nils-herrmann/clean_scirepeval huggingface dataset:
Texture preserving super resolution in thermal images

This paper proposes a new super-resolution (SR) image reconstruction method targeting edges and textured regions in thermal images. Proposed two stage method runs two Bayesian SR estimators at the first stage. These estimators are; maximum likelihood method and maximum a-posteriori method with Tikhonov type regularization. The piksel-to-piksel difference image of these two estimates is an high frequency (HF) image including observation noise, process noise, edges and textures (that are smoothed out by regularizers). In the second stage of the proposed method, the difference image is post-processed to extract edge and texture information while eliminating noise. The proposed method uses a Gabor filter family to analyze this difference image at various frequencies and directions. The strong frequency components are restored and added to the MAP estimate to obtain the final image. The proposed methods are validated through simulations on several textured surfaces from Brodatz data base. Peak-signal-to-noise ratio (PSNR) measures and illustrations clearly shows the success of the proposed method. The Real experiments on uncooled thermal cameras are also conducted to compare the methods to classical SR methods known to literature.
Visual Group Binary Signature for Video Copy Detection

Need for automatic video copy detection is increased with the recent technical developments in the internet technologies and video recording. Even though image-based techniques with bag-of-word kind of representations are accepted as the best solution because of robustness and speed, they discard the convenient geometric relation which exists among interest points. In this work, we propose a novel geometric relation which computes a binary signature leveraging existence and non-existence of interest points in the neighborhood area. The experimental results on TRECVID 2009 content-based video copy detection dataset show that combination of our method with recently proposed quantization-based indexing and weak geometric consistency schemes outperforms classical representations.
Transport Methods in 3DTV—A Survey

We present a survey of transport methods for 3-D video ranging from early analog 3DTV systems to most recent digital technologies that show promise in designing 3DTV systems of tomorrow. Potential digital transport architectures for 3DTV include the DVB architecture for broadcast and the Internet Protocol (IP) architecture for wired or wireless streaming. There are different multiview representation/compression methods for delivering the 3-D experience, which provide a tradeoff between compression efficiency, random access to views, and ease of rate adaptation, including the "video-plus-depth" compressed representation and various multiview video coding (MVC) options. Commercial activities using these representations in broadcast and IP streaming have emerged, and successful transport of such data has been reported. Motivated by the growing impact of the Internet protocol based media transport technologies, we focus on the ubiquitous Internet as the network infrastructure of choice for future 3DTV systems. Current research issues in unicast and multicast mode multiview video streaming include network protocols such as DCCP and peer-to-peer protocols, effective congestion control, packet loss protection and concealment, video rate adaptation, and network/service scalability. Examples of end-to-end systems for multiview video streaming have been provided.
Multiple Description Scalar Quantization Based 3D Mesh Coding

In this paper, we address the problem of 3D model transmission over error-prone channels using multiple description coding (MDC). The objective of MDC is to encode a source into multiple bitstreams, called descriptions, supporting multiple quality levels of decoding. Compared to layered coding techniques, each description can be decoded independently to approximate the model. In the proposed approach, the mesh geometry is compressed using multiresolution geometry compression. Then multiple descriptions are obtained by applying multiple description scalar quantization (MDSQ) to the obtained wavelet coefficients. Experimental results show that, the proposed approach achieves competitive compression performance compared with existing multiple description methods.
Enhanced spatio-temporal video copy detection by combining trajectory and spatial consistency

The recent improvements on internet technologies and video coding techniques cause an increase in copyright infringements especially for video. Frequently, image-based approaches appear as an essential solution due to the fact that joint usage of quantization-based indexing and weak geometric consistency stages give a capability to compare duplicate videos quickly. However, exploiting purely spatial content ignores the temporal variation of video. In this work, we propose a system that combines the state-of-the-art quantization-based indexing scheme with a novel trajectory-based geometric consistency on spatio-temporal features. This combination improves duplicate video matching task significantly. Briefly, spatial mean and variance of the trajectories are incorporated to establish a weak geometric consistency among pair of frames. To show the success of the proposed method, content-based video copy detection field is selected and TRECVID 2009 dataset is utilized. The experimental results show that constituting trajectory-based consistency on corresponding feature pairs outperforms the performances of merely utilizing spatiotemporal signature and visual signature with enhanced weak geometric consistency.
Directionally adaptive super-resolution

