nils-herrmann/clean_scirepeval huggingface dataset:
Watermarked Movie Soundtrack Finds the Position of the Camcorder in a Theater

In recent years, the problem of camcorder piracy in theaters has become more serious due to technical advances in camcorders. In this paper, as a new deterrent to camcorder piracy, we propose a system for estimating the recording position from which a camcorder recording is made. The system is based on spread-spectrum audio watermarking for the multichannel movie soundtrack. It utilizes a stochastic model of the detection strength, which is calculated in the watermark detection process. Our experimental results show that the system estimates recording positions in an actual theater with a mean estimation error of 0.44 m. The results of our MUSHRA subjective listening tests show the method does not significantly spoil the subjective acoustic quality of the soundtrack. These results indicate that the proposed system is applicable for practical uses.
Story based representation for broadcasted sports video and automatic story segmentation

This paper presents a model to represent a broadcasted sports video in a semantical way and proposes a method to segment the sports video into the semantical units for the representation. Representation of a video should clarify its semantical content as accurately as possible. Our model structurizes the video and gives suitable semantical descriptions to its particular time locations based on the structures of the sports video. We consider the speech transcript as the useful information stream for the generation of the semantical descriptions. The proposed method tries to segment the speech transcript into the units of the structurization in a probabilistic framework based on Bayesian networks. Moreover, the association with the image stream enables us to find the corresponding video segments. We discuss some experimental results.
Constructing Distributed Hippocratic Video Databases for Privacy-Preserving Online Patient Training and Counseling

Digital video now plays an important role in supporting more profitable online patient training and counseling, and integration of patient training videos from multiple competitive organizations in the health care network will result in better offerings for patients. However, privacy concerns often prevent multiple competitive organizations from sharing and integrating their patient training videos. In addition, patients with infectious or chronic diseases may not want the online patient training organizations to identify who they are or even which video clips they are interested in. Thus, there is an urgent need to develop more effective techniques to protect both video content privacy and access privacy . In this paper, we have developed a new approach to construct a distributed Hippocratic video database system for supporting more profitable online patient training and counseling. First, a new database modeling approach is developed to support concept-oriented video database organization and assign a degree of privacy of the video content for each database level automatically. Second, a new algorithm is developed to protect the video content privacy at the level of individual video clip by filtering out the privacy-sensitive human objects automatically. In order to integrate the patient training videos from multiple competitive organizations for constructing a centralized video database indexing structure, a privacy-preserving video sharing scheme is developed to support privacy-preserving distributed classifier training and prevent the statistical inferences from the videos that are shared for cross-validation of video classifiers. Our experiments on large-scale video databases have also provided very convincing results.
Scene retrieval with sign sequence matching based on video and audio features

This paper presents a method of retrieving similar scenes from video streams with sign sequence matching. The visual and auditory streams are partitioned into fixed-length video/audio packets. Based on feature vectors extracted from these packets, sign sequences are formed. The sign sequence can be viewed as abstraction of video and audio features. DP matching between the target and query sign sequences allows us to find scenes similar to the query in the video stream. For the purpose of efficient processing, packets, histogram based features, and sign sequences, which are the key ideas in this method, are introduced. The preliminary experimental results show that this method is promising for quick retrieval for similar scenes
Long-term people reidentification using anthropometric signature

Anthropometric biometrics are the most suitable features for long-term people reidentification. However, feature selection is still a major problem in the anthropometric biometrics literature. In this paper, we aim at improving people reidentification by enhancing feature selection. Based on a statistical analysis of body measurements on a large-scale dataset, a new anthropometric signature is introduced. The proposed signature describes both the size and shape of human body at specific anatomical landmarks. While size is measured by Euclidian distance between four skeleton joint pairs, shape is described by the surface distance along four circular body parts. A novel algorithm is proposed to automatically segment the circular body parts from the subject point cloud using body geometry, cylindrical fitting and soft clustering. The overall system is evaluated on two public datasets using CMC and nAUC metrics. Experimental results show the effectiveness of our method compared to state of the art.
Maximum-Likelihood Estimation of Recording Position Based on Audio Watermarking

In this paper, we propose a maximum-likelihood method for estimating a recording position inside several loudspeakers as a brand-new application of digital audio watermarking, which could be useful for protecting from illegal recordings of movies. An error model of times of arrival (TOAs) is utilized to estimate the recording position. The error model is constructed based on watermarking strengths, which are calculated by a watermark detection algorithm. This enables us to estimate the position if some of the TOAs are accurately measured. Experimental results indicate that the algorithm can estimate the recording position with the mean value of errors of 0.81 m.
User and Device Adaptation for Sports Video Content

Several methods for summarizing sports videos by selecting only important scenes based upon their semantic content have been proposed so far. However, there remain two problems: (1) what is important depends on each user's preferences, and (2) the summaries should be tailored for media devices that each user has. To solve these problems, we discuss user and device adaptation for sports video summarization. The proposed framework dynamically adapts the video content to fit user's preferences using user profiles which describe the preference degrees for keywords. The video content is then presented through proper media such as image or text according to the confinement of media devices. For sports videos, the framework is tested using PCs and mobile phones as the media devices.
Owner authentication for mobile devices using motion gestures based on multi-owner template update

