nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian estimation of subpixel-resolution motion fields and high-resolution video stills

Multiframe resolution enhancement methods are used to estimate a high-resolution video still (HRVS) from several low-resolution image sequence frames, provided that objects within the video sequence move with subpixel increments. Estimating accurate subpixel-resolution motion vectors is a challenging, albeit critically important component of super-resolution enhancement algorithms. A Bayesian motion estimation technique is proposed which models the motion field with a discontinuity-preserving prior. The method is related to Horn-Schunck (1981) optical flow estimation, except that the discontinuity-preserving prior can allow abrupt changes within the motion field without the use of line processes. Simulations compare the high-resolution video stills which result from using the subpixel motion vectors calculated by block matching and the proposed Bayesian motion estimation technique.
Morphological filters: Statistics and further syntactic properties

Mathematical morphology has recently been introduced as a powerful tool for studying the geometrical properties of signals and systems. These techniques have been applied very successfully to the smoothing of noisy data. In this paper, we first derive some new mathematical morphology results for function and set processing (FSP) systems. Using these results we derive several statistical results for the (FSP) morphological filtering of random signals, by deriving probabilistic mappings between input and output signals. Finally, we introduce a two-dimensional filter for image restoration which has desirable structure preserving properties.
Improved image decompression for reduced transform coding artifacts

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image that best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem that can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality.
A Similarity Metric for Multimodal Images Based on Modified Hausdorff Distance

This paper presents a similarity metric on multimodal images utilizing curves as comparing primitives. Curves are detected from images, and then junctions are detected along curves and used to partition curves into subcurves. A modified Hausdorff distance is applied to determine whether a test subcurve is matched to a reference curve. The similarity metric is defined to be the number of matched curves. The number of overlapped edge pixels between two images is also defined on the basis of matched curves, which does not require accurately localizing edge pixels. The partitioning scheme avoids addresing curve partial matching and allows for test subcurves being matched to a reference curve if they correspond to each other. Experimental results show that the presented similarity metric gives more robust and reliable results, especially under noise.
Invariant reconstruction of visual surfaces

This study addresses a problem that arises in the early stages of visual processing. Low-level vision processing tasks generate information describing distances and orientations at sparse locations of visible surfaces in a scene. The information from these various low-level tasks need to be combined to form an estimate of the visual surface for all points in the scene. This step is commonly referred to as the surface reconstruction problem. The problem is cast as an ill-posed inverse problem, which must be stabilized using a priori information relative to image and constraint formation. A desirable property of any reconstruction algorithm is invariance with respect to rigid transformations of the surface in three-dimensional space. Two surface reconstruction algorithms that are based on invariant surface characteristics are proposed. These algorithms are studied in detail, and an example is presented to demonstrate their effectiveness. The discrete realization of the algorithms is also briefly discussed.<<ETX>>
Discontinuity preserving regularization of inverse visual problems

The method of Tikhonov regularization has been widely used to form well-posed inverse problems in low-level vision. The application of this technique usually results in a least squares approximation or a spline fitting of the parameter of interest. This is often adequate for estimating smooth parameter fields. However, when the parameter of interest has discontinuities the estimate formed by this technique will smooth over the discontinuities. Several techniques have been introduced to modify the regularization process to incorporate discontinuities. Many of these approaches however, will themselves be ill-posed or ill-conditioned. This paper presents a technique for incorporating discontinuities into the reconstruction problem while maintaining a well-posed and well-conditioned problem statement. The resulting computational problem is a convex functional minimization problem. This method is compared to previous approaches and examples are presented for the problems of reconstructing curves and surfaces with discontinuities and for estimating image data. Computational issues arising in both analog and digital implementations are also discussed.
Dynamic range improvement through multiple exposures

This paper presents an approach for improving the effective dynamic range of cameras by using multiple photographs of the same scene taken with different exposure times. Using this method enables the photographer to accurately capture scenes that contain a high dynamic range, i.e., scenes that have both very bright and very dark regions. The approach requires an initial calibration, where the camera response function is determined. Once the response function for a camera is known, high dynamic range images can be computed easily. The high dynamic range output image consists of a weighted average of the multiply-exposed input images, and thus contains information captured by each of the input images. From a computational standpoint, the proposed algorithm is very efficient, and requires little processing time to determine a solution.
Feedthrough EMI filter with ground isolation for cardiac pacemakers and implantable cardioverter defibrillators

