nils-herrmann/clean_scirepeval huggingface dataset:
Early computation of shape and reflectance in the visual system

A COMPELLING sense of three-dimensional shape may be conveyed by the photograph of an object. Cues such as contour, shading, perspective and occlusion, to name a few, contribute to this percept. Psychophysical experiments suggest that certain aspects of three-dimensional shape are computed rapidly and in parallel by the visual system1–7,15. Here we report that reflectance is also computed rapidly; moreover, it is the apparent reflectance, rather than brightness or perceptual three-dimensional shape, that is the primary basis for discrimination during the early stages of visual processing.
Scale-Space Properties of Quadratic Feature Detectors

We consider the scale-space properties of quadratic feature detectors and, in particular, investigate whether, like linear detectors, they permit a scale selection scheme with the "causality property", which guarantees that features are never created as the scale is coarsened. We concentrate on the design of one dimensional detectors with two constituent filters, with the scale selection implemented as convolution and a scaling function. We consider two special cases of interest: the constituent filter pairs related by the Hilbert transform, and by the first spatial derivative. We show that, under reasonable assumptions, Hilbert-pair quadratic detectors cannot have the causality property. In the case of derivative-pair detectors, we describe a family of scaling functions related to fractional derivatives of the Gaussian that are necessary and sufficient for causality. In addition, we report experiments that show the effects of these properties in practice. We thus demonstrate that at least one class of quadratic feature detectors has the same desirable scaling property as the more familiar detectors based on linear filtering.
Motion from fixation

We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. We cast the problem in the framework of "epipolar geometry", and propose a filter based upon implicit dynamical model for recursively estimating motion under the fixation constraint. This allows us to compare the quality of the estimates directly against the ones obtained assuming a general rigid motion simply by changing the geometry of the parameter space, while maintaining the same structure of the recursive estimator. We also present a closed-form static solution from two views, and a recursive estimator of the relative pose between the viewer and the scene.
Deformable Kernels for Early Vision

Early vision algorithms often have a first stage of linear-filtering that 'extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce computation and storage costs. A technique is presented that allows: 1) computing the best approximation of a given family using linear combinations of a small number of 'basis' functions; and 2) describing all finite-dimensional families, i.e., the families of filters for which a finite dimensional representation is possible with no error. The technique is based on singular value decomposition and may be applied to generating filters in arbitrary dimensions and subject to arbitrary deformations. The relevant functional analysis results are reviewed and precise conditions for the decomposition to be feasible are stated. Experimental results are presented that demonstrate the applicability of the technique to generating multiorientation multi-scale 2D edge-detection kernels. The implementation issues are also discussed.
Depth from Brightness of Moving Images

In this note we describe a method for recursively estimating the depth of a scene from a sequence of images. The input to the estimator are brightness values at a number of locations of a grid in a video image, and the output is the relative (scaled) depth corresponding to each image-point. The estimator is invariant with respect to the motion of the viewer, in the sense that the motion parameters are not part of the state of the estimator and therefore the estimates do not depend on motion as long as there is enough parallax (the translational velocity is nonzero). This scheme is a "direct" version of an other algorithm previously presented by the authors for estimating depth from point-feature correspondence independent of motion.
Finding faces in cluttered scenes using random labeled graph matching

An algorithm for locating quasi-frontal views of human faces in cluttered scenes is presented. The algorithm works by coupling a set of local feature detectors with a statistical model of the mutual distances between facial features it is invariant with respect to translation, rotation (in the plane), and scale and can handle partial occlusions of the face. On a challenging database with complicated and varied backgrounds, the algorithm achieved a correct localization rate of 95% in images where the face appeared quasi-frontally.<<ETX>>
Motion from "X" by Compensating "Y"

This paper analyzes the geometry of the visual motion estimation problem in relation to transformations of the input (images) that stabilize particular output functions such as the motion of a point, a line and a plane in the image. By casting the problem within the popular "epipolar geometry", we provide a common framework for including constraints such as point, line of plane fixation by just considering "slices" of the parameter manifold. The models we provide can be used for estimating motion from a batch using the preferred optimization techniques, or for defining dynamic filters that estimate motion from a causal sequence. We discuss methods for performing the necessary compensation by either controlling the support of the camera or by pre-processing the images. The compensation algorithms may be used also for recursively fitting a plane in 3-D both from point-features or directly from brightness. Conversely, they may be used for estimating motion relative to the plane independent of its parameters.
Visual motion estimation from point features: unified view

