nils-herrmann/clean_scirepeval huggingface dataset:
Calibration of Smooth Camera Models

Generic imaging models can be used to represent any camera. Current generic models are discrete and define a mapping between each pixel in the image and a straight line in 3D space. This paper presents a modification of the generic camera model that allows the simplification of the calibration procedure. The only requirement is that the coordinates of the 3D projecting lines are related by functions that vary smoothly across space. Such a model is obtained by modifying the general imaging model using radial basis functions (RBFs) to interpolate image coordinates and 3D lines, thereby allowing both an increase in resolution (due to their continuous nature) and a more compact representation. Using this variation of the general imaging model, we also develop a calibration procedure. This procedure only requires that a 3D point be matched to each pixel. In addition, not all the pixels need to be calibrated. As a result, the complexity of the procedure is significantly decreased. Normalization is applied to the coordinates of both image and 3D points, which increases the accuracy of the calibration. Results with both synthetic and real datasets show that the model and calibration procedure are easily applicable and provide accurate calibration results.
Pose Estimation for General Cameras Using Lines

In this paper, we address the problem of pose estimation under the framework of generalized camera models. We propose a solution based on the knowledge of the coordinates of 3-D straight lines (expressed in the world coordinate frame) and their corresponding image pixels. Previous approaches used the knowledge of the coordinates of 3-D points (zero dimensional elements) and their corresponding images (zero dimensional elements). In this paper, pixels belonging to the image of 3-D lines are used. There is no need to establish correspondences between pixels and 3-D points. Correspondences are established between 3-D lines and their images. There is no need to identify individual pixels. The use of correspondences between pixels (that belong to the images of the 3-D lines) and 3-D lines facilitates the correspondence problem when compared to the use of world and image points. This is one of the contributions of this paper. The approach is both evaluated and validated using synthetic data and also real images.
Simulating pursuit with machine experiments with robots and artificial vision

This article describes one solution for the problem of pursuit of objects moving on a plane by using a mobile robot and an active vision system. The solution deals with the interaction of different control systems using visual feedback and it is accomplished by the implementation of a visual gaze holding process interacting cooperatively with the control of the trajectory of a mobile robot. These two systems are integrated to follow a moving object at constant distance and orientation with respect to the mobile robot. The orientation and the position of the active vision system running a gaze holding process give the feedback signals to the control used to pursuit the target in real-time. The paper addresses the problems of visual fixation, visual smooth pursuit, navigation using visual feedback and compensation for system's movements. The algorithms for visual processing and control are described in the article. The mechanisms of cooperation between the different control and visual algorithms are also described. The final solution is a system able to operate at approximately human walking rates as the experimental results show in the paper.
Efficient Iterative Pose Estimation Using an Invariant to Rotations

This paper deals with pose estimation using an iterative scheme. We show that using adequate visual information, pose estimation can be performed iteratively with only three independent unknowns, which are the translation parameters. Specifically, an invariant to rotational motion is used to estimate the camera position. In addition, an adequate transformation is applied to the proposed invariant to decrease the nonlinearities between the variations in image space and 3-D space. Once the camera position is estimated, we show that the rotation can be estimated efficiently using two different direct methods. The proposed approach is compared against two other methods from the literature. The results show that using our method, pose tracking in image sequences and the convergence rate for randomly generated poses are improved.
Differential epipolar constraint in mobile robot egomotion estimation

The estimation of camera egomotion is a well established problem in computer vision. Many approaches have been proposed based on both the discrete and the differential epipolar constraint. The discrete case is mainly used in self-calibrated stereoscopic systems, whereas the differential case deals with a unique moving camera. The article surveys several methods for mobile robot egomotion estimation covering more than 0.5 million samples using synthetic data. Results from real data are also given.
Iterative multistep explicit camera calibration

