nils-herrmann/clean_scirepeval huggingface dataset:
Real-time end-effector motion behavior planning approach using on-line point-cloud data towards a user adaptive assistive bath robot

Elderly people have particular needs in performing bathing activities, since these tasks require body flexibility. Our aim is to build an assistive robotic bath system, in order to increase the independence and safety of this procedure. Towards this end, the expertise of professional carers for bathing sequences and appropriate motions has to be adopted, in order to achieve natural, physical human — robot interaction. In this paper, a real-time end-effector motion planning method for an assistive bath robot, using on-line Point-Cloud information, is proposed. The visual feedback obtained from Kinect depth sensor is employed to adapt suitable washing paths to the user's body part motion and deformable surface. We make use of a navigation function-based controller, with guarantied globally uniformly asymptotic stability, and bijective transformations for the adaptation of the paths. Experiments were conducted with a rigid rectangular object for validation purposes, while a female subject took part to the experiment in order to evaluate and demonstrate the basic concepts of the proposed methodology.
A PDE approach to nonlinear image simplification via levelings and reconstruction filters

We present a nonlinear partial differential equation (PDE) that models the generation of a large class of advanced morphological filters, the levelings and the openings/closings by reconstruction. These types of filters are very useful in numerous image analysis and vision tasks ranging from enhancement, feature detection, image simplification, to segmentation. The developed PDE models these nonlinear filters as the limit of a controlled growth starting from an initial seed signal. This growth is of the multiscale dilation or erosion type and the controlling mechanism is a switch that reverses the growth when the difference between the current evolution and a reference signal switches signs. We discuss theoretical aspects of this PDE, propose a discrete algorithm for its numerical solution and corresponding filter implementation, and provide insights via several experiments. Finally, we outline its use for improving the Gaussian scale-space by using the latter as initial seed to generate multiscale levelings that have a superior preservation of image edges and boundaries.
Pattern Spectrum and Multiscale Shape Representation

The results of a study on multiscale shape description, smoothing and representation are reported. Multiscale nonlinear smoothing filters are first developed, using morphological opening and closings. G. Matheron (1975) used openings and closings to obtain probabilistic size distributions of Euclidean-space sets (continuous binary images). These distributions are used to develop a concept of pattern spectrum (a shape-size descriptor). A pattern spectrum is introduced for continuous graytone images and arbitrary multilevel signals, as well as for discrete images, by developing a discrete-size family of patterns. Large jumps in the pattern spectrum at a certain scale indicate the existence of major (protruding or intruding) substructures of the signal at the scale. An entropy-like shape-size complexity measure is also developed based on the pattern spectrum. For shape representation, a reduced morphological skeleton transform is introduced for discrete binary and graytone images. This transform is a sequence of skeleton components (sparse images) which represent the original shape at various scales. It is shown that the partially reconstructed images from the inverse transform on subsequences of skeleton components are the openings of the image at a scale determined by the number of eliminated components; in addition, two-way correspondences are established among the degree of shape smoothing via multiscale openings or closings, the pattern spectrum zero values, and the elimination or nonexistence of skeleton components at certain scales.
Pattern spectrum of images and morphological shape-size complexity

By using morphological opening and closing set operations, a pattern spectrum of binary images can be developed, which measures the image content relative to patterns of arbitrary shape and size. In this paper, the pattern spectrum of discrete images is further generalized, is directly related to the skeleton (medial axis) transform, is used to derive a shape-size complexity measure of the image and its immediate background, and all these concepts are extended to graytone images. The results of this study indicate that the pattern spectrum can be used to analyze and enrich the medial axis (or other skeleton-like) transforms, and quantify the roughness of the image boundary or surface.
Video event detection and summarization using audio, visual and text saliency

Detection of perceptually important video events is formulated here on the basis of saliency models for the audio, visual and textual information conveyed in a video stream. Audio saliency is assessed by cues that quantify multifrequency waveform modulations, extracted through nonlinear operators and energy tracking. Visual saliency is measured through a spatiotemporal attention model driven by intensity, color and motion. Text saliency is extracted from part-of-speech tagging on the subtitles information available with most movie distributions. The various modality curves are integrated in a single attention curve, where the presence of an event may be signified in one or multiple domains. This multimodal saliency curve is the basis of a bottom-up video summarization algorithm, that refines results from unimodal or audiovisual-based skimming. The algorithm performs favorably for video summarization in terms of informativeness and enjoyability.
Adaptive and constrained algorithms for inverse compositional Active Appearance Model fitting

