nils-herrmann/clean_scirepeval huggingface dataset:
New adaptive iterative image restoration algorithm

It has been shown in the literature that adaptive regularized image restoration is superior to the non-adaptive case. However, the adaptivity introduced in most proposed iterative algorithms is based only on the application of the space-variant smoothing operator. It is found that these adaptive algorithms suffer from insufficient smoothing of the flat image regions. In this paper, an adaptive iterative image restoration algorithm, which applies both techniques of space-variant smoothing and space-variant restoration, is proposed to overcome the stated problem. It is shown by experiments that the restored images obtained by the proposed algorithm are better in terms of both numerical measurement and visual quality.<<ETX>>
Generalized approach for the realization of discrete cosine transform using cyclic convolutions

The authors propose a general solution for the realization of the discrete cosine transform (DCT) and the inverse discrete cosine transform (IDCT). This algorithm converts the DCT/IDCT into convolution form such that one can easily implement it with technologies that are well suited for doing convolutions. This is an efficient and effective approach as it can avoid complicated data routing and data management. For example, there is no address generation problem and a simple pipeline structure can be applied to achieve parallel processing. Compared with conventional algorithms, this algorithm is much more flexible as it can be applied to realize DCT/IDCT with any length. This algorithm also suggests an efficient approach for realizing a unified hardware for the implementation of both the DCT and the IDCT. The realization for both the DCT and the IDCT can rely on the same convolution module.<<ETX>>
Color quantization of compressed video sequences

This paper presents a novel color quantization algorithm for compressed video data. The proposed algorithm extracts discrete cosine (DC) transform coefficients and motion vectors of blocks in a shot to estimate a cumulative color histogram of the shot and, based on the estimated histogram, design a color palette for displaying the video sequence in the shot. It significantly reduces the complexity of the generation of a palette by effectively reducing the number of training vectors used in training a palette without sacrificing the quality. The palette obtained can provide a good display quality even if zooming and panning exist in a shot. The experimental results show that the proposed method can achieve a significant signal-to-noise ratio improvement as compared with conventional video color-quantization schemes when zooming and panning are encountered in a shot.
MPEG-compatible motion compensation using background/foreground information

This paper presents a new approach to block-based motion vector estimation, called the background/foreground motion search (B/FS) algorithm. This algorithm separates the background and foreground content inside a block to determine the motion vector of the block. As a result, the motion-compensated frames can be subjectively close to the original image. Experimental results show that our approach provides a significant improvement in the subjective quality of motion-compensated frames as compared with traditional MSE/MAD-oriented block-based methods.
A Technique for Producing Scalable Color-Quantized Images With Error Diffusion

To reliably and efficiently deliver media information to diverse clients over heterogeneous networks, the media involved must be scalable. In this paper, a color quantization algorithm for generating scalable color-indexed images is proposed based on a multiscale error diffusion framework. Images of lower resolutions are embedded in the outputs such that a simple down-sampling process can extract images of any desirable resolutions. Images possessing this scalable property support transmission over the Internet which contains clients with different display resolutions, systems with different caching resources and networks with varying bandwidths and QoS capabilities. Unlike most of the color halftoning algorithms available nowadays, the proposed algorithm is not dedicated for printing applications but for color-indexed displays. It works with any arbitrary palettes of different size
Fast radix-3/6 algorithms for the realization of the discrete cosine transform

Two fast discrete cosine transform (DCT) algorithms, the radix-3 and radix-6 algorithms, are proposed. They are less computationally complex than the radix-2 algorithm. They also provide a wider choice of the sequence length for which the DCT can be realized and support the prime-factor-decomposed computation algorithm for realizing the 2/sup m/3/sup n/-point DCT. A mixed-radix algorithm that achieves an optimal performance by applying the radix-3, radix-6, and radix-2 decomposition techniques in a proper sequence is proposed. This mixed-radix algorithm possesses the advantages mentioned above. Compared with the prime-factor-decomposed algorithm, it requires less computational effort and avoids complicated data routing and mapping procedures.<<ETX>>
A dot-overlap compensation scheme for Feature-preserving Multiscale Error Diffusion