Perspective camera calibration has been a research subject for a large group of researchers. However, only a small number of those methods base their approaches on the use of monoplane calibration points. We developed one of those methodologies using monoplane calibration points to perform an explicit three-dimensional (3-D) camera calibration. This methodology is based on an iterative approach. To avoid the singularities obtained with the calibration equations when monoplane calibration points are used, this method computes the calibration parameters in a multistep procedure and requires a first-guess solution for the intrinsic parameters. These intrinsic parameters are updated and their accuracy increased during the iterative procedure. All computations required are linear and in addition to the extrinsic parameters the proposed method also computes the first coefficient of the radial distortion (k/sub 1/) and the skew angle. A first-guess value for the focal length of the lens is required but its value is iteratively updated using the Gauss lens model. This methodology also includes the uncertainty horizontal image scale factor (S/sub x/) on the set of calibration parameters to be computed, which makes this approach independent of the accuracy of the horizontal scale factor. The proposed methodology has the advantage that it can be used with monoplane calibration data with no restrictions for the pose geometry of the camera. In this approach the pose estimation problem is treated separately, computing the pose orientation of the camera in a first step and using this information to compute the pose location. A model for the expected stability of the camera look angles from noisy image data and its stability analysis as a function of the pose geometry of the camera is presented. Several experimental and simulated analyzes were performed and are presented.
Point-based calibration using a parametric representation of the general imaging model

Generic imaging models can be used to represent any camera. These models are specially suited for non-central cameras for which closed-form models do not exist. Current models are discrete and define a mapping between each pixel in the image and a straight line in 3D space. Due to difficulties in the calibration procedure and model complexity these methods have not been used in practice. The focus of our work was to relax these drawbacks. In this paper we modify the general imaging model using radial basis functions to interpolate image coordinates and 3D lines allowing both an increase in resolution (due to their continuous nature) and a more compact representation. Using this new variation of the general imaging model we also develop a new linear calibration procedure. In this process it is only required to match one 3D point to each image pixel. Also it is not required the calibration of every image pixel. As a result the complexity of the procedure is significantly decreased.
Pursuit control in a binocular active vision system using optical flow

An active vision system must be able to perform reactive visual processes in real time. In this paper we discuss a number of issues related to the implementation of a real-time control architecture and describe the architecture we use with camera heads. Another important issue of the operation of active vision binocular heads is their integration into more complex robotic systems. Higher levels of autonomy and integration can be obtained by designing the control system architecture based on the concept of purposive behavior. At the lower levels we consider the vision as a sensor and integrate it in the control systems (both feedforward and servo loops); and several visual processes are implemented in parallel, computing relevant measures for the control process. At higher levels the architecture is modelled as a state transition system. Finally, we show how this architecture can be used to implement a pursuit behavior using optical flow. Simultaneously vergence control can also be performed using the same visual processes.
Shape-based attention for identification and localization of cylindrical objects

In this paper, we propose a novel framework for detecting and identifying cylindrical shapes, commonly found in daily contexts, using multi-modal visual information provided by RBG-D cameras. The current state-of-the-art methods for cylinder detection are based on RANSAC and Hough transforms which estimate cylinder parameters using 3D point cloud information. However, the presence of distracting non-cylindrical shapes leads to time-consuming parametric fitting of wrong detections, compromising the efficiency of the whole method. We tackle the aforementioned problem by introducing a biologically plausible framework, incorporating a pre-attentive mechanism which learns in a supervised and data-efficient manner to selectively discard irrelevant shapes before further processing. A set of experiments with real data are conducted to assess the advantages of our framework. The results demonstrate that combining bottom-up 3D segmentation with top-down shape-based attention allows for large speedup and accuracy improvements on cylinder identification. The qualitative and quantitative results with real data acquired from a consumer RGB-D camera, confirm the advantages of the proposed framework.
Model predictive control to improve visual control of motion: applications in active tracking of moving targets

Deals with active tracking of 3D moving targets. Visual tracking is presented as a regulation control problem. The performance and robustness in visual control of motion depends both on the vision algorithms and the control structure. Delays and system latencies substantially affect the performance of visually guided systems. We discuss ways to cope with delays while improving system performance. Model predictive control strategies are proposed to compensate for the mechanical latency in visual control of motion.
Efficient decoupled pose estimation from a set of points

This paper deals with pose estimation using an iterative scheme. We show that using adequate visual information, pose estimation can be performed in a decoupling the estimation of translation and rotation. More precisely, we show that pose estimation can be achieved iteratively as a function of only three independent unknowns, which are the translation parameters. An invariant to rotational motion is used to estimate the camera position. Once the camera position is estimated, we show that the rotation can be estimated efficiently using a direct method. The proposed approach is compared against two classical methods from the literature. The results show that using our method, pose tracking in image sequences and the convergence rate for randomly generated poses are improved.
Projection model, 3D reconstruction and rigid motion estimation from non-central catadioptric images

