nils-herrmann/clean_scirepeval huggingface dataset:
Evidential reasoning for facial gestures recognition from cartoon images

Facial gesture analysis is a challenging subject for automatic recognition. There are uncertainties of the possible errors on feature extraction or of more than one gesture having similar facial features. To tackle this problem, a new approach for facial gesture analysis is proposed using evidential reasoning. Before any recognition, different expressions must be identified. The six universal expressions are used and tested. They are: anger, disgust, fear, happiness, sadness and surprise. In order to computerize them, a set of parameters is used to encode these expressions. A subset of FACS (the Facial Action Coding System) is selected and modified for this purpose. Cartoon drawings are designed and constructed for different expressions. Several facial marks are identified, and the relationships between them are measured. They are used as feature vectors for recognition purpose. In the recognition phase, the Dempster-Shafer theory of evidential reasoning is applied to combine the evidence as represented by the facial features. This approach generates hypotheses, given the set of facial evidence. It accumulates the corresponding confidence, and calculates the support and the plausibility of the resulting hypothesis. This study demonstrates the feasibility of applying Dempster-Shafer theory to facial gesture recognition. The performance is greatly affected by the number of FACS codes used to describe an expression.<<ETX>>
Indexing and retrieval of 3D models by unsupervised clustering with hierarchical SOM

A hierarchical indexing structure for 3D model retrieval based on the hierarchical self organizing map (HSOM) is proposed. The proposed approach organizes the database into a hierarchy so that head models are partitioned by coarse features initially and finer scale features are used in lower levels. The aim is to traverse a small subset of the database during retrieval. This is made possible by exploiting the multi-resolution capability of spherical wavelet features to successively approximate the salient characteristics of the head models, which are encoded in the form of weight vectors associated with the nodes at different levels (from coarse to fine) of the HSOM. To avoid premature commitment to a possibly erroneous model class, search is propagated from a subset of nodes at each level, which is selected based on a fuzzy membership measure between the query feature vector and weight vector, instead of taking the winner-take-all approach. Experiments show that, in addition to efficiency improvement, model retrieval based on the HSOM approach is able to achieve a much higher accuracy compared with the case where no indexing is performed.
An image computing system for the estimation and reconstruction of diffuse volume of an anti-cancer drug in liver tumours

Presents a medical image computing system which has been designed to extract and analyze quantitative information from a spatial sequence of CT (computed tomography) images. In particular, it has been applied to the quantitation and 3D reconstruction of the diffuse pattern of a radio-opaque anti-cancer drug, lipiodol-doxorubicin, in hepatocellular carcinoma. The PC-based system computes quantitative measures which can be used to assess the effectiveness of the chemotherapeutic treatment and to study the histology of the tumour. The system can provide a visualization of the tumour volume and quantification of drug absorption using 3D surface reconstruction and graphics techniques.<<ETX>>
Sliding Window Update Using Suffix Arrays

The sliding window dictionary-based algorithms of the Lempel-Ziv (LZ) 77 family are widely used for universal loss less data compression. The encoding component of these algorithms performs repeated sub string search. Data structures, such as hash tables, binary search trees, and suffix trees have been used to speedup these searches, at the expense of memory usage. Previous work has shown how suffix arrays (SA) can be used for dictionary representation and LZ77 decomposition. In this paper, we improve over that work by proposing a new efficient algorithm to update the sliding window each time a token is outputted. Our algorithm toggles between two SA on consecutive tokens. The resulting SA-based encoder requires less memory than tree-based encoders. We compare our technique against tree-based encoders, on a large set of benchmark files. In some cases, our encoder is also faster than tree-based encoders.
Group-sparse matrix recovery

We apply the OSCAR (octagonal selection and clustering algorithms for regression) in recovering group-sparse matrices (two-dimensional - 2D - arrays) from compressive measurements. We propose a 2D version of OSCAR (2OSCAR) consisting of the ℓ1 norm and the pair-wise ℓ∞ norm, which is convex but non-differentiable. We show that the proximity operator of 2OSCAR can be computed based on that of OSCAR. The 2OSCAR problem can thus be efficiently solved by state-of-the-art proximal splitting algorithms. Experiments on group-sparse 2D array recovery show that 2OSCAR regularization solved by the SpaRSA algorithm is the fastest choice, while the PADMM algorithm (with debiasing) yields the most accurate results.
Bayesian image segmentation using wavelet-based priors

This paper introduces a formulation which allows using wavelet-based priors for image segmentation. This formulation can be used in supervised, unsupervised, or semi-supervised modes, and with any probabilistic observation model (intensity, multispectral, texture). Our main goal is to exploit the well-known ability of wavelet-based priors to model piece-wise smoothness (which underlies state-of-the-art methods for denoising, coding, and restoration) and the availability of fast algorithms for wavelet-based processing. The main obstacle to using wavelet-based priors for segmentation is that they're aimed at representing real values, rather than discrete labels, as needed for segmentation. This difficulty is sidestepped by the introduction of real-valued hidden fields, to which the labels are probabilistically related. These hidden fields, being unconstrained and real-valued, can be given any type of spatial prior, such as one based on wavelets. Under this model, Bayesian MAP segmentation is carried out by a (generalized) EM algorithm. Experiments on synthetic and real data testify for the adequacy of the approach.
Sparse multinomial logistic regression: fast algorithms and generalization bounds

Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero. From a learning-theoretic perspective, these methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization. This paper presents three contributions related to learning sparse classifiers. First, we introduce a true multiclass formulation based on multinomial logistic regression. Second, by combining a bound optimization approach with a component-wise update procedure, we derive fast exact algorithms for learning sparse multiclass classifiers that scale favorably in both the number of training samples and the feature dimensionality, making them applicable even to large data sets in high-dimensional feature spaces. To the best of our knowledge, these are the first algorithms to perform exact multinomial logistic regression with a sparsity-promoting prior. Third, we show how nontrivial generalization bounds can be derived for our classifier in the binary case. Experimental results on standard benchmark data sets attest to the accuracy, sparsity, and efficiency of the proposed methods.
A velocity field approach to the detection of pedestrian interactions

The problem of detecting pedestrian interactions in image sequences is addressed. An extension of the motion field model to account for pedestrian interactions is presented. Pedestrian movements are modelled by a non-linear state model in discrete time, in which the trajectories follow a known field, in the absence of interactions, and include an extra term otherwise. The detection of interactions between pedestrians is reduced to a time varying parameter estimation problem, for which algorithms based on moving horizon estimation with a variable forgetting factor and on a Kalman filter are compared.
Network Inference From Co-Occurrences

The discovery of networks is a fundamental problem arising in numerous fields of science and technology, including communication systems, biology, sociology, and neuroscience. Unfortunately, it is often difficult, or impossible, to obtain data that directly reveal network structure, and so one must infer a network from incomplete data. This paper considers inferring network structure from "co-occurrence" data: observations that identify which network components (e.g., switches, routers, genes) carry each transmission but do not indicate the order in which they handle the transmission. Without order information, the number of networks that are consistent with the data grows exponentially with the size of the network (i.e., the number of nodes). Yet, the basic engineering/evolutionary principles underlying most networks strongly suggest that not all data-consistent networks are equally likely. In particular, nodes that co-occur in many observations are probably closely connected. With this in mind, we model the co-occurrence observations as independent realizations of a random walk on the network, subjected to a random permutation to account for the lack of order information. Treating permutations as missing data, we derive an expectation-maximization (EM) algorithm for estimating the random walk parameters. The model and EM algorithm significantly simplify the problem, but the computational complexity of the reconstruction process does grow exponentially in the length of each transmission path. For networks with long paths, the exact e-step may be computationally intractable. We propose a polynomial-time Monte Carlo EM algorithm based on importance sampling and derive conditions that ensure convergence of the algorithm with high probability. Simulations and experiments with Internet measurements demonstrate the promise of this approach.
Orientation in Manhattan: equiprojective classes and sequential estimation

The problem of inferring 3D orientation of a camera from video sequences has been mostly addressed by first computing correspondences of image features. This intermediate step is now seen as the main bottleneck of those approaches. In this paper, we propose a new 3D orientation estimation method for urban (indoor and outdoor) environments, which avoids correspondences between frames. The scene property exploited by our method is that many edges are oriented along three orthogonal directions; this is the recently introduced Manhattan world (MW) assumption. The main contributions of this paper are: the definition of equivalence classes of equiprojective orientations, the introduction of a new small rotation model, formalizing the fact that the camera moves smoothly, and the decoupling of elevation and twist angle estimation from that of the compass angle. We build a probabilistic sequential orientation estimation method, based on an MW likelihood model, with the above-listed contributions allowing a drastic reduction of the search space for each orientation estimate. We demonstrate the performance of our method using real video sequences.
Sequential and parallel image restoration: neural network implementations

Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.
Efficient unsupervised feature selection for sparse data

Feature selection and feature reduction are central problems in machine learning and pattern recognition. Many datasets have a sparse nature, that is, many features have zero value. For instance, in text classification based on the bag-of-words (BoW) or similar representations, there is usually a large number of features, many of which may be irrelevant (or even detrimental) for classification tasks. This paper proposes a new unsupervised feature selection method for sparse data, suitable for both standard and binarized representations. The method is applicable to supervised, semi-supervised, and unsupervised learning, since it does not use class labels. The experimental results on standard benchmarks show that the proposed method performs better than existing ones on numeric floating-point and binary feature. It yields efficient feature selection, reducing the number of features while simultaneously improving the classification accuracy.
Simultaneous feature selection and clustering using mixture models

Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters.
Class-adapted image compression using independent component analysis