Feature-preserving Multiscale Error Diffusion (FMED) algorithm is a digital halftoning method to convert a gray scale image into a binary image. This technique is superior to conventional MED methods in a way that it can preserve more image features and provide an output halftone image of good blue noise characteristics. In practice, a printer is not able to produce square ink dots and hence dots may overlap with each other and cover the gird area of neighboring pixels. This dot-overlap effect darkens an image when it is printed. In this paper, a dot compensation scheme is proposed to work with FMED. It compensates for the dot-overlap effect introduced by a printer. Features can be preserved and there is no biasing in the emulated gray scale of the printed image.
Improving MPEG-4 coding performance by jointly optimizing both compression and blocking effect elimination

In most current image/video coding systems, the compression stage and the restoration stage operate separately and hence they cannot make use of each other to optimize the overall coding performance. In this research, we suggest modifying the basic structure of the encoding systems such that the contribution of the restoration to be performed can be taken into account in the compression by jointly optimizing the two processes. An example is also provided to show how this idea works successfully when MPEG-4 deblocking filter is exploited in the post-processing stage.
POCS-based algorithm for restoring colour-quantised images

The paper studies the restoration of colour-quantised images. Restoration of colour-quantised images is rarely addressed in the literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. The authors propose a POCS-based restoration algorithm specific to colour-quantised images, which makes a good use of the available colour palette to derive useful a priori information for restoration. The proposed restoration algorithm is shown to be capable of improving the quality of a colour-quantised image to a certain extent.
A High Performance Lossless Bayer Image Compression Scheme

Demosaicing and compression are generally performed sequentially in most digital cameras. Recent reports show that the compression-first scheme outperforms the conventional demosaicing-first scheme in terms of image quality and complexity. In this paper, an efficient lossless compression scheme for Bayer images is presented. It exploits a context matching technique to rank the neighboring pixels for predicting a pixel. Besides, an adaptive color difference estimation scheme is also proposed to remove the spectral redundancy. Simulation results show that the proposed algorithm can achieve a better compression performance as compared with the existing lossless CFA image coding methods.
Optimal choice of local regularization weights in iterative image restoration

In the study of space-variant regularization for image restoration, little effort has been devoted to the search of optimal local regularization weights. In this paper, we address how to derive the optimal local regularization weights in the context of iterative image restoration. The optimal relationship between the two weight matrices for local regularization is derived, and, based on that relationship, a proper choice of the weight matrices is then presented. The results we derived provide a mathematical backup of the viability of some heuristic solutions suggested in the literature.
A POCS-based restoration algorithm for restoring halftoned color-quantized images

This paper studies the restoration of images which are color-quantized with error diffusion. Though there are many reported algorithms proposed for restoring noisy blurred color images and inverse halftoning, restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. In this paper, a restoration algorithm based on projection onto convex sets is proposed. This algorithm makes use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results showed that it could improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric.
Restoration of halftoned color-quantized images using projection onto convex sets

Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. We propose a POCS-based restoration algorithm to solve the problem. This algorithm derives useful information for restoration with the available color palette and the mechanism of a halftoning process. It is shown to be able to provide a good restoration result.
Face tracking using Rao-Blackwellized particle filter and pose-dependent probabilistic PCA

This paper deals with tracking of face blobs containing pose changes. We propose a novel tracking method to deal with face pose changes during the tracking. In the method, tracking is formulated as an approximate solution to the MAP estimate of the state vector, consisting of a linear and a nonlinear part. Multi-pose face appearances are described by local linear models, each being related to a single pose and estimated by probabilistic PCA (PPCA). A Markov model with pose indices as its states is used to model the transitions between poses. Shape and locations of face blobs and the associated pose indices are assumed to be nonlinear, and are estimated by a Rao-Blackwellized particle filter (RBPF). This enables a separate estimation of the linear state vector through marginalizing the joint probability. The proposed method has been tested for videos containing frequent face pose changes and large illumination variations, where 5 poses (left, frontal, right, up, down) were modeled. The tracking results are shown to be robust to variable speed of pose changes and with relatively tight boxes.
Learning full-range affinity for diffusion-based saliency detection

In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperforms 7 recent state-of-the-art saliency models on 3 benchmark datasets.
Video-based tracking and quantified assessment of spontaneous limb movements in neonates

Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection and quantification of these movements in infants from videos are hence desirable for providing useful information to clinicians. This could lead to computer-aided diagnosis of dysfunctions where early treatment may improve infant development. In this paper, we propose a scheme for detecting and quantifying qualitative aspects of limb movement through multiple tracking and state space motion modeling on videos. The main novelties of the paper include: (a) An enhanced detection method for effectively detection small weak marker points from video; (b) Bayesian estimation and nearest neighbor searching for selecting new observation in individual tracker and for tracking marker trajectories on limbs; (c) A criterion for anomaly detection based on the frequency and duration of abrupt changes in limb movement, using window averaged prominent residual powers. The proposed method has been tested on videos of neonates, results show that the proposed method is promising for tracking and quantifying the movement of neonate limbs for helping medical diagnostics.
Voltage dip detection and power system transients

Recently developed power quality mitigation equipment, like the static transfer switch, needs methods for fast and reliable detection of voltage dips. Such a detection scheme must be able to detect a voltage dip as fast as possible and be immune to other types of disturbances. In this paper, the authors address the problem of voltage dip detection regarding Kalman filtering, the characteristics of fault-induced voltage dips and other power system disturbances. They investigate how the voltage dip characteristics influence the speed of detection and show that disturbances other than fault-induced dips could trigger a detection scheme. Special attention is given to trans former-related events. Their characteristics are presented using measurements. Kalman filtering modelling issues are discussed. Statistics on the characteristics of fault-induced dips and transformer events are presented from medium voltage networks.
ML Nonlinear Smoothing for Image Segmentation and its Relationship to the Mean Shift

This paper addresses the issues of nonlinear edge-preserving image smoothing and segmentation. A ML-based approach is proposed which uses an iterative algorithm to solve the problem. First, assumptions about segments are made by describing the joint probability distribution of pixel positions and colours within segments. Based on these assumptions, an optimal smoothing algorithm is derived under the ML condition. By studying the derived algorithm, we show that the solution is related to a two-stage mean shift which is separated in space and range. This novel ML-based approach takes a new kernel function. Experiments have been conducted on a range of images to smooth and segment them. Visual results and evaluations with 2 objective criteria have shown that the proposed method has led to improved results which suffer from less over-segmentation than the standard mean-shift.
Super-Resolution reconstruction of High Dynamic Range images with perceptual weighting of errors

Super-Resolution and High Dynamic Range image reconstruction are two different signal processing techniques that share in common that they utilize information from multiple observations of the same scene to enhance visual image quality. In this paper, both techniques are merged in a common model, and the focus is to solve the reconstruction problem in a suitable image domain, which relates to the perception of the Human Visual System. Simulated results are presented, including a comparison with a conventional method, demonstrating the benefits of the proposed approach, in this case avoiding some severe reconstruction artifacts.
Analysis of Voltage Dips for Event Identification

This paper focuses on a large class of power system events: those where their main characteristic is a temporary decrease in the fundamental frequency voltage (voltage dip) in one or more phases. Voltage dips have attracted a lot of attention due to the problems that they cause to equipment like adjustable speed drives, computers, industrial control systems etc. They are mainly caused by faults and induction motor starting. For the analysis of the different types of voltage dips, recordings are used that were performed at medium and low voltage networks in Sweden (by Goteborg Energi Nat AB-GENAB), Norway (by SINTEF Energy Research) and Scotland (by Scottish Power). Finally, a large set of recordings is classified, explained and presented in terms of the above mentioned types. (4 pages)
Combining Foreground / Background Feature Points and Anisotropic Mean Shift For Enhanced Visual Object Tracking

This paper proposes a novel visual object tracking scheme, exploiting both local point feature correspondences and global object appearance using the anisotropic mean shift tracker. Using a RANSAC cost function incorporating the mean shift motion estimate, motion smoothness and complexity terms, an optimal feature point set for motion estimation is found even when a high proportion of outliers is presented. The tracker dynamically maintains sets of both foreground and background features, the latter providing information on object occlusions. The mean shift motion estimate is further used to guide the inclusion of new point features in the object model. Our experiments on videos containing long term partial occlusions, object intersections and cluttered or close color distributed background have shown more stable and robust tracking performance in comparison to three existing methods.
Joint causal and anti-causal segmentation and location of transitions in power disturbances