This paper addresses the problem of rigid motion estimation and 3D reconstruction in vision systems where it is possible to recover the incident light ray direction from the image points. Such systems include pinhole cameras and catadioptric cameras. Given two images of the same scene acquired from two different positions, the transformation is estimated by means of an iterative process. The estimation process aims at having corresponding incident rays intersecting at the same 3D point. Geometrical relationships are derived to support the estimation method. Furthermore, this paper also addresses the problem of the mapping from 3D points to image points, for non-central catadioptric cameras with mirror surfaces given by quadrics. The projection model presented can be expressed in a non-linear equation of only one variable, being more stable and easier to solve than the classical Snell's law. Experiments with real images are presented, by using simulated annealing as estimation method.
A surveillance system integrating visual telepresence

We describe an indoor surveillance system which enables the control of a binocular active vision system by a remote operator wearing a head mounted display. Visual feedback to the head mounted display is obtained using the images captured by the active vision system. To maximize the 3D perception of the operator the system is able to adjust the vergence angle of the active vision system in order to maintain fixation on the objects in the center of the image. A wide-angle lens static camera located on the surveillance area is also used to enable the detection and tracking of intruders. In case of an intrusion a map with the location of the detected targets is displayed on top of the images shown on the head mounted display. This information allows the remote operator to redirect his attention to where the target is.
Mirror shape recovery from image curves and intrinsic parameters: Rotationally symmetric and conic mirrors

This paper analyzes the problem of the estimation of the local mirror shape in a catadioptric imaging system. We propose a method to recover the 3D coordinates of mirror surface points as long as there are images of those points, i.e., as long as there is an image of a 3D geometric element that is reflected by those points. For that purpose the information required is the image, the intrinsic parameters of the camera, and the 3D coordinates of 3 points in the scene. The estimation of the local shape can be used to calibrate the system even though that problem is not addressed in this paper. We address the problem of the shape recovery for conic shaped mirrors and rotationally symmetric mirrors. Experimental results for synthetic images are presented.
Direct Least Square Fitting of Paracatadioptric Line Images

Paracatadioptric sensors combine a parabolic shaped mirror and a camera inducing an orthographic projection. Such a configuration provides a wide field of view while keeping a single effective viewpoint. In general the paracatadioptric image of a line is a conic curve. The estimation of line images is an important subject for applications such as reconstruction and visual control of motion. However the estimation of the conic curves where lines are mapped is hard to accomplish. In general only a small arc of the conic is visible in the image and conventional conic fitting techniques are unable to correctly estimate the curve. This paper shows that line images can be accurately estimated by constraining the search space. A conic curve is the paracatadioptric image of a line if, and only if, the image of the circular points lie on the curve and two certain points are conjugate with respect to the conic. Considering the space of all conic curves, the line images lie in a linear subspace which depends on the system calibration. The paracatadioptric projection of a line can estimated by fitting a conic in the subspace to the data points. The proposed approach is computationally efficient since the fitting problem can be solved by an eigensystem
A simple and robust solution to the minimal general pose estimation

In this article we address the problem of minimal pose under the framework of the generalized camera models. Previous approaches were based on geometric properties, such as the preservation of distance between points. In this paper we propose a novel formulation of the problem using an algebraic-based approach. We represent the pose by a 3×3 matrix. Using both the algebraic relationship between three incident 3D points and straight lines and the underlying constraints of the pose matrix, pose can be computed. In terms of experimental results, the main contribution of the proposed method is the robustness to critical configurations. In addition, a full comparison and analysis between state-of-the-art methods is made (so far, there is no published comparison between state-of-the-art methods published).
Planar pose estimation for general cameras using known 3D lines

In this article, we address the pose estimation for planar motion under the framework of generalized camera models. We assume the knowledge of the coordinates of 3D straight lines in the world coordinate system. Pose is estimated using the images of the 3D lines. This approach does not require the determination of correspondences between pixels and 3D world points. Instead, and for each pixel, it is only required that we determine to which 3D line it is associated with. Instead of identifying individual pixels, it is only necessary to establish correspondences between the pixels that belong to the images of the 3D lines, and the 3D lines. Moreover and using the assumption that the motion is planar, this paper presents a novel method for the computation of the pose using general imaging devices and assuming the knowledge of the coordinates of 3D straight lines. The approach is evaluated and validated using both synthetic data and real images. The experiments are performed using a mobile robot equipped with a non-central camera.
Camera recalibration with hand-eye robotic system

