nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting clustering and stereo information in label propagation on facial images

In this paper, a method for performing semiautomatic identity label annotation on facial images, obtained from monocular and stereoscopic videos is introduced. The proposed method exploits prior information for the data structure, obtained from the application of a clustering algorithm, for the selection of the facial images from which label inference should begin. Then, a sparse graph is constructed according to the Linear Neighborhood Propagation (LNP) method and, finally, label inference is performed according to an iterative update rule. In the case of stereoscopic videos, the classification decision is determined by the combined information of the left and right channels. The objective of the proposed framework is to be used by archivists for semi-automatic annotation of television content, in order to further enable journalists to directly access video shots/frames of interest.
Semantic description in stereo video content for surveillance applications

The use of a stereo camera in surveillance applications adds information about the in depth position of the object being monitored. Thus, stereo videos can help extract semantic information about a person or object movement direction, not only on the image plane, but also in depth space. This paper describes a method that performs semantic labeling of movement direction in stereo videos along the horizontal and vertical axes and also along the depth axis when disparity information is available. A method that that extracts information about whether two or more objects are approaching or moving away is also presented.
Stereoscopic video description for key-frame extraction in movie summarization

A novel, low-level video frame description method is proposed that is able to compactly capture informative image statistics from luminance, color and stereoscopic disparity video data, both in a global and in various local scales. Thus, scene texture, illumination and geometry properties may succinctly be contained within a single frame feature descriptor, which can subsequently be employed as a building block in any key-frame extraction scheme, e.g., shot frame clustering. The computed key-frames are subsequently used to derive a movie summary in the form of a video skim, which is suitably post-processed to reduce stereoscopic video defects that cause visual fatigue and are a by-product of the summarization.
Copyright protection of images using robust digital signatures

A method for copyright protection of digital images is presented. Copyright protection is achieved by embedding an "invisible" signal, known as digital signature, in the digital image. Signature casting is performed in the spatial domain by slightly modifying the intensity level of randomly selected image pixels. Signature detection is done by comparing the mean intensity value of the marked pixels against that of the not marked pixels. Statistical hypothesis testing is used for this purpose. The signature can be designed in such a way that it is resistant to JPEG compression and lowpass filtering. This is done by minimizing the energy content of the signature signal in higher frequencies. Experiments on real image data verify the effectiveness of the method.
3D facial expression recognition using Zernike moments on depth images

In this paper we propose a new method for 3D facial expression recognition. We make use of the Zernike moments, which are calculated in the depth image of a 3D facial point cloud. Combining, the Zernike moments along with the 3D point clouds and the depth images, we succeed in tackling problems arising in facial expression recognition due to affine transformations of the data, such as translation, rotation and scaling which, in other approaches are considered very harmful in the overall accuracy of a facial expression recognition algorithm. Support vector machines are used in order to classify the previously extracted features. Results are drawn in two publicly available databases for 3D facial expression recognition.
Graph representations using adjacency matrix transforms for clustering

This paper is meant as a proof of concept regarding the application of standard 2D signal representation and feature extraction tools that have wide use in their respective fields to graph related pattern recognition tasks such as, in this case, clustering. By viewing the adjacency matrix of a graph as a 2-dimensional signal, we can apply 2D Discrete Cosine Transform (DCT) to it and use the relation between the adjacency matrix and the values of the DCT bases in order to cluster nodes into strongly connected components. By viewing the adjacency matrices of multiple graphs as feature vectors, we can apply Principal Components Analysis (PCA) to decorrelate them and achieve better clustering performance. Experimental results on synthetic data indicate that there is potential in the use of such techniques to graph analysis.
Temporal Video Segmentation by Graph Partitioning

A novel temporal video segmentation method that, in addition to abrupt cuts, can detect with very high accuracy gradual transitions such as dissolves, fades and wipes is proposed. The method relies on evaluating mutual information between multiple pairs of frames within a certain temporal frame window. This way we create a graph where the frames are nodes and the measures of similarity correspond to the weights of the edges. By finding and disconnecting the weak connections between nodes we separate the graph to subgraphs ideally corresponding to the shots. Experiments on TRECVID2004 video test set containing different types of shot transitions and significant object and camera motion inside the shots prove that the method is very efficient
3D head pose estimation using support vector machines and physics-based deformable surfaces

