nils-herrmann/clean_scirepeval huggingface dataset:
Split aperture imaging for high dynamic range

Most imaging sensors have limited dynamic range and hence are sensitive to only a part of the illumination range present in a natural scene. The dynamic range can be improved by acquiring multiple images of the same scene under different exposure settings and then combining them. In this paper, we describe a camera design for simultaneously acquiring multiple images of the same scene under different exposure settings. The cross-section of the incoming beam from a scene point is partitioned into as many parts as the desired degree of split. This is done by splitting the aperture into multiple parts and directing the light exiting from each in a different direction using an assembly of mirrors. A sensor is placed in the path of each beam and exposure of each sensor is controlled either by appropriately setting its exposure parameter, or by splitting the incoming beam unevenly. The resulting multiple exposure images are used to construct a high dynamic range image. We have implemented a video-rate camera based on this design and the results obtained are presented.
Motion from images: image matching, parameter estimation and intrinsic stability

Presents an image-matching algorithm that uses multiple attributes associated with a pixel to yield a generally overdetermined system of constraints. taking into account possible structural discontinuities and occlusions. Both top-down and bottom-up data flows are used in a multiresolution computational structure. The matching algorithm computes dense displacement fields and the associated occlusion maps. The motion and structure parameters are estimated through optimal estimation (e.g. maximal likelihood) using the solution of a linear algorithm as an initial guess. To investigate the intrinsic stability of the problem in the presence of noise, a theoretical lower bound on error variance of the estimates, the Cramer-Rao bound, is determined for motion parameters.<<ETX>>
Integration of transitory image sequences

A transitory image sequence is one in which no scene element is visible through the entire sequence. This article deals with some major theoretical and algorithmic issues associated with the task of estimating structure and motion from transitory image sequences. Two representations, world-centered (WC) and camera-centered (CC), behave very differently with a transitory sequence. The asymptotical error properties derived in this article indicate that one representation is significantly superior to the other, depending on whether one uses camera-centered or world-centered estimates. Rigorous experiments were conducted with real-image sequences taken by a fully calibrated camera system. The comparison demonstrated that a good accuracy can be obtained from transitory image sequences.<<ETX>>
Surface Reconstruction By Dynamic Integration Of Focus, Camera Vergence, And Stereo

This paper concerns estimation of surface maps for real scenes having a wide field of view and a wide range of depths. Much research has emphasized stereo disparity as a source of depth information. To a lesser extent, camera focus and camera vergence have also been investigated for their utility in depth recovery. We argue that these sources of visual information have mutually complementary strengths and weaknesses, and to obtain surface maps for real scenes these processes must be integrated. Such integration requires active control of camera orientations and imaging parameters to dynamically and cooperatively interleave image acquisition with surface estimation. Accordingly, a global surface map of the visual field is synthesized by systematically scanning the scene, and combining estimates of adjacent, local surface patches, each acquired by an intermediate camera configuration and having a small depth range. We present an algorithm to perform this integration, and describe its implementation on a dynamic stereo-camera imaging system. Experimental results are presented to demonstrate the superior performance of the integrated system over that of each of its components.
Video denoising by combining Kalman and Wiener estimates

The paper proposes a computationally fast scheme for denoising a video sequence. Temporal processing is done separately from spatial processing and the two are then combined to get the denoised frame. The temporal redundancy is exploited using a scalar state 1D Kalman filter. A novel way is proposed to estimate the variance of the state noise from the noisy frames. The spatial redundancy is exploited using an adaptive edge-preserving Wiener filter. These two estimates are then combined using simple averaging to get the final denoised frame. Simulation results for the foreman, trevor and susie sequences show an improvement of 6 to 8 dB in PSNR over the noisy frames at PSNR of 28 and 24 dB.
Generalized Radial Alignment Constraint for Camera Calibration

In camera calibration, the radial alignment constraint (RAC) has been proposed as a technique to obtain closed form solution to calibration parameters when the image distortion is purely radial about an axis normal to the sensor plane. But, in real images this normality assumption might be violated due to manufacturing limitations or intentional sensor tilt. A misaligned optic axis results in traditional formulation of RAC not holding for real images leading to calibration errors. In this paper, we propose a generalized radial alignment constraint (gRAC), which relaxes the optic axis-sensor normality constraint by explicitly modeling their configuration via rotation parameters which form a part of camera calibration parameter set. We propose a new analytical solution to solve the gRAC for a subset of calibration parameters. We discuss the resulting ambiguities in the analytical approach and propose methods to overcome them. The analytical solution is then used to compute the intersection of optic axis and the sensor about which overall distortion is indeed radial. Finally, the analytical estimates from gRAC are used to initialize the nonlinear refinement of calibration parameters. Using simulated and real data, we show the correctness of the proposed gRAC and the analytical solution in achieving accurate camera calibration.
Improving the throughput of flexible-precision DSPS via algorithm transformation