In this paper a novel direction adaptive super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator uses gradient direction for optimal noise reduction while preserving the edges. Compared to the other edge-preserving methods, the proposed algorithm uses gradient direction in addition to the gradient amplitude for optimum regularization. The method comprises a gradient amplitude and direction estimation stage where a gradient direction map is obtained. This map guides the SR reconstruction stage through iterations. Three variations of the proposed method are compared against other edge-preserving super resolution methods. PSNR (Peak signal-to-noise-ratio), SSIM (Structural similarity index measure) values, and illustrations show that the proposed method has better performance especially on image pixel values where a strong gradient is present.
E-Museum: Web-based Tour and Information System for Museums

A Web-based system-consisting of data entrance, access and retrieval modules-is constructed for museums. Internet users that visit the e-museum, are able to view the written and visual information, belonging to the artworks in the museum, are able to follow the virtual tour prepared for the different sections of the museum, are able to browse the artworks according to certain properties, are able to search the artworks having the similar visual content with the viewed artwork
3D Mesh Coding Using Multiple Description Scalar Quantizers

In this paper, we address the problem of 3D model transmission over error-prone channels using multiple description coding (MDC). The objective of MDC is to encode a source into multiple bitstreams, called descriptions, supporting multiple quality levels of decoding. Compared to layered coding techniques, each description can be decoded independently to approximate the model. In the proposed approach, the mesh geometry is compressed using multiresolution geometry compression. Then multiple descriptions are obtained by applying multiple description scalar quantization (MDSQ) to the obtained wavelet coefficients. Experimental results show that, the proposed approach achieves competitive compression performance compared with existing single and multiple description
Circular target detection algorithm on satellite images based on radial transformation

Remote sensing is used in a spreading manner by many governmental and industrial institutions worldwide in recent years. Target detection has an important place among the applications developed using satellite imagery. In this paper, an original circular target detection algorithm has been proposed based on a radial transformation. The algorithm consists of three stages such as pre-processing, target detection, and postprocessing. In the pre-processing stage, bilateral noise reduction filtering and vegetation detection operations are completed which they are required by target detection step. The target detection stage finds the circular target by a radial transformation algorithm and variables obtained from the training, and postprocessing stage carries out the elimination of falsely detected targets by utilizing the vegetation information. The Petroleum Oil Lubricants (POL) depots in the industrial areas and harbors have been chosen as an application area of the proposed algorithm. The algorithm has been trained and tested on a data set which includes 4-band images with Near-Infrared band. Proposed algorithm is able to detect many circular targets with different types and sizes as a consequence of using a full radial transformation search as well as it gives rewarding results on industrial areas and harbors in the experiments conducted.
Quality evaluation of stereoscopic videos using depth map segmentation

This paper presents a new quality evaluation model for stereoscopic videos using depth map segmentation. This study includes both objective and subjective evaluation. The goal of this study is to understand the effect of different depth levels on the overall 3D quality. Test sequences with different coding schemes are used. The results show that overall quality has a strong correlation with the quality of the background, where disparity is smaller relative to the foreground. The results also showed that content type is an important factor in determining the visual quality. While depth segmentation gives information related to 3D perception, additional work is required to develop an objective metric.
Direction Adaptive Super-Resolution Imaging

In this paper a novel edge-preserving super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator uses gradient direction and amount for optimal noise reduction while preserving the edges. Compared to the other edge-preserving methods, the proposed algorithm uses the gradient direction for optimum regularization. The proposed method estimates gradient amplitude and direction at each iteration. This gradient map guides the SR reconstruction stage through iterations. Proposed method is compared against other traditional super resolution methods. Peak-signal-to-noise-ratio (PSNR) measures and illustrations clearly show that the proposed method is successful especially on edge structures in images.
A multimodal approach for aggressive driving detection

Aggressive driving behavior is among the important causes of traffic accidents. Hence, detection of driver aggressiveness has an importance in terms of decreasing the number of traffic accidents. Collected driving data while the vehicle is in traffic can be used to make inferences about the aggressiveness of the driver. In this study, a multimodal method is proposed in order to detect driver aggressiveness. The proposed method is based on utilizing the visual data taken from the on vehicle camera and sensor data taken from the controller area network bus (CAN-bus) in order to decide whether the driving session involves aggressive driving behavior. Lane following pattern and vehicle following distance information is obtained from the data collected by camera while vehicle speed and engine speed information is obtained from CAN-bus. These information is fused to conceive feature vectors that represent the driving session and aggressiveness decision is made according to the classification of these feature vectors.
A comparative study on no-reference Video Quality Assessment metrics

