nils-herrmann/clean_scirepeval huggingface dataset:
Design of Tone-Dependent Color-Error Diffusion Halftoning Systems

Grayscale error diffusion introduces nonlinear distortion (directional artifacts and false textures), linear distortion (sharpening), and additive noise. Tone-dependent error diffusion (TDED) reduces these artifacts by controlling the diffusion of quantization errors based on the input graylevel. We present an extension of TDED to color. In color-error diffusion, which color to render becomes a major concern in addition to finding optimal dot patterns. We propose a visually meaningful scheme to train input-level (or tone-) dependent color-error filters. Our design approach employs a Neugebauer printer model and a color human visual system model that takes into account spatial considerations in color reproduction. The resulting halftones overcome several traditional error-diffusion artifacts and achieve significantly greater accuracy in color rendition
A Unified Paradigm For Scalable Multi-Projector Displays

We present a general framework for the modeling and optimization of scalable multi-projector displays. Based on this framework, we derive algorithms that can robustly optimize the visual quality of an arbitrary combination of projectors without manual adjustment. When the projectors are tiled, we show that our framework automatically produces blending maps that outperform state-of-the-art projector blending methods. When all the projectors are superimposed, the framework can produce high-resolution images beyond the Nyquist resolution limits of component projectors. When a combination of tiled and superimposed projectors are deployed, the same framework harnesses the best features of both tiled and superimposed multi-projector projection paradigms. The framework creates for the first time a new unified paradigm that is agnostic to a particular configuration of projectors yet robustly optimizes for the brightness, contrast, and resolution of that configuration. In addition, we demonstrate that our algorithms support high resolution video at real-time interactive frame rates achieved on commodity graphics platforms. This work allows for inexpensive, compelling, flexible, and robust large scale visualization systems to be built and deployed very efficiently.
A new transform for the stabilization and stability testing of multidimensional recursive digital filters

We present a new transform which, when applied to the denominator polynomial of the transfer function of an unstable multidimensional recursive digital filter (of a special class) will yield a stable polynomial with good preservation of the magnitude spectrum. In fact, the discrete Hilbert transform (DHT), used to stabilize 2-D and 1-D recursive digital filters, is a special case of the general multidimensional transform we present here. We also address the problem of stability testing of a multidimensional recursive digital filter and show that the new transform may be used to implement a straightforward test for stability of any causal multidimensional recursive digital filter, having no nonessential singularities of the second kind.
An automated framework for multicriteria optimization of analog filter designs

This paper presents an extensible framework for designing analog filters that exhibit several desired behavioral properties after being realized in circuits. In the framework, we model the constrained nonlinear optimization problem as a sequential quadratic programming (SQP) problem. SQP requires real-valued constraints and objective functions that are differentiable with respect to the free parameters (pole-zero locations). We derive the differentiable constraints and a weighted differentiable objective function for simultaneously optimizing the behavioral properties of magnitude response, phase response, peak overshoot, and the implementation property of quality factors. We use Mathematica to define the algebraic equations for the constraints and objective function, compute their gradients symbolically, and generate standalone MATLAB programs to perform the multicriteria optimization. Providing closed-form gradients prevents divergence in the SQP procedure. The automated approach avoids errors in algebraic calculations and errors in transcribing equations into software. The key contributions are: 1) an extensible, automated, multicriteria filter optimization framework; 2) an analytic approximation for peak overshoot; and 3) three novel filter designs. We have released the source code for the framework on the Internet.
Color error diffusion with generalized optimum noise shaping

We optimize the noise shaping behavior of color error diffusion by designing an optimized error filter based on a proposed noise shaping model for color error diffusion and a generalized linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm.
A fast, high-quality inverse halftoning algorithm for error diffused halftones

Halftones and other binary images are difficult to process with causing several degradation. Degradation is greatly reduced if the halftone is inverse halftoned (converted to grayscale) before scaling, sharpening, rotating, or other processing. For error diffused halftones, we present (1) a fast inverse halftoning algorithm and (2) a new multiscale gradient estimator. The inverse halftoning algorithm is based on anisotropic diffusion. It uses the new multiscale gradient estimator to vary the tradeoff between spatial resolution and grayscale resolution at each pixel to obtain a sharp image with a low perceived noise level. Because the algorithm requires fewer than 300 arithmetic operations per pixel and processes 7x7 neighborhoods of halftone pixels, it is well suited for implementation in VLSI and embedded software. We compare the implementation cost, peak signal to noise ratio, and visual quality with other inverse halftoning algorithms.
FM halftoning via block error diffusion

Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones. In FM halftoning the dot size and shape is fixed (equal to one pixel) and the dot frequency is varied in accordance to the graylevel values of the underlying grayscale image. We generalize error diffusion to produce FM halftones with user controlled dot size and shape using block quantization and a block filter in the feedback loop. We call this modified quantization and feedback process block error diffusion. The block filters are designed from well known scalar error filter prototypes and retain their properties. Further, we show that choosing a structured block filter results in an efficient parallel implementation of block error diffusion.
Image quality assessment based on a degradation model

We model a degraded image as an original image that has been subject to linear frequency distortion and additive noise injection. Since the psychovisual effects of frequency distortion and noise injection are independent, we decouple these two sources of degradation and measure their effect on the human visual system. We develop a distortion measure (DM) of the effect of frequency distortion, and a noise quality measure (NQM) of the effect of additive noise. The NQM, which is based on Peli's (1990) contrast pyramid, takes into account the following: 1) variation in contrast sensitivity with distance, image dimensions, and spatial frequency; 2) variation in the local luminance mean; 3) contrast interaction between spatial frequencies; 4) contrast masking effects. For additive noise, we demonstrate that the nonlinear NQM is a better measure of visual quality than peak signal-to noise ratio (PSNR) and linear quality measures. We compute the DM in three steps. First, we find the frequency distortion in the degraded image. Second, we compute the deviation of this frequency distortion from an allpass response of unity gain (no distortion). Finally, we weight the deviation by a model of the frequency response of the human visual system and integrate over the visible frequencies. We demonstrate how to decouple distortion and additive noise degradation in a practical image restoration system.
Direct Solution to the Minimal Generalized Pose

Pose estimation is a relevant problem for imaging systems whose applications range from augmented reality to robotics. In this paper we propose a novel solution for the minimal pose problem, within the framework of generalized camera models and using a planar homography. Within this framework and considering only the geometric elements of the generalized camera models, an imaging system can be modeled by a set of mappings associating image pixels to 3-D straight lines. This mapping is defined in a 3-D world coordinate system. Pose estimation performs the computation of the rigid transformation between the original 3-D world coordinate system and the one in which the camera was calibrated. Using synthetic data, we compare the proposed minimal-based method with the state-of-the-art methods in terms of numerical errors, number of solutions and processing time. From the experiments, we conclude that the proposed method performs better, especially because there is a smaller variation in numerical errors, while results are similar in terms of number of solutions and computation time. To further evaluate the proposed approach we tested our method with real data. One of the relevant contributions of this paper is theoretical. When compared to the state-of-the-art approaches, we propose a completely new parametrization of the problem that can be solved in four simple steps. In addition, our approach does not require any predefined transformation of the dataset, which yields a simpler solution for the problem.
Non-central catadioptric cameras visual servoing for mobile robots using a radial camera model

Catadioptric cameras combine conventional cameras and mirrors to create omnidirectional sensors providing 360° panoramic views of a scene. Modeling such cameras has been subject of significant research interest in the computer vision community leading to a deeper understanding of the image properties and also to different models for different types of configurations. Visual servoing applications using catadioptric cameras have essentially been using central cameras and the corresponding unified projection model. So far only in very few cases more general models have been used. In this paper we address the problem of visual servoing using the so-called the radial model. The radial model can be applied to many camera configurations and in particular to non-central catadioptric systems with mirror shapes that are symmetric around the optical axis. In this case we show that the radial model can be used with a non-central catadioptric camera to allow effective image-based visual servoing (IBVS) of a mobile robot. Using this model, which is valid for a large set of catadioptric cameras, new visual features are proposed to control the degrees of freedom of a mobile robot moving on a plane. Several simulation results are provided to validate the effectiveness of such features.
A focusing-by-vergence system controlled by retinal motion disparity

We describe a focusing-by-vergence system combining a flow based vergence process with an off-line pre-calibration of the focusing odometry. The vergence process is controlled by the retinal optical flow disparity and the target depth velocity is directly obtained from this disparity. The relationship between the target depth (focused target distance) and the focusing odometry of the motorized lens is obtained by an off-line focusing calibration, using a bivariate polynomial to model the relationship between these parameters. The focusing system is velocity controlled and the focusing velocity is dependent on the target focusing depth velocity and the focal distance velocity. The influence of the focal distance velocity that arise due to the focusing process itself is compensated by the adjustment of the zoom parameter based on a constant magnification approach, or through the modeling of the focal distance as a function of the zoom and focus lens settings.
Parallel texture analysis using high-order statistics and neural networks