In this paper, we have presented a systematic technique to improve throughput of signal/image processing algorithms when implemented on flexible precision hardware. Many image/signal processing algorithms need 8-16 bit precision while the DSPs available are of much higher precision (32 bit). Significant performance gain can be obtained if multiple low precision computations can be performed in one cycle of a high precision DSP. We have proposed a framework based on algorithm transformation techniques of unfolding and retiming to systematically map low precision algorithms onto high precision DSPs. The improvement in throughput obtained by this framework is linearly related to the ratio of precision used by the processor and that required by the algorithm. The efficacy of this technique has been demonstrated on a IIR filter. We have also established some theoretical bounds on the maximum throughput that can be achieved using the proposed methodology.
Integrated 3-D Analysis and Analysis-Guided Synthesis of Flight Image Sequences

This paper is concerned with three-dimensional (3D) analysis, and analysis-guided syntheses, of images showing 3-D motion of an observer relative to a scene. There are two objectives of the paper. First, it presents an approach to recovering 3D motion and structure parameters from multiple cues present in a monocular image sequence, such as point features, optical flow, regions, lines, texture gradient, and vanishing line. Second, it introduces the notion that the cues that contribute the most to 3-D interpretation are also the ones that would yield the most realistic synthesis, thus suggesting an approach to analysis guided 3-D representation. For concreteness, the paper focuses on flight image sequences of a planar, textured surface. The integration of information in these diverse cues is carried out using optimization. For reliable estimation, a sequential batch method is used to compute motion and structure. Synthesis is done by using (i) image attributes extracted from the image sequence, and (ii) simple, artificial image attributes which are not present in the original images. For display, real and/or artificial attributes are shown as a monocular or a binocular sequence. Performance evaluation is done through experiments with one synthetic sequence, and two real image sequences digitized from a commercially available video tape and a laserdisc. The attribute based representation of these sequences compressed their sizes by 502 and 367. The visualization sequence appears very similar to the original sequence in informal, monocular as well as stereo viewing on a workstation monitor.
A Comparative Study for Single Image Blind Deblurring

Numerous single image blind deblurring algorithms have been proposed to restore latent sharp images under camera motion. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real blurred images. It is thus unclear how these algorithms would perform on images acquired "in the wild" and how we could gauge the progress in the field. In this paper, we aim to bridge this gap. We present the first comprehensive perceptual study and analysis of single image blind deblurring using real-world blurred images. First, we collect a dataset of real blurred images and a dataset of synthetically blurred images. Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms. Second, we systematically analyze subject preferences, including the level of agreement, significance tests of score differences, and rationales for preferring one method over another. Third, we study the correlation between human subjective scores and several full-reference and noreference image quality metrics. Our evaluation and analysis indicate the performance gap between synthetically blurred images and real blurred image and sheds light on future research in single image blind deblurring.
On stochastic gradient descent and quadratic mutual information for image registration

Mutual information (MI) is quite popular as a cost function for intensity based registration of images due to its ability to handle highly non-linear relationships between intensities of the two images. More recently, quadratic mutual information (QMI) has been proposed as an alternative measure that computes Euclidean distance instead of KL divergence between the joint and the product of the marginal densities of pixel intensities. In this paper, we examine the conditions under which QMI is advantageous over the classical MI measure, for the image registration problem. We show that QMI is a better cost function to use for optimization methods such as stochastic gradient descent. We show that the QMI cost function remains much smoother than the classical MI measure on stochastic subsampling of the image data. As a consequence, QMI has a higher probability of convergence, even for larger degrees of initial misalignment of the images.
Transformation guided image completion

In this paper, we describe a new interactive image completion system that allows users to easily specify various forms of mid-level structures in the image. Our system supports the specification of four basic symmetric types: reflection, translation, rotation, and glide. The user inputs are automatically converted into guidance maps that encode possible candidate shifts and, indirectly, local transformations of rotation and scale. These guidance maps are used in conjunction with a color matching cost for image completion. We show that our system is capable of handling a variety of challenging examples.
Models of large population recognition performance

