nils-herrmann/clean_scirepeval huggingface dataset:
A Factorized Variational Technique for Phase Unwrapping in Markov Random Field

Some types of medical and topographic imaging device produce images in which the pixel values are "phase-wrapped", i.e., measured modulus a known scalar. Phase unwrapping can be viewed as the problem of inferring the integer number of relative shifts between each and every pair of neighboring pixels, subject tO an a priori preference for smooth surfaces, and a zero curl constraint, which requires that the shifts must sum to 0 around every loop. We formulate phase unwrapping as a probabilistic inference problem in a Markov random field where the prior favors the zero curl constraint. We derive a relaxed, factorized variational method that infers approximations to the marginal probabilities of the integer shifts between pairs of neighboring pixels. The original, unwrapped image can then be obtained by integrating the integer shifts. We compare our mean field technique with the least squares method on a synthetic 100 × 100 image, and give results on a larger 512 × 512 image measured using synthetic aperature radar from Sandia National Laboratories.
Learning flexible sprites in video layers

We propose a technique for automatically learning layers of "flexible sprites" (probabilistic 2-dimensional appearance maps and masks of moving, occluding objects). The model explains each input image as a layered composition of flexible sprites. A variational expectation maximization algorithm is used to learn a mixture of sprites from a video sequence. For each input image, probabilistic inference is used to infer the sprite class, translation, mask values and pixel intensities (including obstructed pixels) in each layer. Exact inference is intractable, but we show how a variational inference technique can be used to process 320/spl times/240 images at 1 frame/second. The only inputs to the learning algorithm are the video sequence, the number of layers and the number of flexible sprites. We give results on several tasks, including summarizing a video sequence with sprites, point-and-click video stabilization, and point-and-click object removal.
Very loopy belief propagation for unwrapping phase images

Since the discovery that the best error-correcting decoding algorithm can be viewed as belief propagation in a cycle-bound graph, researchers have been trying to determine under what circumstances "loopy belief propagation" is effective for probabilistic inference. Despite several theoretical advances in our understanding of loopy belief propagation, to our knowledge, the only problem that has been solved using loopy belief propagation is error-correcting decoding on Gaussian channels. We propose a new representation for the two-dimensional phase unwrapping problem, and we show that loopy belief propagation produces results that are superior to existing techniques. This is an important result, since many imaging techniques, including magnetic resonance imaging and interfer-ometric synthetic aperture radar, produce phase-wrapped images. Interestingly, the graph that we use has a very large number of very short cycles, supporting evidence that a large minimum cycle length is not needed for excellent results using belief propagation.
Enforcing integrability for surface reconstruction algorithms using belief propagation in graphical models

Accurate calculation of the three dimensional shape of an object is one of the classic research areas of computer vision. Many of the existing methods are based on surface normal estimation, and subsequent integration of surface gradients. In general, these methods do not produce valid surfaces due to violation of surface integrability. We introduce a new method for shape reconstruction by integration of valid surface gradient maps. The essence of the new approach is in the strict enforcement of the surface integrability via belief propagation across graphical models. The graphical model is selected in such a way as to extract information from underlying, possibly noisy, surface gradient estimators, utilize the surface integrability constraint, and produce the maximum a-posteriori estimate of a valid surface. We demonstrate the algorithm for two classic shape reconstruction techniques; shape-from-shading and photometric stereo. On a set of real and synthetic examples, the new approach is shown to be fast and accurate, in the sense that shape can be rendered even in the presence of high levels of noise and sharp occlusion boundaries.
ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition

A challenging, unsolved problem in the speech recognition community is recognizing speech signals that are corrupted by loud, highly nonstationary noise. One approach to noisy speech recognition is to automatically remove the noise from the cepstrum sequence before feeding it in to a clean speech recognizer. In previous work published in Eurospeech, we showed how a probability model trained on clean speech and a separate probability model trained on noise could be combined for the purpose of estimating the noise-free speech from the noisy speech. We showed how an iterative 2nd order vector Taylor series approximation could be used for probabilistic inference in this model. In many circumstances, it is not possible to obtain examples of noise without speech. Noise statistics may change significantly during an utterance, so that speech-free frames are not sufficient for estimating the noise model. In this paper, we show how the noise model can be learned even when the data contains speech. In particular, the noise model can be learned from the test utterance and then used to denoise the test utterance. The approximate inference technique is used as an approximate E step in a generalized EM algorithm that learns the parameters of the noise model from a test utterance. For both Wall Street Journal data with added noise samples and the Aurora benchmark, we show that the new noise adaptive technique performs as well as or significantly better than the non-adaptive algorithm, without the need for a separate training set of noise examples.
Towards non-stationary model-based noise adaptation for large vocabulary speech recognition