In this paper we present a texture analysis and classification method based on the computation of high-order statistics of gray-levels or selected features. The computation of these high-order statistics can be performed in parallel in non-overlapping windows that cover the full image. Classification is performed by neural networks. In this extended summary results are presented for backpropagation networks only, but several other types of neural nets were also tested.<<ETX>>
Linear solution for the pose estimation of noncentral catadioptric systems

This paper presents a linear method to estimate the pose of a noncentral catadioptric system with a quadric shaped mirror in relation to a world reference frame (or local reference frame without loss of generality). The vision system is assumed to be calibrated. The method uses also as input data the structure of the scene. It is proved that any reflection point should belong to an analytical quadric that intersects the mirror quadric itself. This constraint can be written linearly in the 3D scene point coordinates (in the camera reference frame). The unknown pose screw transformation that relates camera and world reference frames can then be used in the linear model, allowing for the construction of a linear equation in the pose transformation elements. Additional constraints are used to force the estimated rotation elements to build an orthogonal matrix. Tests with simulated data and also on real images with different mirrors proved the method to be consistent and to estimate the pose accurately. However, it was also observed that the method is sensitive to noise. The results are compared with another method.
Tracking multiple objects in 3D

A system for tracking multiple targets in 3D is described. The system is made up of two pan-and-tilt units that are attached to the extremities of a rotating arm. This configuration has several advantages and can deal with several specific instances of tracking more than one target. A control strategy that guarantees equal target disparities in both images whenever targets are seen by both cameras is presented. This has advantages for segmentation and trajectory reconstruction. Target images are simultaneously visible in the cameras, enabling the recovery of the targets 3D trajectory. It is also shown that mutual occlusion occurs in a well-defined configuration and can therefore be dealt with.
Plücker correction problem: Analysis and improvements in efficiency

A given six dimensional vector represents a 3D straight line in Plücker coordinates if its coordinates satisfy the Klein quadric constraint. In many problems aiming to find the Plücker coordinates of lines, noise in the data and other type of errors contribute for obtaining 6D vectors that do not correspond to lines, because of that constraint. A common procedure to overcome this drawback is to find the Plücker coordinates of the lines that are closest to those vectors. This is known as the Plücker correction problem. In this article we propose a simple, closed-form, and global solution for this problem. When compared with the state-of-the-art method, one can conclude that our algorithm is easier and requires much less operations than previous techniques (it does not require Singular Value Decomposition techniques).
Depth recovery using active focus in robotics (vision)

In practice focusing can be obtained by displacing the sensor plate with respect to the image plane, by moving the lens or by moving the object with respect to the optical system. Moving the lens or sensor plate with respect to each other, causes changes of the magnification and corresponding changes on the object coordinates. In order to overcome these problems, the authors propose a technique to vary the degree of focusing by moving the camera with respect to the object position. The camera is attached to the tool of a manipulator in a hand-eye configuration with the position of the camera always known. This approach ensures that the focused areas of the image are always subjected to the same magnification. To measure the focus quality operators are used to evaluate the quantity of high-frequency components on the image. Different types of these operators was tested and the results compared.<<ETX>>
Image-based servoing of non-holonomic vehicles using non-central catadioptric cameras

Novel contributions on image-based control of a mobile robot using a general catadioptric camera model are presented in this paper. Visual servoing applications using catadioptric cameras have essentially been using central cameras and the corresponding unified projection model. So far only in a few cases more general models have been used. In this paper we address the problem of visual servoing using the socalled the radial model. The radial model can be applied to many camera configurations and in particular to non-central catadioptric systems with mirrors that are symmetric around an axis coinciding with the optical axis. In this case, we show that the radial model can be used with a non-central catadioptric camera to allow effective image-based visual servoing (IBVS) of a mobile robot. Two sets of experiments are carried. In one of the sets, IMU (Inertial Measurement Unit) is used to measure the relative rotation of the robot and in the other set visual features are solely used. The achieved results validate both the applicability and effectiveness of the proposed method for imaged-based control of a non-holonomic robot.
Binocular tracking and accommodation controlled by retinal motion flow

