nils-herrmann/clean_scirepeval huggingface dataset:
Sparse approximation with adaptive dictionary for image prediction

The paper presents a dictionary construction method for spatial texture prediction based on sparse approximations. Sparse approximations have been recently considered for image prediction using static dictionaries such as a DCT or DFT dictionary. These approaches rely on the assumption that the texture is periodic, hence the use of a static dictionary formed by pre-defined waveforms. However, in real images, there are more complex and non-periodic textures. The main idea underlying the proposed spatial prediction technique is instead to consider a locally adaptive dictionary, A, formed by atoms derived from texture patches present in a causal neighborhood of the block to be predicted. The sparse spatial prediction method is assessed against the sparse prediction method based on a static DCT dictionary. The spatial prediction method is then assessed in a complete image coding scheme where the prediction residue is encoded using a coding approach similar to JPEG.
Embedded multiple description coding for progressive image transmission over unreliable channels

A multiple description scalar quantization (MDSQ) based coding system can be regarded as a source coder (quantizer) followed by a channel coder, i.e. the combination of index and codeword assignment. The redundancy, or the correlation between the descriptions, is controlled by the number of diagonals covered by the index assignment. We consider here the usage of multiple description uniform scalar quantization (that we call MDUSQ) for robust and progressive transmission of images over unreliable channels. The progressive feature is an important factor for rate control in non-stationary (varying bandwidth) communication environments. In this context, the paper describes an embedded index assignment strategy that provides improved rate-distortion performances in progressive transmission scenarios, against index assignments defined so far for MDSQ. The MDUSQ together with the embedded index assignment algorithm are incorporated into the JPEG2000 verification model. The approach is compared against a progressive multiple description scheme based on a polyphase transform (PT) decomposition of the signal.
Superrays for Efficient Light Field Processing

Light field acquisition devices allow capturing scenes with unmatched postprocessing possibilities. However, the huge amount of high-dimensional data poses challenging problems to light field processing in interactive time. In order to enable light field processing with a tractable complexity, in this paper, we address the problem of light field oversegmentation. We introduce the concept of superray, which is a grouping of rays within and across views, as a key component of a light field processing pipeline. The proposed approach is simple, fast, accurate, easily parallelisable, and does not need a dense depth estimation. We demonstrate experimentally the efficiency of the proposed approach on real and synthetic datasets, for sparsely and densely sampled light fields. As superrays capture a coarse scene geometry information, we also present how they can be used for real-time light field segmentation and correcting refocusing angular aliasing.
Inter-view motion prediction in 3D-HEVC

This paper presents a novel inter-view motion prediction technique used in 3D-HEVC which provides efficient compression for motion vectors. The 3D extension of HEVC (High Efficiency Video Coding) namely 3D-HEVC is under development in JCT-3V for coding multi-view video and depth data. Inter-view motion prediction takes benefit of the inter-view correlation between views by inferring the motion information of a view from the already coded motion information in another view as well as the local disparity between two views. Experimental results show that inter-view motion prediction in 3D-HEVC provides an average bit-rate savings of 15% for the dependent views.
M-channel nonrectangular wavelet representation for 2-D signals: basis for quincunx sampled signals

The authors have described the framework underlying continuous and discrete families of nonseparable two-dimensional wavelets and an M-channel nonrectangular multiresolution wavelet representation for L/sup 2/(R/sup 2/) functions and I/sup 2/(Z/sup 2/) sequences. Focusing on the case of quincunx sampled signals, solutions of digital filter banks for implementing the decomposition with different characteristics of linear phase and regularity for smoothing and wavelet functions are provided.<<ETX>>
Super Resolution of Light Field Images Using Linear Subspace Projection of Patch-Volumes

Light field imaging has emerged as a very promising technology in the field of computational photography. Cameras are becoming commercially available for capturing real-world light fields. However, capturing high spatial resolution light fields remains technologically challenging, and the images rendered from real light fields have today a significantly lower spatial resolution compared to traditional two-dimensional (2-D) cameras. This paper describes an example-based super-resolution algorithm for light fields, which allows the increase of the spatial resolution of the different views in a consistent manner across all subaperture images of the light field. The algorithm learns linear projections between subspaces of reduced dimension in which reside patch-volumes extracted from the light field. The method is extended to cope with angular super-resolution, where 2-D patches of intermediate subaperture images are approximated from neighboring subaperture images using multivariate ridge regression. Experimental results show significant quality improvement when compared to state-of-the-art single-image super-resolution methods applied on each view separately, as well as when compared to a recent light field super-resolution techniques based on deep learning.
Geometry-Aware Neighborhood Search for Learning Local Models for Image Superresolution