Recognition rates of speech recognition systems are known to degrade substantially when there is a mismatch between training and deployment environments. One approach to tackling this problem is to transform the acoustic models based on the channel distortion and noise characteristics of the new environment. Currently, most model adaptation strategies assume that the noise characteristics are stationary. We present results for using multiple noise distributions for the Whisper large vocabulary speech recognition system. The vector Taylor series method for adaptation of the distributions is used, and either a weighted average of the noise states or the locally best noise states is used. Our results indicate that for certain types of noise, significant gains in recognition accuracy can be achieved.
Joint estimation of noise and channel distortion in a generalized EM framework

The performance of speech cleaning and noise adaptation algorithms is heavily dependent on the quality of the noise and channel models. Various strategies have been proposed in the literature for adapting to the current noise and channel conditions. We describe the joint learning of noise and channel distortion in a novel framework called ALGONQUIN. The learning algorithm employs a generalized EM strategy wherein the E step is approximate. We discuss the characteristics of the new algorithm, with a focus on convergence rates and parameter initialization. We show that the learning algorithm can successfully disentangle the non-linear effects of noise and linear effects of the channel and achieve a relative reduction in WER of 21.8% over the non-adaptive algorithm.
Learning Dynamic Noise Models from Noisy Speech for Robust Speech Recognition

A challenging, unsolved problem in the speech recognition community is recognizing speech signals that are corrupted by loud, highly nonstationary noise. One approach to noisy speech recognition is to automatically remove the noise from the cepstrum sequence before feeding it in to a clean speech recognizer. In previous work published in Eurospeech, we showed how a probability model trained on clean speech and a separate probability model trained on noise could be combined for the purpose of estimating the noisefree speech from the noisy speech. We showed how an iterative 2nd order vector Taylor series approximation could be used for probabilistic inference in this model. In many circumstances, it is not possible to obtain examples of noise without speech. Noise statistics may change signi cantly during an utterance, so that speechfree frames are not suAEcient for estimating the noise model. In this paper, we show how the noise model can be learned even when the data contains speech. In particular, the noise model can be learned from the test utterance and then used to denoise the test utterance. The approximate inference technique is used as an approximate E step in a generalized EM algorithm that learns the parameters of the noise model from a test utterance. For both Wall Street Journal data with added noise samples and the Aurora benchmark, we show that the new noise adaptive technique performs as well as or signi cantly better than the non-adaptive algorithm, without the need for a separate training set of noise examples.
Transformation-Invariant Clustering and Dimensionality Reduction Using EM

Clustering and dimensionality reduction are simple, effective ways to derive useful representations of data, such as images. These procedures often are used as preprocessing steps for more sophisticated pattern analysis techniques. (In fact, these procedures often perform as well as or better than more sophisticated pattern analysis techniques.) However, in situations where each input has been randomly transformed (e.g., by translation, rotation and shearing in images), these methods tend to extract cluster centers and submanifolds that account for variations in the input due to transformations, instead of more interesting and potentially useful structure. For example, if images of a human face are clustered, it would be more useful for the different clusters to represent different poses and expressions, instead of different translations and rotations. We describe a way to add transformation invariance to mixture models, factor analyzers and mixtures of factor analyzers by approximating the nonlinear transformation manifold by a discrete set of points. In contrast to linear approximations of the transformation manifold, which assume the amount of transformation is small, our method works well for large levels of transformation. We show how the expectation maximization algorithm can be used to jointly learn a set of clusters, a subspace model, or a mixture of subspace models and at the same time infer the transformation associated with each case. After illustrating this technique on some difficult contrived problems, we compare the technique with other methods for filtering noisy images obtained from a scanning electron microscope, clustering images of faces into different categories of identification and pose, subspace modeling of facial expressions, subspace modeling of images of handwritten digits for handwriting classification, and unsupervised classification of images of handwritten digits.
Signal-space characterization of iterative decoding