Describes a tracking and accommodation system controlled by retinal motion flow. The tracking system decomposes two basic behaviors: smooth-pursuit and vergence. Both behaviors are optical flow based. The eye's accommodation (focusing) is based on a focusing-by-vergence approach combining a flow based vergence process with an off-line pre-calibration of the lens focusing odometry. The focusing and tracking system run in parallel and are both velocity controlled.
Issues on the geometry of central catadioptric image formation

An imaging system with a single effective viewpoint is called a central projection system. The conventional perspective camera is an example of a central projection system. Systems using mirrors to enhance the field of view while keeping a unique center of projection are also examples of central projection systems. Perspective image formation can be described by a linear model with well known properties. In general central catadioptric imaging, the mapping between points in the world and in the image is highly nonlinear. The paper establishes a general model for central catadioptric image formation made up of three functions: a linear function mapping the world into an oriented projective plane, a nonlinear transformation between two oriented projective planes, and a collineation in the plane. The model is used to study issues in the projection of lines. The equations and geometric properties of general catadioptric imaging of lines are derived. The application of the results in auto-calibration of central catadioptric systems and reconstruction are discussed. A method to calibrate the system using three line images is presented.
Estimation of 3D motion from stereo images-uncertainty analysis and experimental results

This paper analyses the problem of motion estimation from a sequence of stereo images. Both the differential and discrete approaches of two methods are formulated The differential approach uses differential optical flow whereas the discrete approaches uses feature correspondences. Both methods are used to compute, first, the 3D velocity in the depth (Z) direction and, second, the complete rigid motion parameters. Furthermore, the uncertainty propagation models for both methods and approaches are derived These models are analysed in order to point out the critical variables for the methods. The methods were extensively tested using synthetic images as well as real images and several conclusions are drawn from the results. The real images are used without any illumination control of the scene in order to study the behavior of the methods in strongly noisy environments with low resolution depth maps.
Pose estimation for central catadioptric systems: an analytical approach

The number of applications for central catadioptric systems, often called central panoramic systems, is increasing. Among these are surveillance systems, commercial systems for Web navigation and robot localization and navigation. This work is involved with the latter, and presents an analytical method for pose estimation using any central catadioptric system. Any known four points in the world, forming a planar rectangle. may be used for pose computation. The computed pose includes the rotation and the translation from the camera coordinate system to the world coordinate system.
Real-time vergence and binocular gaze control

In this paper we describe the implementation of real-time binocular gaze control. This implementation is performance by using a complex binocular active vision system. In addition real-time vergence is also implemented. Vergence and gaze control are implemented based on binocular optical flow. Vergence control is implemented based on optical flow disparity, using the horopter as a reference. A Kalman filter is used to filter out the signals and compensate for the delays of the system.
Iterative multi-step explicit camera calibration

Perspective camera calibration has been in the last decades a research subject for a large group of researchers and as a result several camera calibration methodologies can be found in the literature. However only a small number of those methods base their approaches on the use of monoplane calibration points. This paper describes one of those methodologies that uses monoplane calibration points to realize an explicit 3D camera calibration. To avoid the singularity obtained with the calibration equations when monoplane calibration points are used, this method computes the calibration parameters in a multi-step procedure and requires a first-guess solution for the intrinsic parameters. These parameters are updated and their accuracy increased through an iterative procedure. A stability analysis as a function of the pose of the camera is presented. Camera pose view strategies for accurate camera orientation computation can be extracted from the pose view stability analysis.
Real-time gesture recognition system based on contour signatures

In this paper we describe a real-time gesture recognition algorithm based on contour signature images, generated by collecting a set of contour signatures over time. Each contour signature is generated in each frame by computing the polar coordinates of each point of the contour in relation to the center of mass of that contour The gesture recognition is reduced to the problem of finding the correspondent contour signature image from a set of previously learned image contour signatures. This is achieved using a Karhunen-Loeve (KL) transform that reduces the set of trained images to a low-dimensional eigenspace, making recognition easier This method is particularly suitable for the recognition of gestures that can be perfectly described by silhouettes. Since no shape or 3D model is required the proposed method can easily be adapted to recognize several other types of gestures not necessarily performed by humans. The proposed algorithm is able to extract target gesture motions and classify them at the same time. Preliminary experimental results demonstrate robustness and real-time performance of the proposed algorithm.