Parametric models of shape and texture such as active appearance models (AAMs) are diverse tools for deformable object appearance modeling and have found important applications in both image synthesis and analysis problems. Among the numerous algorithms that have been proposed for AAM fitting, those based on the inverse-compositional image alignment technique have recently received considerable attention due to their potential for high efficiency. However, existing fitting algorithms perform poorly when used in conjunction with models exhibiting significant appearance variation, such as AAMs trained on multiple-subject human face images. We introduce two enhancements to inverse-compositional AAM matching algorithms in order to overcome this limitation. First, we propose fitting algorithm adaptation, by means of (a) fitting matrix adjustment and (b) AAM mean template update. Second, we show how prior information can be incorporated and constrain the AAM fitting process. The inverse-compositional nature of the algorithm allows efficient implementation of these enhancements. Both techniques substantially improve AAM fitting performance, as demonstrated with experiments on publicly available multi-face datasets.
Measuring the Fractal Dimension of Signals: Morphological Covers and Iterative Optimization

Fractals can model many classes of time-series data. The fractal dimension is an important characteristic of fractals that contains information about their geometrical structure at multiple scales. The covering methods are a class of efficient approaches to measure the fractal dimension of an arbitrary fractal signal by creating multiscale covers around the signal's graph. In this paper we develop a general method that uses multiscale morphological operations with varying structuring elements to unify and extend the theory and digital implementations of covering methods. It is theoretically estab- lished that, for the fractal dimension computation, covering one-dimensional signals with planar sets is equivalent to mor- phologically transforming the signal by one-dimensional func- tions, which reduces the computational complexity from quad- ratic in the signal's length to linear. Then a morphological covering algorithm is developed and applied to discrete-time signals synthesized from Weierstrass functions, fractal inter- polation functions, and fractional Brownian motion. Further, for deterministic parametric fractals depending on a single pa- rameter related to their dimension, we develop an optimization method that starts from an initial estimate and iteratively con- verges to the true fractal dimension by searching in the param- eter space and minimizing a distance between the original sig- nal and all such signals from the same class. Experimental results are also provided to demonstrate the good performance of the developed methods.
Two frontiers in morphological image analysis: differential evolution models and hybrid morphological/linear neural networks

We briefly describe advancements in two broad areas of morphological image analysis. Part I deals with differential morphology and curve evolution. The partial differential equations (PDEs) that model basic morphological operations are first presented. The resulting dilation PDE, numerically implemented by curve evolution algorithms, improves the accuracy of morphological multiscale analysis by Euclidean disks and (its anisotropic/heterogeneous version) is the basic ingredient of PDE models that solve image analysis problems such as gridless halftoning and watershed segmentation based on the eikonal PDE. Part II deals with morphology-related systems for pattern recognition. It presents a general class of multilayer feedforward neural networks where the combination of inputs in every node is formed by hybrid linear and nonlinear (of the morphological/rank type) operations. For its design a methodology is formulated using ideas from the backpropagation algorithm and robust techniques are developed to circumvent the non-differentiability of rank functions. Experimental results in handwritten character recognition are described and illustrate some of the properties of this new type of neural nets.
Audio-Assisted Movie Dialogue Detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported.
Morphological systems for multidimensional signal processing

The basic theory and applications of a set-theoretic approach to image analysis called mathematical morphology are reviewed. The goals are to show how the concepts of mathematical morphology geometrical structure in signals to illuminate the ways that morphological systems can enrich the theory and applications of multidimensional signal processing. The topics covered include: applications to nonlinear filtering (morphological and rank-order filters, multiscale smoothing, morphological sampling, and morphological correlation); applications to image analysis (feature extraction, shape representation and description, size distributions, and fractals); and representation theorems, which shows how a large class of nonlinear and linear signal operators can be realized as a combination of simple morphological operations.
On the improvement of modulation features using multi-microphone energy tracking for robust distant speech recognition