This paper describes an Isolated Ground Capacitor (IGC) electromagnetic interference (EMI) feedthrough filter that generally makes cardiac pacemakers and implantable cardioverter defibrillators (ICDs) immune to the RF fields generated by cellular phones and other emitters. The IGC filter consists of a monolithic ceramic capacitor (MLC) in a feedthrough configuration which is directly mounted onto the hermetic terminal of the implantable medical device. The IGC MLC employs unique electrode plate geometry and manufacturing techniques that provide for isolation of pacemaker or ICD circuitry from the titanium shield (ground plane).
Output-queued switch emulation by fabrics with limited memory

The output-queued (OQ) switch is often considered an ideal packet switching architecture for providing quality-of-service guarantees. Unfortunately, the high-speed memory requirements of the OQ switch prevent its use for large-scale devices. A previous result indicates that a crossbar switch fabric combined with lower speed input and output memory and two times speedup can exactly emulate an OQ switch; however, the complexity of the proposed centralized scheduling algorithms prevents scalability. This paper examines switch fabrics with limited memory and their ability to exactly emulate an OQ switch. The switch architecture of interest contains input queueing, fabric queueing, flow-control between the limited fabric buffers and the inputs, and output queueing. We present sufficient conditions that enable this combined input/fabric/output-queued switch with two times speedup to emulate a broad class of scheduling algorithms operating an OQ switch. Novel scheduling algorithms are then presented for the scalable buffered crossbar fabric. It is demonstrated that the addition of a small amount of memory at the crosspoints allows for distributed scheduling and significantly reduces scheduling complexity when compared with the memoryless crossbar fabric. We argue that a buffered crossbar system performing OQ switch emulation is feasible for OQ switch schedulers such as first-in-first-out, strict priority and earliest deadline first, and provides an attractive alternative to both crossbar switch fabrics and to the OQ switch architecture.
Region-activity-based pyramidal image coder using generalized rank-ordered prediction filter

This paper presents a lossy image compression scheme which employs a pyramidal coding technique with segmentation based on the local activity levels. A novel prediction method is introduced, rendering significantly reduced variance of the quantizer input. Consequently, the coding efficiency is much enhanced due to the greatly reduced quantization distortion. Both theoretical analysis and simulation results show that the proposed scheme yields high quality performance. A reconstruction PSNR of 30.5 dB is achieved at a bit rate of 0.181 bpp for a 512×512 image "Lenna" before entropy coding.
Fixed length entropy coding for robust video compression

Entropy coding is a fundamental stage in all video compression algorithms. Variable length entropy codes (VLC) are used in current video codecs. Designed to be employed in noiseless applications, these codes are very sensitive to transmission errors. This paper proposes the use of fixed length entropy codes (FLC) as an alternative to VLC in video compression applications. In noisy transmissions the FLC codes have shown a superior performance compared to VLC schemes with synchronization words.
Contour tracking based on a synergistic approach of geodesic active contours and conditional random fields

This paper presents a new general framework for contour tracking based on the synergy of two powerful segmentation tools, namely, spatial temporal conditional random fields (CRFs) and geodesic active contours (GACs). The contours of targets are modeled using a level set representation. The evolution of the level sets toward the target contours is formulated as one of the joint region-based (CRF) and boundary-based (GAC) segmentations under a unified Bayesian framework. A variational inference technique is used to solve this otherwise intractable inference problem, leading to approximate MAP solutions of both the new 3D spatial temporal CRF and the GAC model. The tracking result of the previous frame is used to initialize the curve in the current frame. Typical contour tracking problems are considered and experimental results are given to illustrate the robustness of the method against noise and its accurate performance in moving objects boundary localization.
Enhancement of sub-sampled chrominance image data