All methods for recursive estimation of 3-D motion from sequences of perspective images of point-features may be cast within a common framework. The unifying concept is the decoupling of the states of the dynamic observer that estimates motion and structure parameters. Two techniques are possible: explicit decoupling, following the principles of the "reduced-order observer", and implicit, via stabilization (or "compensation"). While we know how to calculate explicit decoupling for a limited number of state variables combinations, for instance using the "essential constraint" of Longuet-Higgins (1981) or the "subspace constraint" of Heeger and Jepson (1992), implicit decoupling is always possible by stabilizing an appropriate smooth function of the motion parameters. We describe some of the most "natural" choices, which consist in compensating for the image-motion of a point, a line or a plane. All the models we derive are in the form of implicit dynamical systems with parameters on different manifolds. Estimating motion may be regarded as the identification of such models, which may be carried out using general methods available in the literature.
Face Localization via Shape Statistics

In this paper, a face localization system is proposed in which local detectors are coupled with a statistical model of the spatial arrangement of facial features to yield robust performance. The outputs from the local detectors are treated as candidate locations and constellations are formed from these. The effects of translation, rotation, and scale are eliminated by mapping to a set of shape variables. The constellations are then ranked according to the likelihood that the shape variables correspond to a face versus an alternative model. Incomplete constellations, which occur when some of the true features are missed, are handled in a principled way.
Pyramidal implementation of deformable kernels

In computer vision and increasingly, in rendering and image processing, it is useful to filter images with continuous rotated and scaled families of filters. For practical implementations, one can think of using a discrete family of filters, and then to interpolate from their outputs to produce the desired filtered version of the image. We propose a multirate implementation of deformable kernels, capable to further reduce the computational weight. The "basis" filters are applied to the different levels of a pyramidal decomposition. The new system is not shift-invariant-it suffers from "aliasing". We introduce a new quadratic error criterion which keeps into account the inherent system aliasing. By using hypermatrix and Kronecker algebra, we are able to cast the global optimization task into a multilinear problem. An iterative procedure ("pseudo-SVD") is used to minimize the overall quadratic approximation error.
System-Theoretic Aspects of Dynamic Vision

Our aim in this minicourse is twofold. On one hand, we aim to show that the introduction of computer vision in control systems, i.e. “Vision in the Loop”, raises exciting and yet unexplored problems in system theory. On the other hand, we explain how tools from control and estimation theory are, nowadays applied [3, 6, 7, 26, 27, 38, 54, 57, 63, 66] to “Dynamic Vision” problems with rather encouraging results in traditionally difficult applications, such as autonomous vehicle navigation [17, 18, 19], vision-based tracking and servo [9, 35, 36, 50], vision-based manipulation [5, 35, 36], docking [18, 42], vision-based planning [12], active sensing [67]. The course is therefore divided in two parts. In the first part, we shall pose two fundamental dynamic vision problems, namely 3-D motion and scene structure recovery, in a system theoretical framework. In the second part, we shall illustrate some relevant applications of dynamic vision to control systems.
Monocular tracking of the human arm in 3D

We address the problem of estimating the position and motion of a human arm in 3D without any constraints on its behavior and without the use of special markers. We model the arm as two truncated right-circular cones connected with spherical joints. We propose to use a recursive estimator for arm position, and to provide the estimator with error signals obtained by comparing the projected estimated arm position with that of the actual arm in the image. The system is demonstrated and tested on a real image sequence.<<ETX>>
Visual navigation using a single camera

We assess the usefulness of monocular recursive motion estimation techniques for vehicle navigation in the absence of a model for the environment. For this purpose we extend a recently proposed recursive motion estimator, the Essential filter, to handle scale estimation. We examine experimentally the accuracy with which the motion and position of the vehicle may be computed on an 8000 frame indoors sequence. The issues of sampling time frequency and number of necessary features in the environment are addressed systematically.<<ETX>>
Dynamic rigid motion estimation from weak perspective

"Weak perspective" represents a simplified projection model that approximates the imaging process when the scene is viewed under a small viewing angle and its depth relief is small relative to its distance from the viewer. We study how to generate dynamic models for estimating rigid 3D motion from weak perspective. A crucial feature in dynamic visual motion estimation is to decouple structure from motion in the estimation model. The reasons are both geometric-to achieve global observability of the model-and practical, for a structure independent motion estimator allows us to deal with occlusions and appearance of new features in a principled way. It is also possible to push the decoupling even further, and isolate the motion parameters that are affected by the so called "bas relief ambiguity" from the ones that are not. We present a novel method for reducing the order of the estimator by decoupling portions of the state space from the time evolution of the measurement constraint. We use this method to construct an estimator of full rigid motion (modulo a scaling factor) on a six dimensional state space, an approximate estimator for a four dimensional subset of the motion space, and a reduced filter with only two states. The latter two are immune to the bas relief ambiguity. We compare strengths and weaknesses of each of the schemes on real and synthetic image sequences.<<ETX>>
Motion from " X " by Compensating CCY , , *