This paper proposes a template updating method for improving authentication accuracy in behavioral biometric authentication with hand/arm motion gestures for mobile devices. We introduce an extended version of the standard K-Medoids based clustering algorithm called supervised K-Medoids, which can handle with 2-class data such as positive samples and negative samples. Using the supervised K-Medoids, the template corresponding to each owner is selected as the one that is the most identifiable as the actual owner, and, at the same time, the most distinguishable from the others. Therefore, our method can decrease False-Rejection-Rate (FRR) and False-Acceptance-Rate (FAR) simultaneously, compared to the conventional work that is based on the template update with only the owner's data to decrease FRR. Our template update with multi-owner data attains Equal-Error-Rate (EER) of 5.2% whereas the conventional template update method with owner's own data results in 12.0% when 10 subjects authenticate with gestures for 10 days.
Visiting card understanding system

The authors present the visiting card understanding system, whose output is suitable for the input of a visiting-card database. The system consists of two modules. One is a document model which represents the hierarchical knowledge about the layout structure of visiting cards. The other is an understanding module which interprets the document model to general and test hierarchical hypotheses about the contents of a visiting card. Since the understanding module is fundamentally independent of document type, the system is applicable to many kinds of documents.<<ETX>>
International workshop on “Sensing Web”

The Web becomes a large knowledge database in the world. Most of the data stored in the Web are text based. Recently, many kinds of sensors, including obstacle sensors, video cameras, thermometers, and so on, have been installed in various places in our daily living environments: stations, streets, malls, etc. It becomes important to open the data obtained by the sensors existing in our daily living environment for various purposes. Since the data obtained by observing the real world directly with sensors include real-world information different from the Web, a new worldwide social information infrastructure, which we call Sensing Web, may be realized. In the workshop, we would like to discuss technical research issues related to pattern recognition for realizing the Sensing Web. The fields of interest are as follows, but not limited: • Sensor networks, sensor data transmission • Sensor data processing, including visual data, audio data, access logs, and other sensor data • Privacy handling for sensor data • Sensor data description for data exchange and data retrieval • Sensor data presentation • Other applications for sensor data
Event tactic analysis in sports video using spatio-temporal pattern

Recently, event detection in sports videos has been gaining some remarkable results. Unfortunately, there is a lack of useful tools for users to explore and exploit these event clips on their own demands. In this paper, a novel method using spatio-temporal patterns to analyze event tactics in sports videos is introduced. The proposed method aims to understand tactics of events such as distributions and speeds of players, or attacking/defensive formations throughout a time when such events happen without tracking objects. The major contribution of the proposed method is to model event tactics by using sequence of symbols. Each symbol represents a distribution of players in a certain period of time. Therefore, a sequence of symbols intrinsically is concerned as spatio-temporal patterns. By using these patterns, an event tactic is detected, explained, and integrated into an event video to create a visualizing abstract. This visualizing abstract is very useful to help users understand an event tactic without watching whole clip. Moreover, users could query by an example or by a text to find all events sharing the same tactic. Thorough testing with over 100 goal clips in soccer domain demonstrate the superiority of the proposed method in terms of precision recall ratios.
Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.
Mean shift and optimal prediction for efficient object tracking

A new paradigm for the efficient color-based tracking of objects seen from a moving camera is presented. The proposed technique employs the mean shift analysis to derive the target candidate that is the most similar to a given target model, while the prediction of the next target location is computed with a Kalman filter. The dissimilarity between the target model and the target candidates is expressed by a metric based on the Bhattacharyya coefficient. The implementation of the new method achieves real-time performance, being appropriate for a large variety of objects with different color patterns. The resulting tracking, tested on various sequences, is robust to partial occlusion, significant clutter, target scale variations, rotations in depth, and changes in camera position.
Statistical modeling and performance characterization of a real-time dual camera surveillance system

The engineering of computer vision systems that meet application specific computational and accuracy requirements is crucial to the deployment of real-life computer vision systems. This paper illustrates how past work on a systematic engineering methodology for vision systems performance characterization can be used to develop a real-time people detection and zooming system to meet given application requirements. We illustrate that by judiciously choosing the system modules and performing a careful analysis of the influence of various tuning parameters on the system it is possible to: perform proper statistical inference, automatically set control parameters and quantify limits of a dual-camera real-time video surveillance system. The goal of the system is to continuously provide a high resolution zoomed-in image of a person's head at any location of the monitored area. An omni-directional camera video is processed to detect people and to precisely control a high resolution foveal camera, which has pan, tilt and zoom capabilities. The pan and tilt parameters of the foveal camera and its uncertainties are shown to be functions of the underlying geometry, lighting conditions, background color/contrast, relative position of the person with respect to both cameras as well as sensor noise and calibration errors. The uncertainty in the estimates is used to adaptively estimate the zoom parameter that guarantees with a user specified probability, /spl alpha/, that the detected person's face is contained and zoomed within the image.
Incremental density approximation and kernel-based Bayesian filtering for object tracking

Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The particle filter (condensation) algorithm provides a general framework for estimating the probability density functions (pdf) of general non-linear and non-Gaussian systems. However, since this algorithm is based on a Monte Carlo approach, where the density is represented by a set of random samples, the number of samples is problematic, especially for high dimensional problems. In this paper, we propose an alternative to the classical particle filter in which the underlying pdf is represented with a semi-parametric method based on a mode finding algorithm using mean-shift. A mode propagation technique is designed for this new representation for tracking applications. A quasi-random sampling method in the measurement stage is used to improve performance, and sequential density approximation for the measurements distribution is performed for efficient computation. We apply our algorithm to a high dimensional color-based tracking problem, and demonstrate its performance by showing competitive results with other trackers.
A unified framework for uncertainty propagation in automatic shape tracking

Uncertainty handling plays an important role during shape tracking. We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences [22]. Nevertheless, this approach required user initialization of the tracking process. This paper solves the automatic initialization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework. We show how to propagate the local detection uncertainties of multiple shape candidates during shape alignment, fusion with the predicted shape prior, and fusion with subspace constraints. As a result, we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood. Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart. Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations.
Automatic image-to-model framework for patient-specific electromechanical modeling of the heart

A key requirement for recent advances in computational modeling to be clinically applicable is the ability to fit models to patient data. Various personalization techniques have been proposed for isolated sub-components of complex models of heart physiology. However, no work has been presented that focuses on personalizing full electromechanical (EM) models in a streamlined, consistent and automatic fashion, which has been evaluated on a large population. We present an integrated system for full EM personalization from routinely acquired clinical data. The importance of mechanical parameters is analyzed in a comprehensive sensitivity study, revealing that myocyte contraction and Young's modulus are the main determinants of model output variation, what lead to the proposed personalization strategy. On a large, physiologically diverse set of 15 patients, we demonstrate the effectiveness of our framework by comparing measured and calculated parameters, yielding left ventricular ejection fraction and stroke volume errors of 6.6% and 9.2 mL, respectively.
Hierarchical Learning of Curves Application to Guidewire Localization in Fluoroscopy

In this paper we present a method for learning a curve model for detection and segmentation by closely integrating a hierarchical curve representation using generative and discriminative models with a hierarchical inference algorithm. We apply this method to the problem of automatic localization of the guidewire in fluoroscopic sequences. In fluoroscopic sequences, the guidewire appears as a hardly visible, non-rigid one-dimensional curve. Our paper has three main contributions. Firstly, we present a novel method to learn the complex shape and appearance of a free-form curve using a hierarchical model of curves of increasing degrees of complexity and a database of manual annotations. Secondly, we present a novel computational paradigm in the context of Marginal Space Learning, in which the algorithm is closely integrated with the hierarchical representation to obtain fast parameter inference. Thirdly, to our knowledge this is the first full system which robustly localizes the whole guidewire and has extensive validation on hundreds of frames. We present very good quantitative and qualitative results on real fluoroscopic video sequences, obtained in just one second per frame.
Enhancement of organ of interest via background subtraction in cone beam rotational angiocardiogram

The real time X-ray angiography based on C-arm cone beam system is the workhorse imaging modality for interventional cardiac procedures. These images are two-dimensional (2D) projections of three dimensional (3D) objects along the X-ray direction. The organ of interest (OOI), such as left ventricle (LV) endocardium, in the projection image is superimposed with other anatomical structures and often has low contrast. In this study, a novel approach is proposed to isolate the OOI in projection images by subtracting with a background image, which is generated by numerical projection of 3D tomographic image with OOI masked out. Study based on one patient and one pig image is taken. About two to three-fold increase in the contrast-to-noise ratio (CNR) is achieved for LV endocardium, compared to an unprocessed image.
Scale selection for anisotropic scale-space: application to volumetric tumor characterization

A unified approach for treating the scale selection problem in the anisotropic scale-space is proposed. The anisotropic scale-space is a generalization of the classical isotropic Gaussian scale-space by considering the Gaussian kernel with a fully parameterized analysis scale (bandwidth) matrix. The "maximum-over-scales" and the "most-stable-over-scales" criteria are constructed by employing the "L-normalized scale-space derivatives", i.e., response-normalized derivatives in the anisotropic scale-space. This extension allows us to directly analyze the anisotropic (ellipsoidal) shape of local structures. The main conclusions are (i) the norm of the /spl gamma/- and L-normalized anisotropic scale-space derivatives with a constant /spl gamma/ =1/2 are maximized regardless of the signal's dimension iff the analysis scale matrix is equal to the signal's covariance and (ii) the most-stable-over-scales criterion with the isotropic scale-space outperforms the maximum-over-scales criterion in the presence of noise. Experiments with 1D and 2D synthetic data confirm the above findings. 3D implementations of the most-stable-over-scales methods are applied to the problem of estimating anisotropic spreads of pulmonary tumors shown in high-resolution computed-tomography (HRCT) images. Comparison of the first- and second-order methods shows the advantage of exploiting the second-order information.