By tracing the flow of computations in the iterative decoders for low-density parity-check codes, we formulate a signal-space view for a finite number of iterations in a finite-length code. On a Gaussian channel, maximum a posteriori (MAP) codeword decoding (or "maximum-likelihood decoding") decodes to the codeword signal that is closest to the channel output in Euclidean distance. In contrast, we show that iterative decoding decodes to the "pseudosignal" that has highest correlation with the channel output. The set of pseudosignals corresponds to "pseudocodewords", only a vanishingly small number of which correspond to codewords. We show that some pseudocodewords cause decoding errors, but that there are also pseudocodewords that frequently correct the deleterious effects of other pseudocodewords.
Unwrapping phases by relaxed mean field inference

Some types of medical and topographic imaging devices produce images in which the pixel values are "phase-wrapped", i.e., the measured modulus is a known scalar. Phase unwrapping can be viewed as the problem of inferring the number of shifts between each and every pair of neighboring pixels, subject to an a priori preference for smooth surfaces, and subject to a zero curl constraint, which requires that the shifts must sum to 0 around every loop. We formulate phase unwrapping as a mean field inference problem in a probability model, where the prior favors the zero curl constraint. We compare our mean field technique with the least squares method on a synthetic 100/spl times/100 image, and give results on a larger 512/spl times/512 image.
Product Analysis: Learning to Model Observations as Products of Hidden Variables

Factor analysis and principal components analysis can be used to model linear relationships between observed variables and linearly map high-dimensional data to a lower-dimensional hidden space. In factor analysis, the observations are modeled as a linear combination of normally distributed hidden variables. We describe a nonlinear generalization of factor analysis, called "product analysis", that models the observed variables as a linear combination of products of normally distributed hidden variables. Just as factor analysis can be viewed as unsupervised linear regression on unobserved, normally distributed hidden variables, product analysis can be viewed as unsupervised linear regression on products of unobserved, normally distributed hidden variables. The mapping between the data and the hidden space is nonlinear, so we use an approximate variational technique for inference and learning. Since product analysis is a generalization of factor analysis, product analysis always finds a higher data likelihood than factor analysis. We give results on pattern recognition and illumination-invariant image clustering.
"Codes" on images and iterative phase unwrapping

Many imaging techniques, including magnetic resonance imaging and interferometric synthetic aperture radar, produce "phase-wrapped" images. In a phase-wrapped image, the original image values are measured modulus a known wavelength, A. The goal of phase unwrapping is to produce an estimate of the original image using an a priori preference for smooth images. We formulate phase unwrapping as the problem of computing a vector field that is an estimate of the gradient field of the original image. A preference for smooth images is obtained using a Gaussian prior on the vector field. For a vector field to be a gradient field, it must satisfy the constraint that the sum of the vectors around every closed loop is zero. We enforce this constraint using "zero-curl checks" in a factor graph on the vector field. The sum-product algorithm in this factor graph is used to approximately compute the posterior probabilities of the vectors. Hard decisions are used to produce a vector field, which is integrated to obtain the unwrapped image. Experimental results show that this method can work significantly better than existing techniques for phase unwrapping. Although phase unwrapping for general image priors is NP-hard, we conjecture that the sum-product algorithm in an appropriate factor graph will lead to a near-optimal unwrapping algorithm for Gaussian process sources.
Unwrapping phase images by propagating probabilities across graphs

Phase images are derived from source images by applying a modulus operation to each pixel value. Phase unwrapping is the problem of inferring the original, unwrapped values from the wrapped values, using prior knowledge about the smoothness of the image. One approach to solving this problem is to infer the gradient vector field of the unwrapped image and then integrate the gradient field. The gradient in a particular direction at a pixel is equal to the observed pixel difference plus an unknown integer number of shifts. We introduce a technique for inferring these shifts using the low-complexity probability propagation algorithm, applied in a graphical model that prefers shifts that match the phase image and that constrains the shifts to satisfy the properties of a gradient field. We present results for a phase image from the region of the Sandia National Laboratories.
Filling in scenes by propagating probabilities through layers and into appearance models

Inferring the identities and positions of multiple occluding objects in a noisy image is a difficult problem, even when the shapes and appearances of the allowable objects are known. Methods that detect and analyze shape features, occlusion boundaries and optical flow break down when the image is noisy. In situations where we know the boundaries and appearances of the allowable objects, a brute force method can be used to perform MAP inference. If there are K possible objects (including translations, etc.) in up to L layers, the number of possible configurations of the scene is K/sup L/, so exact inference is intractable for large numbers of objects and reasonably large numbers of layers. We construct a Bayesian network that describes the occlusion process and we use iterative probability propagation to approximately recover the identities and positions of the objects in the scene in time that is linear in K and L. Although iterative probability propagation is an approximate inference technique, it was recently used to obtain the world record in error-correcting decoding. Experiments show that when one explanation of the scene is most probable, the algorithm finds the solution. For a small problem, we show that as the number of iterations increases, iterative probability propagation performs better than a greedy technique and becomes closer to the exact MAP algorithm. Quite surprisingly, we also find that when the order of occlusion is ambiguous, the output of the algorithm may oscillate between plausible interpretations of the scene.
Accumulator Networks: Suitors of Local Probability Propagation