This paper analyzes the geometry of the visual motion estimation problem in relation to transformations of the input (images) that stabilize particular output functions such as the motion of a point, a line and a plane in the image. By casting the problem within the popular "epipolar geometry", we provide a common framework for including constraints such as point, line of plane fixation by just considering "slices" of the parameter manifold. The models we provide can be used for estimating motion from a batch using the preferred optimization techniques, or for defining dynamic filters that estimate motion from a causal sequence. We discuss methods for performing the necessary compensation by either controlling the support of the camera or by pre-processing the images. The compensation algorithms may be used also for recursively fitting a plane in 3-D both from point-features or directly from brightness. Conversely, they may be used for estimating motion relative to the plane independent of its parameters. Research sponsored by NSF NYI Award, NSF ERC in Neuromorphic Systems Engineering at Caltech, ONR grant N00014-93-1-0990.
Stability of Stochastic Approximation under Verifiable Conditions

In this paper we address the problem of the stability and convergence of the stochastic approximation procedure θ n+1 =θ n +γ n+1 [h(θ n )+ξ n+1 ]. The stability of such sequences {θ n } is known to heavily rely on the behaviour of the mean field h at the boundary of the parameter set and the magnitude of the stepsizes used. The conditions typically required to ensure convergence, and in particular the boundedness or stability of {θ n }, are either too difficult to check in practice or not satisfied at all. The most popular technique to circumvent the stability problem consists of constraining {θ n } to a compact subset K in the parameter space. This is obviously not a satis-factory solution as the choice of K is a delicate one. In the present contribution we first prove a "deterministic" stability result which relies on simple conditions on the seauences {ξ n } and {γ n }. We then propose and analyze an algorithm based on projections on adaptive truncation sets which ensures that the aforementioned conditions required for stability are satisfied. We focus in particular on the case where {ξ n } is a so-called Markov state-dependent noise.
Explicit control of subgeometric ergodicity

This paper discusses explicit quantitative bounds on the convergence rates of Markov chains on general state spaces, under so-called drift and minorization conditions. The focus of this paper is on practical conditions that lead to subgeometric rates. Such explicit bounds are particularly relevant in applications where a family of Markov transition probabilities {Pθ : θ ∈ Θ} is considered and for which it is required to establish uniform-in-θ ergodicity. Examples of applications include stochastic versions of the popular EM algorithm (Expectation-Maximization) or adaptive Markov chain Monte Carlo (MCMC) algorithms (see  (resp. ) where quantitative bounds for geometric ergodicity (resp. geometric and subgeometric ergodicity) are used to establish the validity of the algorithms).
Symbol detection comprising reduced state forward backward recursion

A symbol based communications system involves symbol detection at a receiver in the system. The symbol detection involves determining a set of symbols likely to be present on a sequence of received data. A symbol detector provided to fulfil this role includes means for performing a forward backward recursion means on the basis of a reduced set of active states, to determine state probabilities in a hidden Markov model state trellis. The symbol detector further comprises forward message determination means operable to determine a reduced set of forward messages associated with a set of the available states for performance of the reduced state forward recursion, and backward message determination means operable, independently of the forward message determination means, to determine a reduced set of backward messages associated with a backward message determination means for performance of the reduced state backward recursion.
On-Line Parameter Estimation in General State-Space Models

The estimation of static parameters in general non-linear non-Gaussian state-space models is a long-standing problem. This is despite the advent of Sequential Monte Carlo (SMC, aka particle filters) methods, which provide very good approximations to the optimal filter under weak assumptions. Several algorithms based on SMC have been proposed in the past 10 years to solve the static parameter problem. However all the algorithms we are aware of suffer from the so-called `degeneracy problem'. We propose here a methodology for point estimation of static parameters which does not suffer from this problem. Our methods take advantage of the fact that many state space models of interest are ergodic and stationary: this allows us to propose contrast functions for the static parameter which can be consistently estimated and optimised using simulation-based methods. Several types of contrast functions are possible but we focus here on the average of a so-called pseudo-likelihood which we maximize using on-line Expectation-Maximization type algorithms. In its basic form the algorithm requires the expression of the invariant distribution of the underlying state process. When the invariant distribution is unknown, we present an alternative which relies on indirect inference techniques.
Gaussian approximation based mixture reduction for near optimum detection in MIMO systems