We present new binomial models of open- and closed-set identification recognition performance, giving formulae for identification and false match rates as functions of database size, match rank and operating threshold. We compare these with previously published models and with results from face recognition trials on populations of size 4 10/sup 4/. We note verification to be a special case of open-set identification and relate area under the receiver operating characteristic to closed-set identification. We find the binomial model approximates performance at low false match rates but underestimates identification rates on closed sets. We implicate the binomial iid assumption, but show conditioning and score transformation methods that ameliorate this.
Multiview panoramic cameras using a mirror pyramid

A mirror pyramid consists of a set of flat mirror faces arranged around an axis of symmetry, inclined to form a pyramid. By strategically positioning a number of conventional cameras around a mirror pyramid, the viewpoints for the individual camera's mirror images can be colocated at a single point within the pyramid, effectively forming a virtual camera with a wide field of view. Mirror pyramid-based panoramic cameras have a number of attractive properties, including single-viewpoint imaging, high resolution and video rate capture. Currently existing designs realize a single viewpoint within each mirror pyramid. In order to capture panoramas from multiple viewpoints with these designs, the entire physical setup would need to be relocated or duplicated. The former solution lacks the capability of video rate imaging, and the latter leads to bulky designs due to the multiple mirror pyramids. In this paper we propose a method for generalizing existing designs such that multiple viewpoints can be created in a single mirror pyramid. This enables simultaneous multiview panoramic video rate imaging with a compact design.
Structure and motion estimation from dynamic silhouettes under perspective projection

Addresses the problem of estimating the structure and motion of a smooth curved object from its silhouettes observed over time by a trinocular stereo rig under perspective projection. We first construct a model for the local structure along the silhouette for each frame in the temporal sequence. Successive local models are then integrated into a global surface description by estimating the motion between successive time instants. The algorithm tracks certain surface features (parabolic points) and image features (silhouette inflections and frontier points) which are used to bootstrap the motion estimation process. The entire silhouette along with the reconstructed local structure are then used to refine the initial motion estimate. We have implemented the proposed approach and report results on real images.<<ETX>>
Joint spatial and frequency domain motion analysis

Traditionally, motion estimation and segmentation have been performed mostly in the spatial domain, i.e., using the luminance information in the video sequence. Frequency domain representation offers an alternative, rich source of motion information, which has been used to a very limited extent in the past, and on relatively simple problems such as image registration. We review our work during the last few years on an approach to video motion analysis that combines spatial and Fourier domain information. We review our methods for (1) basic (translation and rotation) motion estimation and segmentation, for multiple moving objects, with constant as well as time varying velocities; and (2) more complicated motions, such as periodic motion, and periodic motion superposed on translation. The joint space analysis leads to more compact and computationally efficient solutions than existing techniques
Seamless video editing

This paper presents a new framework for seamless video editing in the gradient domain. The spatio-temporal gradient fields of target videos are modified or mixed to generate a new gradient field, which is usually not integrable. We propose a 3D video integration algorithm, which finds a potential function, whose gradient field is closest to the resulting gradient field in the sense of least squares. The video is reconstructed by solving a 3D Poisson equation. We use a fast and accurate 3D discrete Poisson solver using diagonal multigrids. A set of gradient operators is defined for user interaction. The resulting video has temporal coherency and no artifacts. We evaluate our algorithm using a variety of examples.
Sufficient conditions for double or unique solution of motion and structure

Several sufficient conditions are presented for a double or unique solution of the problem of motion and structure from two monocular images. It is shown that: five correspondences of points that do not lie on two lines in the image plane suffice to determine a pure rotation uniquely; six correspondences of points that do not lie on two lines in the image plane and do not correspond to space points lying on a specific quadric surface suffice to determine a motion with nonzero translation uniquely; each Maybank quadric can sustain at most two physically acceptable motion solutions and surface interpretations, provided that a sufficient number of correspondences are present; in the plane motion case, six correspondences of points that do not lie on a quadric curve in the image plane will only admit the true motion and structure and their duals as solutions. Several properties of the essential matrix T*R and the plane motion matrix R+TN/sup T/, both of which are frequently used in the motion and structure estimation problem, are listed.<<ETX>>
A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection

Describes a transform to extract image regions at all geometric and photometric scales. It is argued that linear approaches have the shortcoming that they require a priori models of region shape. The proposed transform avoids this by letting the structure emerge, bottom-up, from interactions among pixels. The transform involves global computations on pairs of pixels followed by vector integration of the results. An attraction force field is computed over the image in which pixels belonging to the same region are mutually attracted and the region is characterized by a convergent flow. It is shown that the transform possesses properties that allow multiscale segmentation, or extraction of original, unblurred structure at all different geometric and photometric scales present. This is in contrast with much previous work wherein multiscale structure is viewed as the smoothed structure in a multiscale signal decimation. Scale is an integral parameter of the force computation, and the number and values of scale parameters associated with the image can be estimated automatically. Regions are detected at all a priori unknown scales resulting in automatic construction of a segmentation tree, in which each pixel is annotated with descriptions of all the regions it belongs to. Transform properties are presented for piecewise-constant images but hold for more general ones. Thus the proposed method is intended as a solution to the problem of multiscale, integrated edge and region detection, or low-level image segmentation. Experimental results are given.
Estimation of the light source distribution and its use in integrated shape recovery from stereo and shading

The authors deal with estimation of the light source distribution and its use in surface shape estimation by the methods of stereo and shading. There are many advantages to integrating stereo and shading information. However, shape from shading algorithms are limited in their applicability by the assumption of overly simplistic models of the light source distribution. A more complete representation is described in which the lighting model makes use of multiple fixed point sources located at infinity. Methods of estimating the model parameters are developed, and a method of estimating the surface shape given the albedo and source distribution is presented. The shading algorithm is combined with a stereo algorithm in an integrated approach that is designed to handle albedo variations through the use of color images.<<ETX>>
Panoramic image acquisition

This paper is concerned with acquiring panoramic focused images using a small field of view video camera. When scene points are distributed over a range of distances from the sensor, obtaining a focused composite image involves focus computations and mechanically changing some sensor parameters (translation of sensor plane, panning of camera etc.) which can be time intensive. In this paper we present methods to optimize the image acquisition strategy in order to reduce redundancy. We show that panning a camera about a point f (focal length) in front of the camera eliminates redundancy. The non-frontal imaging camera (NICAM) with tilted sensor plane has been previously introduced as a sensor that can acquire focused panoramic images. In this paper we also describe strategies for optimal selection of panning angle increments and sensor plane tilt for NICAM. Experimental results are presented for panoramic image acquisition using a regular camera as well as using NICAM.
Motion estimation under orthographic projection

Some new results for the problem of motion estimation under orthographic projection are presented. Some basic results obtained by previous researchers are refined and more detailed and precise results are provided. It is shown that, in the two-view problem, when the rotation is around the optical axis, the motion (but not the structure) is uniquely determined. In the three-view problem, only under certain conditions are the motion and structure uniquely determined. For any motion problem, if two-view matching cannot determine the motion, only under certain conditions can three-view or multiview matching help.
Detecting Migrating Birds at Night

Bird migration is a critical indicator of environmental health, biodiversity, and climate change. Existing techniques for monitoring bird migration are either expensive (e.g., satellite tracking), labor-intensive (e.g., moon watching), indirect and thus less accurate (e.g., weather radar), or intrusive (e.g., attaching geolocators on captured birds). In this paper, we present a vision-based system for detecting migrating birds in flight at night. Our system takes stereo videos of the night sky as inputs, detects multiple flying birds and estimates their orientations, speeds, and altitudes. The main challenge lies in detecting flying birds of unknown trajectories under high noise level due to the low-light environment. We address this problem by incorporating stereo constraints for rejecting physically implausible configurations and gathering evidence from two (or more) views. Specifically, we develop a robust stereo-based 3D line fitting algorithm for geometric verification and a deformable part response accumulation strategy for trajectory verification. We demonstrate the effectiveness of the proposed approach through quantitative evaluation of real videos of birds migrating at night collected with near-infrared cameras.
Parallel distributed detection of feature trajectories in multiple discontinuous motion image sequences

This paper is concerned with three-dimensional interpretation of image sequences showing multiple objects in motion. Each object is assumed to have point features which are detected automatically and exhibits smooth motion except at times when a motion discontinuity may occur. The problem of estimating initial feature trajectories is that of matching between the first two frames. As more images are acquired, existing trajectories are extended. Both initial detection and extension are formulated as energy minimization problems that are solved using a Hopfield network. The energy function incorporates pertinent 2-D and 3-D geometric constraints. A second, 1-D Hopfield-like network is used to eliminate wrong matches among those found by the first network.