An important step in power disturbance data analysis is to first partition the disturbance sequence into transition and event segments. One main contribution of the paper is the propose of a new segmentation scheme that jointly uses the causal and anti-causal segmentation. We show that the actual location of a trigger point can be accurately estimated by combining the causal and anti-causal transition segments. Another main contribution is a new method for setting the threshold of detection parameter. From the residuals of Kalman filter, a detection parameter is defined and the threshold for this parameter is computed based on pdf estimates and hypothesis tests on measurement sequences with/without disturbances. Using the proposed segmentation and the statistically-based threshold, we show that transition segments, especially the trigger points, are allocated more accurately. Tests are performed on semi-synthetic disturbance sequences containing step, ramp and multi-stage transition. The proposed method is shown to be able to locate fast transitions with very high accuracy. The performance of segmentation against the point on wave, depth of transition, speed of transition, and polluted noise level are also evaluated.
3D Face Recognition Using Affine Integral Invariants

A new 3D face representation and recognition approach is presented in this paper. Two sets of facial curves are extracted from a face range image, and a novel facial feature representation, the affine integral invariant, is introduced to mitigate the effect of pose on the facial curves. A human face is shown to be representable by a small subset of those affine integral invariant curves. A recognition procedure based on the discriminant analysis and Jensen-Shannon divergence analysis is proposed. Substantiating examples are provided with an achieved classification accuracy of 92.57%
Multi-View ML Object Tracking With Online Learning on Riemannian Manifolds by Combining Geometric Constraints

This paper addresses issues in object tracking with occlusion scenarios, where multiple uncalibrated cameras with overlapping fields of view are exploited. We propose a novel method where tracking is first done independently in each individual view and then tracking results are mapped from different views to improve the tracking jointly. The proposed tracker uses the assumptions that objects are visible in at least one view and move uprightly on a common planar ground that may induce a homography relation between views. A method for online learning of object appearances on Riemannian manifolds is also introduced. The main novelties of the paper include: 1) define a similarity measure, based on geodesics between a candidate object and a set of mapped references from multiple views on a Riemannian manifold; 2) propose multi-view maximum likelihood estimation of object bounding box parameters, based on Gaussian-distributed geodesics on the manifold; 3) introduce online learning of object appearances on the manifold, taking into account of possible occlusions; 4) utilize projective transformations for objects between views, where parameters are estimated from warped vertical axis by combining planar homography, epipolar geometry, and vertical vanishing point; 5) embed single-view trackers in a three-layer multi-view tracking scheme. Experiments have been conducted on videos from multiple uncalibrated cameras, where objects contain long-term partial/full occlusions, or frequent intersections. Comparisons have been made with three existing methods, where the performance is evaluated both qualitatively and quantitatively. Results have shown the effectiveness of the proposed method in terms of robustness against tracking drift caused by occlusions.
Graph Construction for Salient Object Detection in Videos

Recently many graph-based salient region/object detection methods have been developed. They are rather effective for still images. However, little attention has been paid to salient region detection in videos. This paper addresses salient region detection in videos. A unified approach towards graph construction for salient object detection in videos is proposed. The proposed method combines static appearance and motion cues to construct graph, enabling a direct extension of original graph-based salient region detection to video processing. To maintain coherence in both intra- and inter-frames, a spatial-temporal smoothing operation is proposed on a structured graph derived from consecutive frames. The effectiveness of the proposed method is tested and validated using seven videos from two video datasets.
Moving Object Tracking from Videos Based on Enhanced Space-Time-Range Mean Shift and Motion Consistency

Video surveillance and object tracking have drawn increased interests in recent years. This paper addresses the problem of moving object tracking from image sequences captured from stationary cameras. Based on the previous work on video segmentation using joint space-time-range mean shift, we extend the scheme to enable the tracking of moving objects. Large displacements of pdf modes in consecutive image frames are exploited for tracking. We also improve the above mean shift-based video segmentation by introducing edge-guided merging of over-segmented regions. This can be viewed as an extension of the enhanced mean shift 2D image segmentation in  to the enhanced space-time-range mean shift video segmentation. Experiments have been conducted on several indoor and outdoor videos. Our preliminary results and performance evaluation have indicated the effectiveness of the proposed scheme.