In image communication systems, sub-sampling can be used to decrease the transmission rate of images. Since the Human Visual System is less sensitive to chrominance information, one common technique is to sub-sample chrominance information, leaving a high-resolution luminance plane. This process however creates artifacts and results in a degraded image which often contains false colors. Much better visual results can be obtained by enhancing the image after transmission. This paper develops an algorithm for enhancing color image data that has sub-sampled chrominance planes. The algorithm is based on stochastic regularization using the high-resolution luminance channel to guide the enhancement of the sub-sampled channels. Visual results are presented.
Reduction of coding artifacts in low-bit-rate video coding

The compression of digital video data has many applications in the transmission and storage of video sequences. For moderate compression ratios there are many techniques which can provide satisfactory performance. For high compression ratios, however, typical compression techniques produce noticeable artifacts in the reconstructed video. This paper proposes a technique for the post-processing of motion-compensated compressed video data. The technique utilizes a stochastic regularization approach which can be realized using a simple and fast iterative computational algorithm. The approach has been applied to the post-processing of color video sequences and yields good results.
The Evans & Sutherland view of tomorrow's supercomputing

A discussion is presented of the authors' view that a parallel supercomputing environment is necessary for today's large graphics, modeling, and simulation applications. These applications are used by scientists and engineers who make extreme demands on their computer resources. Such applications, accessed by a few key users, are typically critical to the success of a large operation. Such high-average applications point out where today's supercomputing environments need to be augmented. A summary is presented of some important hardware and software characteristics which these customers will require of tomorrow's superconducting environments.<<ETX>>
Reduction of coding artifacts in transform image coding

The problem of image decompression is cast as an ill-posed inverse problem, and a stochastic regularization technique is used to form a well-posed reconstruction algorithm. A statistical model for the image which incorporates the convex Huber minimax function is proposed. The use of the Huber minimax function rho T(.) helps to maintain the discontinuities from the original image which produces high-resolution edge boundaries. Since rho T(.) is convex, the resulting multidimensional minimization problem is a constrained convex optimization problem. The maximum a posteriori (MAP) estimation technique that is proposed results in the constrained optimization of a convex functional. The proposed image decompression algorithm produces reconstructed images which greatly reduced the noticeable artifacts which exist using standard techniques.<<ETX>>
Stochastic modeling and estimation of multispectral image data

The application of a monochromatic restoration algorithm to each channel within a multispectral image does not result in an estimate which takes into account cross-channel correlation. A non-Gaussian prior model is proposed for multispectral images, using the Gibbs distribution. The density accounts for both spatial (within channel) and spectral (between channel) information. Spatial components use a nonlinear operator to preserve discontinuities within each channel, while spectral components incorporate cross-channel information in the model. The prior density is used in a maximum a posteriori (MAP) estimation algorithm for the restoration of color images. The resulting nonlinear estimates are shown to be quantitatively superior to linear estimates produced by multichannel Wiener and least squares restoration, which implicitly use Gaussian priors.<<ETX>>
Corner-Guided Image Registration by using Edges

This paper proposes an image registration method. Edges are detected from images and partitioned into segments as matching primitives. Then, corners on the edges are detected to guide registration. A similarity metric is proposed based on the number of pairs of matching segments. Corner mappings are sequentially tried along a segment, from which a transformation is obtained. The corner mappings are evaluated by the similarity metric under their resulting transformation. By this means, corner mappings are established by utilizing whole images. Since the sensitivity of transformation parameters to the accuracy of corner mappings, as many corner mappings as possible are used. Experimental results show that the proposed method is robust, especially when there is no integral corresponding edges between two images.
Extraction of high-resolution frames from video sequences

The human visual system appears to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. While the mechanisms in the human visual system that do this are unknown, the effect is not too surprising given that temporally adjacent frames in a video sequence contain slightly different, but unique, information. This paper addresses the use of both the spatial and temporal information present in a short image sequence to create a single high-resolution video frame. A novel observation model based on motion compensated subsampling is proposed for a video sequence. Since the reconstruction problem is ill-posed, Bayesian restoration with a discontinuity-preserving prior image model is used to extract a high-resolution video still given a short low-resolution sequence. Estimates computed from a low-resolution image sequence containing a subpixel camera pan show dramatic visual and quantitative improvements over bilinear, cubic B-spline, and Bayesian single frame interpolations. Visual and quantitative improvements are also shown for an image sequence containing objects moving with independent trajectories. Finally, the video frame extraction algorithm is used for the motion-compensated scan conversion of interlaced video data, with a visual comparison to the resolution enhancement obtained from progressively scanned frames.
Fixed-length entropy coding for robust video compression