This paper presents a novel approach for estimating 3D head pose in single-view video sequences. Following initialization by a face detector, a tracking technique that utilizes a 3D deformable surface model to approximate the image intensity is used to track the face in the video sequence. Head pose estimation is performed by using a feature vector which is a by-product of the equations that govern the deformation of the surface model used in the tracking. The afore-mentioned vector is used for training support vector machines (SVM) in order to estimate the 3D head pose. The proposed method was applied to IDIAP head pose estimation database. The obtained results show that the proposed method can achieve an accuracy of 82% if angles are estimated in 10deg increments and 75% if angle are estimated in 5deg increments.
Automatic Detection of 3D Quality Defects in Stereoscopic Videos Using Binocular Disparity

The 3D video quality issues that may disturb the human visual system and negatively impact the 3D viewing experience are well known and become more relevant as the availability of 3D video content increases, primarily through 3D cinema, but also through 3D television. In this paper, we propose four algorithms that exploit available stereo disparity information, in order to detect disturbing stereoscopic effects, namely, stereoscopic window violations, bent window effects, uncomfortable fusion object objects, and depth jump cuts on stereo videos. After detecting such issues, the proposed algorithms characterize them, based on the stress they cause to the viewer’s visual system. Qualitative representative examples, quantitative experimental results on a custom-made video data set, a parameter sensitivity study, and comments on the computational complexity of the algorithms are provided, in order to assess the accuracy and the performance of stereoscopic quality defect detection.
Virtual Dental Patient: a System for Virtual Teeth Drilling

This paper introduces, a virtual teeth drilling system named virtual dental patient designed to aid dentists in getting acquainted with the teeth anatomy, the handling of drilling instruments and the challenges associated with the drilling procedure. The basic aim of the system is to be used for the training of dental students. The application features a 3D model of the face and the oral cavity that can be adapted to the characteristics of a specific person and animated. Drilling using a haptic device is performed on realistic teeth models (constructed from real data), within the oral cavity. Results and intermediate steps of the drilling procedure can be saved for future use
A Bayesian methodology for visual object tracking on stereo sequences

A general Bayesian post-processing methodology for performance improvement of object tracking in stereo video sequences is proposed in this paper. We utilize the results of any single channel visual object tracker in a Bayesian framework, in order to refine the tracking accuracy in both stereo video channels. In this framework, a variational Bayesian algorithm is employed, where prior knowledge about the object displacement (movement) is incorporated via a prior distribution. This displacement information is obtained in a preprocessing step, where object displacement is estimated via feature extraction and matching. In parallel, disparity information is extracted and utilized in the same framework. The improvements introduced by the proposed methodology in terms of tracking accuracy are quantified through experimental analysis.
An Optimal Detector Structure for the Fourier Descriptors Domain Watermarking of 2D Vector Graphics

Polygonal lines constitute a key graphical primitive in 2D vector graphics data. Thus, the ability to apply a digital watermark to such an entity would enable the watermarking of cartoons, drawings, and geographical information systems (GIS) data in vector graphics format. This paper builds on and extends an existing algorithm that achieves polygonal line watermarking by modifying the Fourier descriptors magnitude in an imperceptible way. Watermarks embedded by this technique can be detected in rotated, translated, scaled, or reflected polygonal lines. The detection of such watermarks had been previously carried out through a correlator detector. In this paper, analysis of the statistics of the Fourier descriptors is exploited to devise an optimal blind detector. Furthermore, the problem of watermarking multiple lines, as well as other implementation issues are being addressed. Experimental results verify the imperceptibility and robustness of the proposed method.
Digital image watermarking: an overview

Usage of digital media has witnessed a tremendous growth during the last decades, as a result of their notable benefits in efficient storage, ease of manipulation and transmission. However these features make digital media vulnerable to copyright infringement, tampering and unauthorized distribution. In the last five years the protection of digital information has received significant attention within the digital media community, and a number of techniques that try to address the problem by hiding appropriate information (e.g. copyright or authentication data) within digital media have been proposed. In this paper we review data hiding techniques for copyright protection of still images and describe some recent research results in this field.
Face-Based Digital Signatures for Video Retrieval