In the last two decades the Internet technology has boosted and the connection speeds have been incrased from kilobits to hundred megabits scale. With the rising coverage of the Internet and the usage of mobile devices such as tablets and smart phones, the usage of social media and especially multimedia elements has been increased rapidly. This increment in streaming multimedia created a need for the assessment of the user experience on multimedia and especially video. Even though there are different Video Quality Assessment (VQA) methods for that purpose, most of them are Full-Reference (FR) or Reduced-Reference (RR). In today's world with many mobile devices, the application of these methods are not possible since they need the reference data. The No-Reference (NR) video metrics are much more suitable for the case. In this paper, the main objective is to evaluate a previously proposed NR VQA metric with a new dataset and to compare the results to other highperformance NR metrics such as G.1070 and G.1070E which do not utilize spatial and temporal characteristics of a given video sequence. Evaluation and comparison results show the accuracy and robustness of the proposed metric.
Vertex-Based Anisotropic Smoothing of 3D Mesh Data

We propose a vertex-based anisotropic diffusion for 3D mesh denoising by solving a discrete partial differential equation. The core idea behind our proposed technique is to use geometric insight in helping construct an efficient and fast 3D mesh smoothing strategy to fully preserve the geometric structure of the data. Illustrating experimental results demonstrate a much improved performance of the proposed approach in comparison with existing methods currently used in 3D mesh smoothing
Jensen-renyi divergence measure: theoretical and computational perspectives

We analyze the theoretical properties of the Jensen-RBnyi divergence, and we discuss its appli- cation to parametric classification. Numerical results on medical images show the the potential of the pro- posed divergence measure in image registration. I. THEORETICAL AND COMPUTATIONAL ANALYSIS Definition 1 Let p,,p,, . . . ,p, be n probabzlzty dzstrzbutzons of X and w = (w1, w2,. . . ,wn) be a wezght vector such that C:=lwz = 1 and wz 2 0. The Jensen-Rknyi dzvergence zs defined as n JRf(p,, . . . ,pn) =-Re (CWiPi) - xwzRe(Pi)> i=l i=l
NMF-based Watermarking Scheme for Multimedia Protection

We introduce a robust image watermarking scheme for digital multimedia protection using nonnegative matrix factorization and singular value decomposition approaches. The proposed technique improves the performance of the data embedding system effectively, and it is resistant to a variety of international attacks and normal visual processes. Illustrating experimental results show the much improved performance of the proposed watermarking methodology in comparison with existing techniques, and also its robustness against a wide range of attacks
A Nonnegative Matrix Factorization Scheme for Digital Image Watermarking

We present a new scheme for digital watermarking and secure copyright protection of digital images using non-negative matrix factorization and singular value decomposition approaches. The proposed method improves the performance of the data embedding system effectively, and it is resistant to a variety of intentional attacks and normal visual processes. The experimental results clearly illustrate the much improved performance of the proposed watermarking methodology in comparison with existing techniques
Entropic Smoothing of 3D Volumetric Medical Images

We propose an information-theoretic variational model for volumetric medical image smoothing. It is a result of minimizing a functional subject to some noise constraints, and takes a hybrid form of a negative-entropy variational integral for small gradient magnitudes and a total variational integral for large gradient magnitudes. The core idea behind this approach is to use geometric insight in helping construct regularizing functionals and avoiding a subjective choice of a prior in maximum a posteriori estimation. Illustrating experimental results demonstrate a much improved performance of the approach in the presence of noise
Secure Copyright Protection of Digital Images Using Nonnegative Matrix Factorization

We propose a robust method for digital watermarking and secure copyright protection of digital images using nonnegative matrix factorization and singular value decomposition techniques. The proposed scheme improves the performance of the data embedding system effectively, and it is resistant to a variety of intentional attacks and normal visual processes. The experimental results show the much improved performance of the proposed watermarking methodology in comparison with existing techniques, and also its robustness against the most common attacks
A robust block-based image watermarking scheme using fast Hadamard transform and singular value decomposition

We present a new approach for transparent and high rate embedding of watermarks into digital images using fast Hadamard transform (FHT) and singular value decomposition (SVD). The proposed algorithm consists of three main steps: dividing the cover image into small blocks, applying the FHT to each block, and distributing the singular values of the visual watermark image over the transformed cover blocks. The main attractive features of this approach are simplicity, flexibility in data embedding capacity, and real-time implementation. The experimental results show the much improved performance of the proposed method in comparison with existing techniques, and also its robustness against the most common attacks
Software Reliability Growth Modelling using aWeighted Laplace Test Statistic