Local learning of sparse image models has proved to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are often clustered using the K-means algorithm with the Euclidean distance as a dissimilarity metric. However, the Euclidean distance may not always be a good dissimilarity measure for comparing data samples lying on a manifold. In this paper, we propose two algorithms for determining a local subset of training samples from which a good local model can be computed for reconstructing a given input test sample, where we consider the underlying geometry of the data. The first algorithm, called adaptive geometry-driven nearest neighbor search (AGNN), is an adaptive scheme, which can be seen as an out-of-sample extension of the replicator graph clustering method for local model learning. The second method, called geometry-driven overlapping clusters (GOCs), is a less complex nonadaptive alternative for training subset selection. The proposed AGNN and GOC methods are evaluated in image superresolution and shown to outperform spectral clustering, soft clustering, and geodesic distance-based subset selection in most settings.
Template based inter-layer prediction for high dynamic range scalable compression

This paper presents a scalable high dynamic range (HDR) image coding framework in which the base layer is a low dynamic range (LDR) version of the image that may have been generated by an arbitrary Tone Mapping Operator (TMO). Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template based Inter Layer Prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for block-wise ILP. Our experiments have shown an average bitrate saving of 34% on the HDR enhancement layer, compared to state of the art methods.
Graph-based light fields representation and coding using geometry information

This paper describes a graph-based coding scheme for light fields (LF). It first adapts graph-based representations (GBR) to describe color and geometry information of LF. Graph connections describing scene geometry capture inter-view dependencies. They are used as the support of a weighted Graph Fourier Transform (wGFT) to encode disoccluded pixels. The quality of the LF reconstructed from the graph is enhanced by adding extra color information to the representation for a sub-set of sub-aperture images. Experiments show that the proposed scheme yields rate-distortion gains compared with HEVC based compression (directly compressing the LF as a video sequence by HEVC).
Log-Gabor Filters for Image-Based Vehicle Verification

Vehicle detection based on image analysis has attracted increasing attention in recent years due to its low cost, flexibility, and potential toward collision avoidance. In particular, vehicle verification is especially challenging on account of the heterogeneity of vehicles in color, size, pose, etc. Image-based vehicle verification is usually addressed as a supervised classification problem. Specifically, descriptors using Gabor filters have been reported to show good performance in this task. However, Gabor functions have a number of drawbacks relating to their frequency response. The main contribution of this paper is the proposal and evaluation of a new descriptor based on the alternative family of log-Gabor functions for vehicle verification, as opposed to existing Gabor filter-based descriptors. These filters are theoretically superior to Gabor filters as they can better represent the frequency properties of natural images. As a second contribution, and in contrast to existing approaches, which transfer the standard configuration of filters used for other applications to the vehicle classification task, an in-depth analysis of the required filter configuration by both Gabor and log-Gabor descriptors for this particular application is performed for fair comparison. The extensive experiments conducted in this paper confirm that the proposed log-Gabor descriptor significantly outperforms the standard Gabor filter for image-based vehicle verification.
Detection of anomalies in surveillance scenarios using mixture models

In this paper we present a robust and simple method for the detection of anomalies in surveillance scenarios. We use a “bottom-up” approach that avoids any object tracking, making the system suitable for anomaly detection in crowds. A robust optical flow method is used for the extraction of accurate spatio-temporal motion information, which allows to get simple but discriminative descriptors that are employed to train a Gaussian mixture model. We evaluate our system in a publicly available dataset, concluding that our method outperforms similar anomaly detection approaches but with a simpler model and low-sized descriptors.
On-board robust vehicle detection and tracking using adaptive quality evaluation

This paper presents a robust method for real-time vehicle detection and tracking in dynamic traffic environments. The proposed strategy aims to find a trade-off between the robustness shown by time-uncorrelated detection techniques and the speed-up obtained with tracking algorithms. It combines both advantages by continuously evaluating the quality of the tracking results along time and triggering new detections to restart the tracking process when quality falls behind a certain quality requirement. Robustness is also ensured within the tracking algorithm with an outlier rejection stage and the use of stochastic filtering. Several sequences from real traffic situations have been tested, obtaining highly accurate multiple vehicle detections.
Traffic Sign Detection and Tracking Using Robust 3D Analysis