The characterization of a video segment by a digital signature is a fundamental task in video processing. It is necessary for video indexing and retrieval, copyright protection, and other tasks. Semantic video signatures are those that are based on high-level content information rather than on low-level features of the video stream. The major advantage of such signatures is that they are highly invariant to nearly all types of distortion. A major semantic feature of a video is the appearance of specific persons in specific video frames. Because of the great amount of research that has been performed on the subject of face detection and recognition, the extraction of such information is generally tractable, or will be in the near future. We have developed a method that uses the pre-extracted output of face detection and recognition to perform fast semantic query-by-example retrieval of video segments. We also give the results of the experimental evaluation of our method on a database of real video. One advantage of our approach is that the evaluation of similarity is convolution-based, and is thus resistant to perturbations in the signature and independent of the exact boundaries of the query segment.
3D head pose estimation in monocular video sequences by sequential camera self-calibration

This paper presents a novel approach for estimating 3D head pose in single-view video sequences acquired by an uncalibrated camera. Following the initialization by a face detector, a tracking technique localizes the faces in each frame in the video sequence. Head pose estimation is performed by using a structure from motion and self-calibration technique in a sequential way. The proposed method was applied to the IDIAP database that contains head pose ground truth data. The obtained results demonstrate that the method can estimate the head pose with satisfying accuracy.
Person identity recognition on motion capture data using label propagation

Most activity-based person identity recognition methods operate on video data. Moreover, the vast majority of these methods focus on gait recognition. Obviously, recognition of a subject's identity using only gait imposes limitations to the applicability of the corresponding methods whereas a method capable of recognizing the subject's identity from various activities would be much more widely applicable. In this paper, a new method for activity-based identity recognition operating on motion capture data, that can recognize the subject's identity from a variety of activities is proposed. The method combines an existing approach for feature extraction from motion capture sequences with a label propagation algorithm for classification. The method and its variants (including a novel one, that takes advantage of the fact that, in certain cases, both activity and person identity labels might exist for the labeled sequences) have been tested in two different datasets. Experimental analysis proves that the proposed approach provides very good person identity recognition results, surpassing those obtained by two other methods.
Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant

In this paper, a novel class of support vector machines (SVM) is introduced to deal with facial expression recognition. The proposed classifier incorporates statistic information about the classes under examination into the classical SVM. The developed system performs facial expression recognition in facial videos. The grid tracking and deformation algorithm used tracks the Candide grid over time as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of Candide nodes is used as an input to the bank of novel SVM classifiers, that are utilized to recognize the six basic facial expressions. The experiments on the Cohn-Kanade database show a recognition accuracy of 98.2%.
Frontal view recognition in multiview video sequences

In this paper, a novel method is proposed as a solution to the problem of frontal view recognition from multiview image sequences. Our aim is to correctly identify the view that corresponds to the camera placed in front of a person, or the camera whose view is closer to a frontal one. By doing so, frontal face images of the person can be acquired, in order to be used in face or facial expression recognition techniques that require frontal faces to achieve a satisfactory result. The proposed method firstly employs the Discriminant Non-Negative Matrix Factorization (DNMF) algorithm on the input images acquired from every camera. The output of the algorithm is then used as an input to a Support Vector Machines (SVMs) system that classifies the head poses acquired from the cameras to two classes that correspond to the frontal or non frontal pose. Experiments conducted on the IDIAP database demonstrate that the proposed method achieves an accuracy of 98.6% in frontal view recognition.
Still Image and Video Fingerprinting

Multimedia fingerprinting, also know as robust/perceptual hashing and replica detection is an emerging technology that can be used as an alternative to watermarking for the efficient Digital Rights Management (DRM) of multimedia data. Two fingerprinting approaches are reviewed in this paper. The first is an image fingerprinting technique that makes use of color and texture  descriptors,R-trees and Linear Discriminant Analysis (LDA). The second is a two-step, coarse-to-fine video fingerprinting method that involves color-based descriptors, R-trees and a frame-based voting procedure. Experimental performance evaluation is provided for both methods.
Digital image processing techniques for the detection and removal of cracks in digitized paintings

An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterward, the thin dark brush strokes which have been misidentified as cracks are removed using either a median radial basis function neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks.
Nonlinear processing and analysis of angular signals