One way to approximate inference in richly-connected graphical models is to apply the sum-product algorithm (a.k.a. probability propagation algorithm), while ignoring the fact that the graph has cycles. The sum-product algorithm can be directly applied in Gaussian networks and in graphs for coding, but for many conditional probability functions - including the sigmoid function - direct application of the sum-product algorithm is not possible. We introduce "accumulator networks" that have low local complexity (but exponential global complexity) so the sum-product algorithm can be directly applied. In an accumulator network, the probability of a child given its parents is computed by accumulating the inputs from the parents in a Markov chain or more generally a tree. After giving expressions for inference and learning in accumulator networks, we give results on the "bars problem" and on the problem of extracting translated, overlapping faces from an image.
Learning Sparse Multiple Cause Models

Multiple cause models (MCM) are a way to describe patterns as a superposition of a selection of cause patterns. In contrast to clustering methods and dimensionality reduction, multiple cause models are capable of turning local features on and off and this makes them a more realistic model for many types of data. However, inference and learning in general multiple cause models takes an amount of time that is exponential in the number of causes. We present an approximate inference algorithm that examines only sparse cause patterns, i.e., those configurations of causes where only a small number of causes are active at a time. This leads to an approximate EM algorithm that maximizes a lower bound on the likelihood of a data set. We show that this sparse multiple cause model can model different types of human facial expression patterns. Performance comparison of the MCM classifier with the SNoW (sparse network of winnows) architecture and the nearest neighbor classifier reveals significant improvement in classification accuracy using the MCM classifier.
Event-coupled hidden Markov models

Inferences from time series data can be greatly enhanced by taking into account multiple modalities. In some cases, such as audio of speech and the corresponding video of lip gestures, the different time series are tightly coupled. We are interested in loosely-coupled time series where only the onset of events are coupled in time. We present an extension of the forward-backward algorithm that can be used for inference and learning in event-coupled hidden Markov models and give results on a simplified multimedia indexing task where the objective is to detect an event whose onset is loosely coupled in audio and video.
A new block-coded modulation scheme for Rayleigh fading channel, soft output decoding issues

We investigate the application of the sum-product algorithm to the decoding of a q-ary block-coded modulation (BCM) scheme which is based on extending the parity check equations of a binary block code to q-ary symbols. This is achieved by decomposing the code into a sub-code with an acyclic Tanner graph and its cosets which are represented by a trellis diagram. The combination of these two cycle-free graphs are used to develop an efficient soft output decoding algorithm for the given code.
Keeping Flexible Active Contours on Track using Metropolis Updates

Condensation, a form of likelihood-weighted particle filtering, has been successfully used to infer the shapes of highly constrained "active" contours in video sequences. However, when the contours are highly flexible (e.g. for tracking fingers of a hand), a computationally burdensome number of particles is needed to successfully approximate the contour distribution. We show how the Metropolis algorithm can be used to update a particle set representing a distribution over contours at each frame in a video sequence. We compare this method to condensation using a video sequence that requires highly flexible contours, and show that the new algorithm performs dramatically better that the condensation algorithm. We discuss the incorporation of this method into the "active contour" framework where a shape-subspace is used constrain shape variation.
Transformation-invariant filtering using expectation maximization

Input signals can often be described by a combination of an underlying signal that is easy to adaptively model (e.g., using a Kalman filter or a hidden Markov model) and a highly nonlinear transformation that is randomly drawn from a known set of transformations. Examples include a video of an unknown object that randomly moves in the field of view; a speech signal that is corrupted by a selection from different types of office noise (chatter, keyboard typing, etc.); and a tomographic signal that is phase-wrapped by an unknown number of wavelengths. We show how transformations in the input, such as translation and shearing in images, can be incorporated into an iterative adaptive filter that uses the expectation maximization algorithm. The underlying system model is a hidden Markov model (HMM) and the iterative filter estimates the parameters of the HMM and performs inference in the HMM in a way that is invariant to transformations in the input. We illustrate the iterative filter on a toy example and on video sequences of people and trucks.