The authors address the geometric modeling aspects related to the use of a pair of video cameras mounted on a six-degree-of-freedom manipulator in a hand-eye configuration. The information obtained by these sensors will be used to generate the depth map of the environment where the manipulator works. The procedure for determining the image formation parameters is known by camera calibration, and a method to optimize these calibration parameters is presented. A calibration matrix invariant to movement is developed. The optimization process is applied to this matrix to obtain better performance in the numerical data. This optimization process is the basis for a recalibration procedure that exploits the capability of positioning of the cameras in different positions within the workspace of the robot. The first optimization process used is a recursive form of the least-squares method and the second is the Kalman filter. Experimental results obtained show that the recalibration technique gives some improvement in the numerical data and stabilizes the calibration matrix along the different positions of the vision system within the robot workspace.<<ETX>>
A parallel architecture for texture analysis based on the concept of associative memory

A special architecture for texture analysis and recognition that is inherently parallel and aimed at hardware implementation is described. It has the advantage of making possible real-time recognition (at frame rates). The architecture is based on the concept of associative memory, which has learning and recognizing capabilities. This architecture achieved high rates of recognition. With image processing, over 90% correct classification was obtained. Starting from the concept of associative memory, several implementations were considered and compared.<<ETX>>
Paracatadioptric camera calibration using lines

Paracatadioptric sensors combine a parabolic shaped mirror and a camera inducing an orthographic projection. Such a configuration provides a wide field of view while keeping a single effective viewpoint. Previous work in central catadioptric sensors proved that a line projects into a conic curve and that three line images are enough to calibrate the system. However the estimation of the conic curves where lines are mapped is hard to accomplish. In general only a small arc of the conic is visible in the image and conventional conic fitting techniques are unable to correctly estimate the curve. The present work shows that a set of conic curves corresponds to paracatadioptric line images if, and only if, certain properties are verified. These properties are used to constraint the search space and correctly estimate the curves. The accurate estimation of a minimum of three line images allows the complete calibration of the paracatadioptric camera. If the camera is skewless and the aspect ratio is known then the conic fitting problem is solved naturally by an eigensystem. For the general situation the conic curves are estimated using non-linear optimization.
Real-time visual behaviors with a binocular active vision system

We describe the real-time implementation of several visual behaviors in an active vision system. Issues related to the real-time implementation are discussed, concerning the modeling of the measurements made in the image. We also describe a methodology for calibrating the camera head, taking advantage of its degrees of freedom. These calibration parameters are used to evaluate the performance of the system. Another important issue of the operation of active vision binocular heads is their integration into more complex robotic systems. We claim that higher levels of autonomy and integration can be obtained by designing the system architecture based on the concept of purposive behavior. We show how this architecture can be used to implement a pursuit behavior using optical flow. Simultaneously vergence control can also be performed using the same visual processes.
Pose view stability analysis for camera look angles computation

The main purpose of this paper is not to present a new camera calibration algorithm or a new pose estimation method, but just to analyse the stability of the camera pose estimation using the Haralick observation which is based on the fact that there is sufficient information on the 2D perspective projection of a rectangle of unknown size in 3D space to determine the three camera look angles. A brief description of the camera look angles computation is presented and a model for the expected stability of the camera look angles computation front noisy image data is described. A stability analysis as a function of the pose of the camera is also described, and the influence of the camera pose is confirmed with simulated data. As a result, camera pose view strategies for accurate camera orientation computation can be extracted from this pose view stability analysis.
Simulating pursuit with machines. Experiments with robots and artificial vision

This article describes one solution for the problem of pursuit by using a mobile robot and an active vision system. The solution deals with the interaction of different control systems using visual feedback and it is accomplished by the implementation of a visual gaze holding process interacting cooperatively with the control of the trajectory of a mobile robot. These two systems are integrated to follow a moving object at constant distance and orientation with respect to the mobile robot. The orientation and the position of the active vision system running a gaze holding process give the feedback signals to the control used to pursuit the target in real-time. The mechanisms of cooperation between the different control and visual algorithms are described. The final solution is a system able to operate at approximately human walking rates.