In this paper we present an innovative technique to tackle the problem of automatic road sign detection and tracking using an on-board stereo camera. It involves a continuous 3D analysis of the road sign during the whole tracking process. Firstly, a color and appearance based model is applied to generate road sign candidates in both stereo images. A sparse disparity map between the left and right images is then created for each candidate by using contour-based and SURF-based matching in the far and short range, respectively. Once the map has been computed, the correspondences are back-projected to generate a cloud of 3D points, and the best-fit plane is computed through RANSAC, ensuring robustness to outliers. Temporal consistency is enforced by means of a Kalman filter, which exploits the intrinsic smoothness of the 3D camera motion in traffic environments. Additionally, the estimation of the plane allows to correct deformations due to perspective, thus easing further sign classification.
Robust road modeling based on a hierarchical bipartite graph

Driver assistance systems based on video processing deliver a number of warnings to the driver, such as lane departure, lane invasion by other vehicles, collision prediction, etc. This have been a field of intense research for many years, providing solutions based on road models where vehicles are afterwards detected and tracked. Robustness is essential in this field of road safety where outliers represent one of the major problems for road modeling. The motivation of this work is to provide a robust and, at the same time, flexible road model which identifies a variable number of lanes, their widths, the curvature of the road and the position of the vehicle in its lane. The major advantage of this model is that the system gives confidence measures for each lane, determining which lanes are actually present and which not. The model is structured as a hierarchical bipartite graph which simplifies information management, reduces sub-module dependencies and classifies elements of the road in different levels. At each level different strategies are applied, following four overall steps: measurement, estimation, evaluation and extrapolation, which lead to enhanced road model accuracy, reliability and flexibility. Several experimental results are provided, showing the robustness of the system, its stability and accurate results for large test paths.
I-WAY, intelligent co-operative system for road safety

Fatalities and injuries connected with road accidents and its social and economic implications are dramatic issues nowadays. In this paper we present a strategy to improve road and mobility safety addressed by the I-WAY system. I-WAY is an innovative system able to improve road safety based on a cooperative driving platform which will ubiquitously monitor and recognize the road environment and the driver's state in real time using data obtained from three types of sources: the in-vehicle sensing system, the road infrastructure and neighbouring cars. It supports both, road-to-vehicle and vehicle-to-vehicle communication in order to enhance drivers' perception on road environment and improve responses in critical scenarios. This work addresses the I-WAY technical solution and its overall architecture.
Motorway surveillance through stereo computer vision

The demand for traffic monitoring systems is growing very quickly. The public transportation authorities have a clear need of these systems to gather as much traffic data as possible so that the proper solutions to congested road and motorways can be adopted. This paper presents a stereo computer vision system for traffic monitoring, developed within the European ESPRIT 8819 VICTORIA project. The system is based on stereo vision technology, which provides clear benefits and advantages with respect to conventional systems based on classical inductive loops or pneumatic tubes. Additionally, and compared to existing monocular vision based sensors, the VICTORIA system has the advantage of providing real world three dimensional information that allow vehicle classification. This is a requirement in some applications such as automatic tolling systems.
Object Tracking from Unstabilized Platforms by Particle Filtering with Embedded Camera Ego Motion

Visual tracking with moving cameras is a challenging task. The global motion induced by the moving camera moves the target object outside the expected search area, according to the object dynamics. The typical approach is to use a registration algorithm to compensate the camera motion. However, in situations involving several moving objects, and backgrounds highly affected by the aperture problem, image registration quality may be very low, decreasing dramatically the performance of the tracking. In this work, a novel approach is proposed to successfully tackle the tracking with moving cameras in complex situations, which involve several independent moving objects. The key idea is to compute several hypotheses for the camera motion, instead of estimating deterministically only one. These hypotheses are combined with the object dynamics in a Particle Filter framework to predict the most probable object locations. Then, each hypothetical object location is evaluated by the measurement model using a spatiogram, which is a region descriptor based on color and spatial distributions. Experimental results show that the proposed strategy allows to accurately track an object in complex situations affected by strong ego motion.
Low-cost VRML based architecture for adaptive and user customized virtual visits to art galleries

This paper presents an innovative procedure, capable to automatically generate virtual environments which are applicable to art galleries, museums or similar facilities, and suitable for web-based promotions of artists and their works. The presented system is part of the outcome of an interdisciplinary research for low-cost and platform independent solutions to combine art with virtual reality with the aim to promote artist works through new interaction metaphors which include dynamic 3D visualization of works of art with associated structured multimedia information.
A monocular vision system for autonomous vehicle guidance