This paper exploits independent component analysis (ICA) to obtain transform-based compression schemes adapted to specific image classes. This adaptation results from the data-dependent nature of the ICA bases, learnt from training images. Several coder architectures are evaluated and compared, according to both standard (SNR) and perceptual (picture quality scale - PQS) criteria, on two classes of images: faces and fingerprints. For fingerprint images, our coders perform close to the well-known special-purpose wavelet-based coder developed by the FBI. For face images, our ICA-based coders clearly outperform JPEG at the low bit-rates herein considered.
Adaptive Sparseness for Supervised Learning

The goal of supervised learning is to infer a functional mapping based on a set of training examples. To achieve good generalization, it is necessary to control the "complexity" of the learned function. In Bayesian approaches, this is done by adopting a prior for the parameters of the function being learned. We propose a Bayesian approach to supervised learning, which leads to sparse solutions; that is, in which irrelevant parameters are automatically set exactly to zero. Other ways to obtain sparse classifiers (such as Laplacian priors, support vector machines) involve (hyper)parameters which control the degree of sparseness of the resulting classifiers; these parameters have to be somehow adjusted/estimated from the training data. In contrast, our approach does not involve any (hyper)parameters to be adjusted or estimated. This is achieved by a hierarchical-Bayes interpretation of the Laplacian prior, which is then modified by the adoption of a Jeffreys' noninformative hyperprior. Implementation is carried out by an expectation-maximization (EM) algorithm. Experiments with several benchmark data sets show that the proposed approach yields state-of-the-art performance. In particular, our method outperforms SVMs and performs competitively with the best alternative techniques, although it involves no tuning or adjustment of sparseness-controlling hyperparameters.
Blind image deblurring with unknown boundaries using the alternating direction method of multipliers

Blind image deblurring (BID) is an ill-posed inverse problem, typically solved by imposing some form of regularization (prior knowledge) on the unknown blur and original image. A recent approach, although not requiring prior knowledge on the blurring filter, achieves state-of-the-art performance for a wide range of real-world BID problems. We propose a new version of that method, in which both the optimization problems with respect to the unknown image and with respect to the unknown blur are solved by the alternating direction method of multipliers (ADMM) - an optimization tool that has recently sparked much interest for solving inverse problems, namely due to its modularity and state-of-the-art speed. Our approach also handles seamlessly the realistic case of blind deblurring with unknown boundary conditions. Experiments with synthetic and real blurred images show the competitiveness of the proposed method, both in terms of speed and restoration quality.
An EM algorithm for wavelet-based image restoration

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(N log N) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.
Fast wavelet-based image deconvolution using the EM algorithm

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. The observed image is assumed to be a convolved and noisy version of the original image. The restoration process promotes a low-complexity reconstruction expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Although similar formulations have been considered in previous work, the resulting optimization problems have been computationally demanding. The EM algorithm herein proposed combines the efficient image representation offered by the wavelet transform (DWT) with the diagonalization of the convolution operator provided by the FFT. The algorithm alternates between an FFT-based E-step and a DWT-based M-step, resulting in an efficient iterative process requiring O(NlogN) operations per iteration.
2D Shape Recognition Using Information Theoretic Kernels

In this paper, a novel approach for contour based 2D shape recognition is proposed, using a class of information theoretic kernels recently introduced. This kind of kernels, based on a non-extensive generalization of the classical Shannon information theory, are defined on probability measures. In the proposed approach, chain code representations are first extracted from the contours; then n-gram statistics are computed and used as input to the information theoretic kernels. We tested different versions of such kernels, using support vector machine and nearest neighbor classifiers. An experimental evaluation on the Chicken pieces dataset shows that the proposed approach significantly outperforms the current state-of-the-art methods.
Trajectory analysis in natural images using mixtures of vector fields

This work introduces a new approach to modeling object trajectories in image sequences. Trajectories performed by natural objects (e.g., people, animals) typically depend on the position of each object in the scene and can change in an unpredictable way. Despite this diversity, there is often a small number of typical motion patterns based on which it is possible to explain all the observed trajectories. To achieve this goal, we model each of these motion patterns using a motion field and allow objects to switch between fields in a space-varying, possible probabilistic, way. Our approach provides a space-dependent motion model which can be estimated using an expectation-maximization (EM) algorithm. Experiments with both synthetic and real data are presented to illustrate the ability of the proposed approach in modeling different motion patterns.
Image compression using orthogonalized independent components bases

In this paper we address the orthogonalization of independent component analysis (ICA) to obtain transform-based image coders. We consider several classes of training images, from which we extract the independent components, followed by orthogonalization, obtaining bases for image coding. Experimental tests show the generalization ability of ICA of natural images, and the adaptation ability to specific classes. The proposed fixed size block coders have lower transform complexity than JPEG. They outperform JPEG, on several classes of images, for a given range of compression ratios, according to both standard (SNR) and perceptual (picture quality scale - PQS) measures. For some image classes, the visual quality of the images obtained with our coders is similar to that obtained by JPEG2000, which is currently the state of the art still image coder. On fingerprint images, our fixed and variable size block coders perform competitively with the special-purpose wavelet-based coder developed by the FBI.