Physical quantities referring to angles, like vector direction, color hue, etc., exhibit an inherently periodic nature. Due to this periodicity, digital filters and edge operators proposed for data on the line cannot be applied on such data. We introduce filters for angular signals (circular mean, circular median, circular a-trimmed mean, circular modified trimmed mean). Particular emphasis is given to the circular median filter, for which some interesting properties are derived. We also use estimators of circular dispersion to introduce edge detectors for angular signals. Three variations for the extension of quasirange to circular data are proposed, and expressions for their output PDF are derived. These "circular" quasiranges have good and user-controlled properties as edge detectors in noisy angular signals. The performance of the proposed edge operators is evaluated on angular edges, using certain quantitative criteria. Finally, a series of experiments featuring one-dimensional (1-D) angular signals and hue images is used to illustrate the operation of the new filters and edge detectors.
Image and video fingerprinting for digital rights management of multimedia data

Multimedia fingerprinting, also know as robust/perceptual hashing and replica detection is an emerging technology that can be used as an alternative to watermarking for the efficient digital rights management (DRM) of multimedia data. Two fingerprinting approaches are reviewed in this paper. The first is an image fingerprinting technique that makes use of color-based descriptors, R-trees and linear discriminant analysis (LDA). The second is a video fingerprinting method that utilizes information about the appearances of actors in videos along with an efficient search strategy. Experimental performance analysis is provided for both methods
Multi-view object and human body part detection utilizing 3D scene information

The aim of this paper is to present a new method for multiview object or human body (or body part) detection. The basic idea consists of using a single view detector in every view of a scene captured by multiple cameras and then combining the results using the 3D information of the scene. The method can improve the results of the single view detector, while also localizing the object/human in the 3D space. This results in a robust way for rejecting the false detections, amending the missed detections and associating the results of the single view detector across views.
Video Indexing by Face Occurrence-Based Signatures

The extraction of a digital signature from a video segment in order to uniquely identify it, is often a necessary prerequisite for video indexing, copyright protection and other tasks. Semantic video signatures are those that are based on high-level content information rather than on low-level features of the video stream, their major advantage being that they are invariant to nearly all types of distortion. Since a major semantic feature of a video is the appearance of specific people in specific frames, we have developed a method that uses the pre-extracted output of face detection and recognition to perform fast semantic indexing and retrieval of video segments. We give the results of the experimental evaluation of our method on an artificial database created using a probabilistic model of the creation of video
A mutual information approach to articulated object tracking

A mutual information based articulated object tracking scheme is proposed in this paper. Articulation constraints are introduced using a kinematic model. Further constraints are introduced based on the human joint anatomy and flexibility. The tracking scheme is enhanced by using the tracked object texture map image. The tracking history is incorporated in the tracking scheme by using a temporal model or a Kalman filtering scheme. The Kalman filtering scheme greatly enhances the tracking scheme provided the suitable initial conditions are set. The resulting system was tested on arm and finger tracking cases using real image sequences.
Automatic detection of depth jump cuts and bent window effects in stereoscopic videos

3DTV and 3D cinema witness a significant increase in their popularity nowadays. New movie titles are released in 3D and there are more than 35 TV channels in various countries that broadcast in 3D worldwide. It is well known today and becomes more obvious, as the 3D video content availability increases, that stereoscopy is associated with certain 3D video quality issues that may affect in a negative way the 3D viewing experience. In this paper, we propose two novel algorithms that exploit available disparity information to detect two disturbing stereoscopic issues, namely depth jump cuts and bent window effects. Representative examples are provided to assess the algorithms performance. The proposed algorithms can be helpful in the post-production stage, where, in most cases, the detected issues can be fixed, and also in assessing the overall quality of stereoscopic video content.
Edge detection operators for angular data

Physical quantities referring to angles, like vector direction, color hue etc., are periodic in nature. Due to this periodicity edge detectors proposed for data on the line cannot be used to detect edges in angle-related signals. In this paper we use estimators of circular dispersion to introduce edge detectors for angular signals and discuss their application in edge detection on hue images. Extensions of the notion of quasi-range to circular data are also proposed. These "circular" quasi-ranges have good and user-controlled properties as edge detectors on noisy angular signals. The performance of the proposed edge operators is evaluated on angular edges, using both qualitative and quantitative criteria.
Using Deformable Surface Models to Derive a DCT-Like 2D Transform

This paper introduces a 2D discrete, non-separable transform for image processing, which can be regarded as a combination of the well known discrete cosine transform (DCT) with an analytically derived quantization table that includes a compression ratio selection parameter. A 3D deformable surface model is used to approximate the image intensity and the introduced discrete transform is an intermediate step of the explicit surface deformation governing equations. The proposed transform is applied to lossy image compression and the obtained results are compared to those of a DCT-based compression scheme.