The improvement of vehicle security is a major priority of the car industry. Both active and passive security-systems have experienced a great development during the last decade, but the main research is still focused on minimising the errors committed by the driver rather than trying to avoid them. In this paper, a basic monocular vision system, focused on road location solely, is described. Working with only one video camera hinders the exact 3D reconstruction of the scene: no information about distances and dimensions is available, unless a priori artificial constraints are taken. The problem increases because of the movement of the camera with respect to the scene. In the present system, the road searching and following algorithms operate on the two-dimensional image plane, and 2D to 3D conversion is not accomplished. The system obtains excellent final results, succeeding in more than 75% of the analysed images from the tested sequences.
Stabilization of Inverse Perspective Mapping Images based on Robust Vanishing Point Estimation

In this work, a new inverse perspective mapping (IPM) technique is proposed based on a robust estimation of the vanishing point, which provide bird-view images of the road, so that facilitating the tasks of road modeling and vehicle detection and tracking. This new approach has been design to cope with the instability that cameras mounted on a moving vehicle suffer. The estimation of the vanishing point relies on a novel and efficient feature extraction strategy, which segmentates the lane markings of the images by combining a histogram-based segmentation with temporal and frequency filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. In a last step, the IPM image is computed based on the stabilized vanishing point. Tests have been carried out on several long video sequences captured from cameras inside a vehicle being driven along highways and local roads, with different illumination and weather conditions, presence of shadows, occluding vehicles, and slope changes. Results have shown a significant improvement in terms of lane width constancy and parallelism between lane markings over non-stabilized IPM algorithms.
Video Synchronization Based on Displacements of Center of Motion

Video synchronization is one of the ﬁrst steps inmost of the multi-camera systems. In this paper we introducea novel, computationally simple and reliable approach for videosynchronization that does not require any pre-computed camerageometries or tracking certain features. We deﬁne a featurecalled Center of Motion (COM) and obtain its trajectories alongthe temporal axis for both videos. Then the temporal shiftis estimated by correlating the trajectories. We follow a sub-sequence based approach to make our algorithm reliable andassign a conﬁdence measure to the estimated temporal shift.Whenever our algorithm fails to estimate the temporal shiftreliably it indicates its inability rather than giving a wrongoutput. Experimental results demonstrate promising capabilityof our approach.  A multi-camera system that captures the same scenefrom different viewpoints is commonly used for variousapplications such as surveillance, multiple view synthesisfor object recognition, dynamic scene reconstruction, etc.For such systems it is always important to represent allthe videos on a single time scale. If all the cameras areconnected to the same external clock signal then they will besynchronized automatically. But when cameras do not have acommon clock signal they have to be synchronized using thevisual information present in the videos. And for videos thathave been already captured synchronizing based on visualinformation is the only option.Video synchronization algorithms can be divided into twocategories: intensity based and feature based. Feature basedmethods use tracking and matching of speciﬁc features likepoints on moving objects, object trajectories, etc. whereasintensity based methods[1] use all the pixels and avoid featuretracking and matching. In  Caspi and Irani proposed amethod based on matching the trajectories of objects. But thismethod uses geometric correspondences for synchronizationand hence a sufﬁcient number of correspondences acrossimages are necessary, which may not be possible always.  Wedge
Efficient prediction error regions determination for region-based video coding through shape adaptive DCT

An efficient strategy to determine the prediction error regions to be coded within a region-based prediction error coding scheme is presented. Prediction error coding is based on the segmentation of the displaced field difference (DFD) and coding the resulting arbitrary shaped DFD regions using shape adaptive DCT. Efficiency in the determination of the DFD regions to be coded is achieved by eliminating from the selection process the direct computation of the cost of region contours and textures coding. With this scheme, perceptual distortion of the decoded images is reduced while quality is locally improved on relevant image areas. Comparative results with the complete H.263 coder are shown.
Fast Mode Decision on H.264/AVC Main Profile Encoding Based on PSNR Predictions

In this paper we propose a new fast mode decision (FMD) algorithm to reduce the computational load of the motion estimation (ME) process of the new video coding standard H.264/AVC main profile. The algorithm firstly computes the skip/direct mode (respectively for P and B-frames), for all macroblocks (MBs), and then decides, for each MB if no mores modes are needed. This decision is based on the generation of predictions of the expected PSNR of the frame to be encoded. These predictions are performed with the distortion cost obtained for skip/direct mode jointly with the average distortion cost obtained for the previous encoded frames. Results show that the computational load have been dramatically reduced, with average encoding time saving of-36.38%, while nearly negligible loss of coding efficiency.