Entropy coding is a fundamental stage in all video compression algorithms in terms of compression efficiency and error resilience. Variable-length entropy codes (VLC) are used in current video codecs. Designed to be employed in noiseless applications, these codes are very sensitive to transmission errors. This paper proposes the use of fixed-length entropy codes (FLC) as an alternative to VLC in video compression applications. In noisy transmissions, the FLC-based codec has shown a superior performance compared to VLC-based codecs with synchronization words, while matching its performance in terms of coding efficiency.
Viewpoint invariant recovery of visual surfaces from sparse data

A new algorithm is described for the reconstruction of visual surfaces from sparse data. An important aspect of this algorithm is that the surface estimated from the sparse data is approximately invariant with respect to rigid transformation of the surface in three-dimensional space. To form a surface estimate which is invariant with respect to viewpoint the stabilizing information is based on invariant surface characteristics. With appropriate approximations this results in a convex functional which must be minimized. The relationship of this algorithm to several previously proposed reconstruction algorithms is discussed, and several examples are given which demonstrate the effectiveness of the proposed algorithm in reconstructing viewpoint invariant surface estimates.<<ETX>>
Robust transmission of compressed images over noisy Gaussian channels

The sensitivity of the compressed image representation to bit errors requires application of a channel code before transmission over noisy channels. To prevent the uncontrolled degradation caused by a channel error, an error controlling channel code is applied to the compressed representation before transmission. Many image communication systems have constraints on bandwidth, power and time which prohibit transmission of uncompressed raw image data. Compressed image formats, however, are extremely sensitive to bit errors which can seriously degrade the quality of the image at the receiver. A new list-based iterative trellis decoder is proposed which accepts feedback from a post-processor which can detect channel errors in the reconstructed image. Experimental results are shown which indicate the new decoder provides significant improvement over the standard Viterbi decoder.
Improved definition video frame enhancement

The human visual system seems to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. This paper addresses how to utilize both the spatial and temporal information present in an image sequence to create a high-resolution video still. A novel observation model based on motion compensated subsampling is proposed for a video sequence. Since the reconstruction problem is ill-posed, Bayesian restoration with an edge-preserving prior image model is used to extract a high-resolution video frame from a low-resolution sequence. Estimates computed from an image sequence containing a camera pan show dramatic improvement over bilinear, cubic B-spline, and Bayesian single frame interpolations. Improved definition is also shown for a video sequence containing objects moving with independent trajectories.
Multimodal Image Registration With Line Segments by Selective Search

This paper proposes a line segment-based image registration method. Edges are detected from images by a modified Canny operator, and line segments are then extracted from these edges. At registration, triplets (quaternions) of line segment correspondences are tentatively formed by applying the distance and orientation constraints, which determine an intermediate transformation. Those triplets (quaternions) of lines resulting in higher similarity metrics are preserved, and their intersections are refined by an iterative process or random sample consensus. The proposed method is tested on indoor and outdoor EO/IR image pairs, and the average registration error is calculated to be compared with existing algorithms. Experimental results show that the proposed registration method can robustly align EO/IR images containing line segments, providing more reliable and accurate registration results on multimodal images.
On the synthesis of median filter systems

In this paper, we address the problem of median filtering synthesis, that is, the design of a median filter system realization that will satisfy a given set of specifications. In particular, we address the problem of a cascade median filter realization, where the performance of a median filter is achieved through the cascade of several median filters of smaller window size. Because of the nonlinear nature of these filters, a statistical performance criterion is chosen for the system specifications. In order to evaluate the output of the cascade filter, a method is developed which finds the statistics of the roots of median filters, where roots are signals obtained after several median filter passes. Finally, a VLSI implementation for a cascade median filter system is presented.