In this work, we investigate robust speech energy estimation and tracking schemes aiming at improved energy-based multiband speech demodulation and feature extraction for multi-microphone distant speech recognition. Based on the spatial diversity of the speech and noise recordings of a multi-microphone setup, the proposed Multichannel, Multiband Demodulation (MMD) scheme includes: 1) energy selection across the microphones that are less affected by noise and 2) cross-signal energy estimation based on the cross-Teager energy operator. Instantaneous modulations of speech resonances are estimated on the denoised energies. Second-order frequency modulation features are measured and combined with MFCCs achieving improved distant speech recognition on simulated and real data recorded in noisy and reverberant domestic environments.
Synergy between Object Recognition and Image Segmentation Using the Expectation-Maximization Algorithm

In this work, we formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. We consider segmentation as the assignment of image observations to object hypotheses and phrase it as the E-step, while the M-step amounts to fitting the object models to the observations. These two tasks are performed iteratively, thereby simultaneously segmenting an image and reconstructing it in terms of objects. We model objects using Active Appearance Models (AAMs) as they capture both shape and appearance variation. During the E-step, the fidelity of the AAM predictions to the image is used to decide about assigning observations to the object. For this, we propose two top-down segmentation algorithms. The first starts with an oversegmentation of the image and then softly assigns image segments to objects, as in the common setting of EM. The second uses curve evolution to minimize a criterion derived from the variational interpretation of EM and introduces AAMs as shape priors. For the M-step, we derive AAM fitting equations that accommodate segmentation information, thereby allowing for the automated treatment of occlusions. Apart from top-down segmentation results, we provide systematic experiments on object detection that validate the merits of our joint segmentation and recognition approach.
Morphology-based symbolic image modeling, multi-scale nonlinear smoothing, and pattern spectrum

The author develops a symbolic modeling of images based on their shape-size information. First, multiscale multishape structural distributions in the image are modeled by morphological openings, and a related shape-size descriptor, the pattern spectrum, is developed that can detect critical scales. Then the image is modeled as a nonlinear superposition of simpler parts (the symbols), which are translated and scaled shape patterns drawn from a finite collection. The model parameters are found by using the information from openings and pattern spectrum, and by local searches at points of generalized skeletons. The results appear promising for multiscale image analysis and shape recognition.<<ETX>>
Max-product dynamical systems and applications to audio-visual salient event detection in videos

This paper introduces a theory for max-product systems by analyzing them as discrete-time nonlinear dynamical systems that obey a superposition of a weighted maximum type and evolve on nonlinear spaces which we call complete weighted lattices. Special cases of such systems have found applications in speech recognition as weighted finite-state transducers and in belief propagation on graphical models. Our theoretical approach establishes their representation in state and input-output spaces using monotone lattice operators, finds analytically their state and output responses using nonlinear convolutions, studies their stability, and provides optimal solutions to solving max-product matrix equations. Further, we apply these systems to extend the Viterbi algorithm in HMMs by adding control inputs and model cognitive processes such as detecting audio and visual salient events in multimodal video streams, which shows good performance as compared to human attention.
Finding speech formants and modulations via energy separation: with application to a vocoder

An experimental system that uses an energy-tracking operator and a related energy separation algorithm to find speech formants and amplitude/frequency modulations automatically in voiced speech segments is presented. Initial estimates of formant center frequencies are provided by either LPC (linear predictive coding) or morphological spectral peak picking. These estimates are improved by a combination of bandpass filtering and iterative application of energy separation. The system is shown to be effective. Its application to an AM-FM vocoder is also discussed.<<ETX>>
PDE-based modeling of image segmentation using volumic flooding

The classical case of morphological segmentation is based on the watershed transform, constructed by flooding the gradient image, which is seen as a topographic surface, with constant height speed. Changing the flooding criteria, (e.g. constant-speed height, area or volume) yields different segmentation results. In the field of PDEs and curve evolution the classic watershed transform can be modelled as the solution of an eikonal PDE. In this paper we model the watershed segmentation based on a volume flooding criterion via a different eikonal PDE. Then we solve this PDE using the fast marching method, which is a specific algorithm from the methodology of level sets. In addition, we attempt to exploit the advantages of image segmentation using PDE-based volume flooding over the classic height flooding.
Speech formant frequency and bandwidth tracking using multiband energy demodulation