We introduce a new weighted Laplace test statistic for software reliability growth modelling. The proposed model not only takes into account the activity in the system but also the proportion of reliability growth within the model. This generalized approach is defined as a weighted combination of a growth reliability model and a non-growth reliability model. Experimental results illustrate the effectiveness and the much improved performance of the proposed method in software reliability modelling.
Software Defects Prediction using Operating Characteristic Curves

We present a software defect prediction model using operating characteristic curves. The main idea behind our proposed technique is to use geometric insight in helping construct an efficient and fast prediction method to accurately predict the. cumulative number of failures at any given stage during the software development process. Our predictive approach uses the number of detected faults instead of the software failure-occurrence time in the testing phase. Experimental results illustrate the effectiveness and the much improved performance of the proposed method in comparison with the Bayesian prediction approaches.
Automatic Inpainting Scheme for Video Text Detection and Removal

We present a two stage framework for automatic video text removal to detect and remove embedded video texts and fill-in their remaining regions by appropriate data. In the video text detection stage, text locations in each frame are found via an unsupervised clustering performed on the connected components produced by the stroke width transform (SWT). Since SWT needs an accurate edge map, we develop a novel edge detector which benefits from the geometric features revealed by the bandlet transform. Next, the motion patterns of the text objects of each frame are analyzed to localize video texts. The detected video text regions are removed, then the video is restored by an inpainting scheme. The proposed video inpainting approach applies spatio-temporal geometric flows extracted by bandlets to reconstruct the missing data. A 3D volume regularization algorithm, which takes advantage of bandlet bases in exploiting the anisotropic regularities, is introduced to carry out the inpainting task. The method does not need extra processes to satisfy visual consistency. The experimental results demonstrate the effectiveness of both our proposed video text detection approach and the video completion technique, and consequently the entire automatic video text removal and restoration process.
Entropic hashing of 3D objects using Laplace-Beltrami operator

In this paper, we present a hashing technique for 3D models using spectral graph theory and entropic spanning trees. The main idea is to partition a 3D triangle mesh into an ensemble of sub- meshes, then apply eigen-decomposition to the Laplace-Beltrami matrix of each sub-mesh, followed by computing the hash value of each sub-mesh. This hash value is defined in terms of spectral coefficients and Tsallis entropy estimate. The experimental results on a variety of 3D models demonstrate the effectiveness of the proposed technique in terms of robustness against the most common attacks including Gaussian noise, mesh smoothing, mesh compression, scaling, rotation as well as combinations of these attacks.
An Imagewatermarking Scheme based on Wavelet and Multiple-Parameter Fractional Fourier Transforms

We propose an improved digital image watermarking technique using multiple parameter discrete fractional Fourier (MPDFRF) and discrete wavelet (DW) transforms. The original cover image is decomposed into four wavelet sub-bands using the DW transform, followed by a segmentation of each sub-band into blocks. Then the MPDFRF transform is applied to each block, and the watermark is embedded into each transformed block. Several experiments are conducted to compare the robustness of the proposed method with the discrete fractional Fourier (DFRF) transform. The experimental results demonstrate better visual imperceptibility and an improved resiliency against several attacks.
Topological modeling of illuminated surfaces using Reeb graph

We present a feature-based object representation for topological modeling of three-dimensional illuminated surfaces. The proposed approach encodes an object into the Reeb graph concept from computational topology. This skeletal structure is based on the generalized height function in the light direction defined on the illuminated surface. In this paper, the topological properties of the proposed representation are analyzed in the Morse theoretic framework, and its close relationship to the shading problem is also highlighted. Some numerical simulations with synthetic and real 3D data are provided to demonstrate the potential of object singularities in topological modeling.
Geodesic matching of triangulated surfaces

Recognition of images and shapes has long been the central theme of computer vision. Its importance is increasing rapidly in the field of computer graphics and multimedia communication because it is difficult to process information efficiently without its recognition. In this paper, we propose a new approach for object matching based on a global geodesic measure. The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures the global geodesic distance between two arbitrary points on the surface of an object. In contrast to the Euclidean distance which is more suitable for linear spaces, the geodesic distance has the advantage to be able to capture the intrinsic geometric structure of the data. The matching task therefore becomes a one-dimensional comparison problem between probability distributions which is clearly much simpler than comparing three-dimensional structures. Object matching can then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions, and is additionally computationally efficient and inexpensive