The optimal "soft" symbol detection for spatial multiplexing multiple input multiple output (MIMO) system with known channel information requires knowledge of the marginal posterior symbol probabilities for each antenna. The calculation of these quantities requires the evaluation of the likelihood function of the system for all possible symbol combinations, which is prohibitive for large systems. It is however most often the case that most of the transmitted symbol combinations contribute only very little to these marginal posterior probabilities. We propose in this paper a suboptimal procedure which identifies the most significant symbol combinations via a sequential algorithm with Gaussian Approximation (SGA). Simulation results show that our method can approach the optimal a posteriori probability detector (APP) performance while being less complex than comparable suboptimal algorithms, such as the sphere decoder (SD). We further demonstrate that as opposed to the SD the complexity and memory requirements of our algorithm are fixed, therefore easing practical implementation.
Stability of Stochastic Approximation under Verifiable Conditions

In this paper we address the problem of the stability and convergence of the stochastic approximation procedure θ<inf>n+1</inf>=θ<inf>n</inf>+γ<inf>n+1</inf>[h(θ<inf>n</inf>)+ξ<inf>n+1</inf>]. The stability of such sequences {θ<inf>n</inf>} is known to heavily rely on the behaviour of the mean field h at the boundary of the parameter set and the magnitude of the stepsizes used. The conditions typically required to ensure convergence, and in particular the boundedness or stability of {θ<inf>n</inf>}, are either too difficult to check in practice or not satisfied at all. The most popular technique to circumvent the stability problem consists of constraining {θ<inf>n</inf>} to a compact subset K in the parameter space. This is obviously not a satis-factory solution as the choice of K is a delicate one. In the present contribution we first prove a "deterministic" stability result which relies on simple conditions on the seauences {ξ<inf>n</inf>} and {γ<inf>n</inf>}. We then propose and analyze an algorithm based on projections on adaptive truncation sets which ensures that the aforementioned conditions required for stability are satisfied. We focus in particular on the case where {ξ<inf>n</inf>} is a so-called Markov state-dependent noise.
Joint channel tracking and symbol detection in MIMO systems via multiple model methods

We propose a new semi-blind joint channel tracking and symbol detection for spatial multiplexing MIMO system. The proposed algorithm is based on the generalized pseudo Bayesian algorithm (GPB) and probability data association (PDA). The purpose of the GPB algorithm is to control the size of the filtering tree and the Gaussian approximation idea behind PDA is applied to each time instant to reduce the model size. Simulation results are given to demonstrate the effectiveness of the new algorithm.
On the e ciency of adaptive MCMC algorithms

We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an optimal target process via a learning procedure. We show, under appropriate conditions, that the adaptive process and the optimal (nonadaptive) MCMC process share identical asymptotic properties. The special case of adaptive MCMC algorithms governed by stochastic approximation is considered in details and we apply our results to the adaptive Metropolis algorithm of Haario et al. (2001). We also propose a new class of adaptive MCMC algorithms, called quasi-perfect adaptive MCMC which possesses appealing theoretical and practical properties, as demonstrated through numerical simulations.
Reduced complexity equalization of MIMO systems with a fixed-lag smoothed M-BCJR algorithm

M-BCJR algorithm is a reduced state version of the BCJR algorithm and selects a set of active states in the forward recursion based on an estimation of the filtered probability distribution of states at each time. We propose to use instead an estimation of the fixed-lag smoothed probability distribution of states with a non zero lag. Our implementation uses a Gaussian approximation to estimate these distributions with a low complexity, using the principle of probabilistic data association (PDA). The performance of the M-BCJR can be seen to be greatly improved as a result while remaining robust against changes in the channel multipath profile.
Improving soft output quality of MIMO demodulation algorithm via importance sampling

This paper proposes a two stage algorithm to improve the soft output of the channel demodulation algorithm for a multiple input multiple output (MIMO) system with block fading channels by the Monte Carlo method. In the first stage, the demodulation algorithm, i.e., probability data association detector (PDA) or minimum mean square error (MMSE) algorithm, computes the marginal probability distribution of symbols for each antenna. Then we draw samples from those marginal distributions and use the importance sampling algorithm to correct those distributions to approximate the joint posterior distribution of symbols for all the antennas. Simulation results are given to demonstrate the effectiveness of the new algorithm.