search for the globally optimal solution. The algorithm converges rapidly in practice and this is due to a novel method for quickly computing tree based lower bounds. The key idea is to recycle the dynamic programming (DP) tables associated with the tree model to look up the tree based lower bound rather than recomputing the lower bound from scratch. This technique is further sped up using Range Minimum Query data structures to provide O(1) cost for computing the lower bound for most iterations of the BB algorithm. The algorithm is evaluated on the Iterative Parsing dataset and it is shown to run fast empirically.
A pyramid approach to subpixel registration based on intensity

We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes (three-dimensional). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for nonlinear least-square optimization. The geometric deformation model is a global three-dimensional (3-D) affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.
Model-Based Estimation of 3-D Stiffness Parameters in Photonic-Force Microscopy

We propose a system to characterize the 3-D diffusion properties of the probing bead trapped by a photonic-force microscope. We follow a model-based approach, where the model of the dynamics of the bead is given by the Langevin equation. Our procedure combines software and analog hardware to measure the corresponding stiffness matrix. We are able to estimate all its elements in real time, including off-diagonal terms. To achieve our goal, we have built a simple analog computer that performs a continuous preprocessing of the data, which can be subsequently digitized at a much lower rate than is otherwise required. We also provide an effective numerical algorithm for compensating the correlation bias introduced by a quadrant photodiode detector in the microscope. We validate our approach using simulated data and show that our bias-compensation scheme effectively improves the accuracy of the system. Moreover, we perform experiments with the real system and demonstrate real-time capabilities. Finally, we suggest a simple adjunction that would allow one to determine the mass matrix as well.
Myocardial perfusion assessment by use of system identification method in a one-compartment model

Cardiovascular magnetic resonance has been shown to provide high data quality for myocardial perfusion assessment. However, to analyze the perfusion data, some signal processing and modeling is needed to correct for motion related artifacts and limited spatial resolution. This study describes a method based on system identification, allowing, after a first step of image registration, to integrate and correct the partial volume effect in the myocardium perfusion quantification. This method is then applied to patients with coronary artery disease or hypertrophic obstructive cardiomyopathy.
On the Linearity of Bayesian Interpolators for Non-Gaussian Continuous-Time AR(1) Processes

Bayesian estimation problems involving Gaussian distributions often result in linear estimation techniques. Nevertheless, there are no general statements as to whether the linearity of the Bayesian estimator is restricted to the Gaussian case. The two common strategies for non-Gaussian models are either finding the best linear estimator or numerically evaluating the Bayesian estimator by Monte Carlo methods. In this paper, we focus on Bayesian interpolation of non-Gaussian first-order autoregressive (AR) processes where the driving innovation can admit any symmetric infinitely divisible distribution characterized by the Lévy-Khintchine representation theorem. We redefine the Bayesian estimation problem in the Fourier domain with the help of characteristic forms. By providing analytic expressions, we show that the optimal interpolator is linear for all symmetric -stable distributions. The Bayesian interpolator can be expressed in a convolutive form where the kernel is described in terms of exponential splines. We also show that the limiting case of Lévy-type AR(1) processes, the system of which has a pole at the origin, always corresponds to a linear Bayesian interpolator made of a piecewise linear spline, irrespective of the innovation distribution. Finally, we show the two mentioned cases to be the only ones within the family for which the Bayesian interpolator is linear.
Elastic registration of biological images using vector-spline regularization

We present an elastic registration algorithm for the alignment of biological images. Our method combines and extends some of the best techniques available in the context of medical imaging. We express the deformation field as a B-spline model, which allows us to deal with a rich variety of deformations. We solve the registration problem by minimizing a pixelwise mean-square distance measure between the target image and the warped source. The problem is further constrained by way of a vector-spline regularization which provides some control over two independent quantities that are intrinsic to the deformation: its divergence, and its curl. Our algorithm is also able to handle soft landmark constraints, which is particularly useful when parts of the images contain very little information or when its repartition is uneven. We provide an optimal analytical solution in the case when only landmarks and smoothness considerations are taken into account. We have applied our approach to perform the elastic registration of images such as electrophoretic gels and fly embryos. The validation of the results by experts has been favorable in all cases.
Snakes With an Ellipse-Reproducing Property

We present a new class of continuously defined parametric snakes using a special kind of exponential splines as basis functions. We have enforced our bases to have the shortest possible support subject to some design constraints to maximize efficiency. While the resulting snakes are versatile enough to provide a good approximation of any closed curve in the plane, their most important feature is the fact that they admit ellipses within their span. Thus, they can perfectly generate circular and elliptical shapes. These features are appropriate to delineate cross sections of cylindrical-like conduits and to outline bloblike objects. We address the implementation details and illustrate the capabilities of our snake with synthetic and real data.
Unwarping of unidirectionally distorted EPI images