The AM-FM modulation model and a multiband analysis/demodulation scheme is applied to speech formant frequency and bandwidth tracking. Filtering is performed by a bank of Gabor bandpass filters. Each band is demodulated to amplitude envelope and instantaneous frequency signals using the energy separation algorithm. Short-time formant frequency and bandwidth estimates are obtained from the instantaneous amplitude and frequency signals and their merits are presented. The estimates are used to determine the formant locations and bandwidths. Performance and computational issues (frequency domain implementation) are discussed. Overall, the multiband demodulation approach to formant tracking is easy to implement, provides accurate formant frequency and realistic bandwidth estimates, and performs well in the presence of nasalization.
An expectation maximization approach to the synergy between image segmentation and object categorization

In this work, we deal with the problem of modelling and exploiting the interaction between the processes of image segmentation and object categorization. We propose a novel framework to address this problem that is based on the combination of the expectation maximization (EM) algorithm and generative models for object categories. Using a concise formulation of the interaction between these two processes, segmentation is interpreted as the E step, assigning observations to models, whereas object detection/analysis is modelled as the M-step, fitting models to observations. We present in detail the segmentation and detection processes comprising the E and M steps and demonstrate results on the joint detection and segmentation of the object categories of faces and cars.
Two-dimensional linear predictive analysis of arbitrarily-shaped regions

This paper is concerned with the use of 2-D linear prediction for image segmentation. It begins with a brief summary of the mathematics involved in 2-D linear predictive analysis of arbitrarily-shaped regions. Then, it introduces a 2-D LPC distance measure based on the error residual of 2-D linear prediction. Finally, it describes how the above results can be applied to image segmentation using a simple cluster seeking algorithm. The results indicate that arbitrarily-shaped image regions can be well identified and clustered using as features their 2-D LPC parameters.
Multicomponent AM-FM demodulation via periodicity-based algebraic separation and energy-based demodulation

Previously investigated multicomponent AM-FM demodulation techniques either assume that the individual component signals are spectrally isolated from each other or that the components can be isolated by linear time-invariant filtering techniques and, consequently, break down in the case where the components overlap spectrally or when one of the components is stronger than the other. In this paper, we present a nonlinear algorithm for the separation and demodulation of discrete-time multicomponent AM-FM signals. Our approach divides the demodulation problem into two independent tasks: algebraic separation of the components based on periodicity assumptions and then monocomponent demodulation of each component by instantaneously tracking and separating its source energy into its amplitude and frequency parts. The proposed new algorithm avoids the shortcomings of previous approaches and works well for extremely small spectral separations of the components and for a wide range of relative amplitude/power ratios. We present its theoretical analysis and experimental results and outline its application to demodulation of cochannel FM voice signals.
Multisensor multiband cross-energy tracking for feature extraction and recognition

In this paper, we present a multisensor multiband energy tracking scheme for robust feature extraction in noisy environments. We introduce a multisensor feature extraction algorithm which combines both the spatial and frequency information incorporated in the speech signals captured by a microphone array. This is based on the estimation of cross-energies over multiple sensors and minimization of an error term due to noise. The relevant noise-analysis is given. Automatic speech recognition (ASR) experiments at various SNR levels demonstrate that the newly proposed frontend performs better than alternative schemes, especially in noisy conditions.
Evolution equations for continuous-scale morphology

Several nonlinear partial differential equations that model the scale evolution associated with continuous-space multiscale morphological erosions, dilations, openings, and closings are discussed. These systems relate the infinitesimal evolution of the multiscale signal ensemble in scale space to a nonlinear operator acting on the space of signals. The type of this nonlinear operator is determined by the shape and dimensionality of the structuring element used by the morphological operators, generally taking the form of nonlinear algebraic functions of certain differential operators.<<ETX>>
Photon-limited image denoising by inference on multiscale models

We present an improved statistical model of Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main novel contributions are (1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the observed Poisson data (counts); (2) extension of the method to work under a scale-recursive hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling inter-scale coefficient dependencies in the vicinity of edges; and (3) exploration of a fully 2-D quad-tree image partitioning, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary image partitioning involving Beta-mixture rate-ratio densities. Experimental intensity estimation results on standard images with artificially simulated Poisson noise and photon-limited images with real shot noise demonstrate the effectiveness of the proposed approach.