Echo-planar imaging (EPI) is a fast nuclear magnetic resonance imaging (MRI) method. Unfortunately, local magnetic field inhomogeneities induced mainly by the subject's presence cause significant geometrical distortion, predominantly along the phase-encoding direction, which must be undone to allow for meaningful further processing. So far, this aspect has been too often neglected. In this paper, the authors suggest a new approach using an algorithm specifically developed for the automatic registration of distorted EPI images with corresponding anatomically correct MRI images. They model the deformation field with splines, which gives us a great deal of flexibility, while comprising the affine transform as a special case. The registration criterion is least squares. Interestingly, the complexity of its evaluation does not depend on the resolution of the control grid. The spline model gives the authors good accuracy thanks to its high approximation order. The short support of splines leads to a fast algorithm. A multiresolution approach yields robustness and additional speedup. The algorithm was tested on real as well as synthetic data, and the results were compared with a manual method. A wavelet-based Sobolev-type random deformation generator was developed for testing purposes. A blind test indicates that the proposed automatic method is faster, more reliable, and more precise than the manual one.
Snakuscules

A snakuscule (a minuscule snake) is the simplest active contour that we were able to design while keeping the quintessence of traditional snakes: an energy term governed by the data, and a regularization term. Our construction is an area-based snake, as opposed to curve-based snakes. It is parameterized by just two points, thus further easing requirements on the optimizer. Despite their ultimate simplicity, snakuscules retain enough versatility to be employed for solving various problems such as cell counting and segmentation of approximately circular features. In this paper, we detail the design process of a snakuscule and illustrate its usefulness through practical examples. We claim that our didactic intentions are well served by the simplicity of snakuscules.
Linear interpolation revitalized

We present a simple, original method to improve piecewise-linear interpolation with uniform knots: we shift the sampling knots by a fixed amount, while enforcing the interpolation property. We determine the theoretical optimal shift that maximizes the quality of our shifted linear interpolation. Surprisingly enough, this optimal value is nonzero and close to 1/5. We confirm our theoretical findings by performing several experiments: a cumulative rotation experiment and a zoom experiment. Both show a significant increase of the quality of the shifted method with respect to the standard one. We also observe that, in these results, we get a quality that is similar to that of the computationally more costly "high-quality" cubic convolution.
Optimization of mutual information for multiresolution image registration

We propose a new method for the intermodal registration of images using a criterion known as mutual information. Our main contribution is an optimizer that we specifically designed for this criterion. We show that this new optimizer is well adapted to a multiresolution approach because it typically converges in fewer criterion evaluations than other optimizers. We have built a multiresolution image pyramid, along with an interpolation process, an optimizer, and the criterion itself, around the unifying concept of spline-processing. This ensures coherence in the way we model data and yields good performance. We have tested our approach in a variety of experimental conditions and report excellent results. We claim an accuracy of about a hundredth of a pixel under ideal conditions. We are also robust since the accuracy is still about a tenth of a pixel under very noisy conditions. In addition, a blind evaluation of our results compares very favorably to the work of several other researchers.
An efficient mutual information optimizer for multiresolution image registration

We propose a new optimizer in the context of multimodal image registration. The optimized criterion is the mutual information between the images to be align. This criterion requires that their joint histogram be available. For its computation, we introduce differentiable and separable Parzen windows that satisfy the partition of unity. Along with a continuous model of the images based on splines, this allows us to derive exact and tractable expressions for the gradient and the Hessian of the criterion. Then, we develop an optimizer based on the Marquardt-Levenberg (1963) strategy. Our new optimizer is specific to mutual information, in the same sense that Marquardt-Levenberg is specific to least-squares. We show that our optimizer is particularly well-adapted to an iterative coarse-to fine approach. We validate its accuracy by comparing its performance to that of several results available in the literature.
REGULARIZED INTERPOLATION FOR NOISY DATA

Interpolation is a vital tool in biomedical signal processing. Although there exists a substantial literature dedicated to noise-free conditions, much less is known in the presence of noise. Here, we document the breakdown of standard interpolation for noisy data and study the performance improvement due to regularized interpolation. In particular, we numerically investigate the Tikhonov (quadratic) regularization. On top of that, we explore non-quadratic regularization and show that this yields further improvements. We derive a novel bounded regularization approach to determine the optimal solution. We justify our claims with experimental results.
Consistent and regularized magnification of images

Because more output data must be created than is available from the input, magnification is an ill-posed problem. Traditional magnification relies on resampling an interpolation model at the appropriate rate; unfortunately, this simple solution is blind to the presence of the analog filter that was implicitly present when the samples of the function to be magnified were acquired. Consistent resampling has been introduced to take this into account, but it turns out that this solution is still under-constrained. In this paper, we propose regularization as a way to devise a deterministic magnification method that fully satisfies consistency constraints in the absence of noise, and at the same time that produces an output that best fulfills a wide class of criteria for regularity. Contrarily to many other methods, ours has been designed without ever leaving the continuous domain. We conduct experiments that show the benefit of our approach.
Interpolation revisited [medical images application]

Based on the theory of approximation, this paper presents a unified analysis of interpolation and resampling techniques. An important issue is the choice of adequate basis functions. The authors show that, contrary to the common belief, those that perform best are not interpolating. By opposition to traditional interpolation, the authors call their use generalized interpolation; they involve a prefiltering step when correctly applied. The authors explain why the approximation order inherent in any basis function is important to limit interpolation artifacts. The decomposition theorem states that any basis function endowed with approximation order ran be expressed as the convolution of a B spline of the same order with another function that has none. This motivates the use of splines and spline-based functions as a tunable way to keep artifacts in check without any significant cost penalty. The authors discuss implementation and performance issues, and they provide experimental evidence to support their claims.