nils-herrmann/clean_scirepeval huggingface dataset:
Bringing user satisfaction to media access: the IST BUSMAN Project

Interactive and seamless access to video content is facilitated by technology that is able to annotate and retrieve media data efficiently and automatically with the minimal human interventions. Media should be accessible by intended users quickly and independent of the database size, regardless of the homogeneous or heterogeneous nature of the delivery channels or the user computing platform. These requirements are driving research and development efforts in the European project BUSMAN; in which a system for efficient annotation, seamless delivery of and access to video components across heterogeneous channels is being developed. The BUSMAN system architecture is based on the needs and expectations of two types of users: content providers and end users. In this paper an overview of the BUSMAN system is presented and its main functionalities are described.
Compact hierarchical image descriptors

An efficient approach for image annotation and retrieval is presented. The main objective is to overcome the speed limitations of existing video indexing and retrieval systems. A family of successively simplified image descriptors is defined using colour histograms. The colour distribution is quantised according to a scale-parameter obtained by convolving the distribution function with Gaussians of increasing size. The generated family of histograms is then used to define descriptors at various levels of detail. Using this hierarchical descriptor structure large sets of non-similar images are discriminated at very low computational cost using low detailed descriptions. The search is then refined progressively until only a few very similar objects or images are found and ranked using higher levels of detail. To compare image descriptors the earth mover's distance is used. Experiments have been conducted to assess the performance of the proposed technique.
Robust scalable video transmission using adaptive double binary turbo code

In this paper an approach for joint source-channel coding in a spatio-temporal wavelet-based scalable coding environment is presented. The scalable quality layers are protected using a double binary turbo code for robust scalable transmission over wireless channels. In the proposed schema the double binary turbo code is used for bitstream adaptation with unequal error protection. An efficient method to estimate the minimum distance of the double binary turbo codes is also proposed. Experimental results show promising performance of the proposed model as compared to equal error protection at low signal to noise ratio.
A resolution adaptive interpolation technique for enhanced decoding of scalable coded video

Subpixel accurate motion compensated temporal filtering introduces a significant coding gain in scalable 3D wavelet video codecs. The influence of the chosen subpixel interpolation technique has not yet been fully analysed in the context of resolution scalability. That problem is addressed in this paper. It is shown that support for increased accuracy and resolution adaptive spatial interpolation needs to be featured in a scalable video decoder, when low resolution sequences are targeted. Using the proposed resolution adaptive filters based on sinc kernels leads to improved decoding performance at low resolution in the sense of achieving higher quality while reducing the complexity of the system.
Adaptive Salient Block Based Image Retrieval in Multi-Feature Space

In this paper, an approach to tackle the object based image retrieval problem is proposed. The core technique is designed to adaptively and efficiently locate the salient block of objects of interest in each image. The salient blocks are then used as cues for representing the whole images when semantic-based searching is performed. Relevance Feedback is seamlessly integrated in the retrieval process. In each iteration, the user is requested to select images relevant to the query concept. Salient blocks of the selected images are used as training examples. To guarantee the accuracy of salient block matching, the similarities of block regions are calculated within an optimised concept-specific multi-feature space. In the multi-feature space, it is expected that the visual patterns of objects of interest can be effectively discriminated from irrelevant regions. This multi-feature space metric is learned from a group of representative salient blocks using a multi-objective optimisation approach. An empirical assessment of the proposed technique was conducted. Selected results show good performance of the proposed approach.
Scene classification of ambiguous visual information

A framework for scene classification of ambiguous visual information is described and validated. Context-based scene classification algorithm is used for ambiguous visual information applications. The system can differentiate ambiguous visual information from various semantic meanings using a spatial layout of context information, which capture the "essential" of the scene. Distinct from previous frameworks, the system presents the entire scheme of being biologically plausible and application efficiency, offering a straightforward platform for rapid analysis and interpretation on ambiguous visual information demonstrating generalization and scalability of the approach.
Segmentation of Document Images Using Higher Order Statistics

This work presents an efficient post-segmentation method for separating text from the background in document images. For this task, this paper proposes the use of textured patterns to represent text in documents, instead of the standard black. It is shown that, in poor quality documents, text segmentation is more efficient when the characters in the document are represented in a halftoned gray level prior to printing. This occurs because the halftoning process induces statistical characteristics that help the text to be distinguished from noise or background. A typical case are noisy printed and scanned documents. Experiments validate the analysis and the applicability of the segmentation method. An important application for the method is in the postal service, where letters have their addresses segmented for automatic sorting.
Surveillance ontology for legal, ethical and privacy protection based on SKOS

In a world filled with heightened vandalism and terrorist activities, video surveillance forms an integral part of any incident investigation. While aiming to provide safety and security for the citizens, CCTV cameras are exponentially deployed around metropolitan cities. However, the information thus collected and further processed should comply with the legal and ethical rules defined by the law. The primary ethical issue invoked by surveillance activities in general is that of privacy. In order to ensure that a surveillance system framework complies with the legal, ethical and privacy requirements of the law, in this paper we present a Surveillance Ontology extending the SKOS foundational ontology. The objective of this ontology is to translate the high-level linguistic rules into the information that can be processed and used to assess the compliance of the video analysis module with the rules defined.
Rate-Distortion Optimized Motion-Compensated Prediction for Packet Loss Resilient Video Coding

A rate-distortion optimized motion-compensated prediction method for robust video coding is proposed. Contrasting methods from the conventional literature, the proposed approach uses the expected reconstructed distortion after transmission, instead of the displaced frame difference in motion estimation. Initially, the end-to-end reconstructed distortion is estimated through a recursive per-pixel estimation algorithm. Then the total bit rate for motion-compensated encoding is predicted using a suitable rate distortion model. The results are fed into the Lagrangian optimization at the encoder to perform motion estimation. Here, the encoder automatically finds an optimized motion compensated prediction by estimating the best tradeoff between coding efficiency and end-to-end distortion. Finally, rate-distortion optimization is applied again to estimate the macroblock mode. This process uses previously selected optimized motion vectors and their corresponding reference frames. It also considers intraprediction. Extensive computer simulations in lossy channel environments were conducted to assess the performance of the proposed method. Selected results for both single and multiple reference frames settings are described. A comparative evaluation using other conventional techniques from the literature was also conducted. Furthermore, the effects of mismatches between the actual channel packet loss rate and the one assumed at the encoder side have been evaluated and reported in this paper
Linear and nonlinear scale-spaces for video indexing and retrieval

An extension of the conventional linear and nonlinear scale-space models for shape and image simplification, indexing and retrieval is presented. The linear model can be used for shape based retrieval when the main video objects have been identified. In this context an algorithm for contour simplification is introduced. Two different nonlinear filtering techniques for image simplification are also described. The first one is generated by convolution with a group of anisotropic weighted filter kernels. The second is based on a parabolic differential equation in divergence form. The linear and nonlinear form of these diffusion filters allows to integrate additional information to control the evolution in both spatial and temporal directions. This property is used to extend the conventional nonlinear scale-space in order to perform content-based segmentation of natural video. (5 pages)
Disparity/segmentation analysis: matching with an adaptive window and depth-driven segmentation

Most of the emerging content-based multimedia technologies are based on efficient methods to solve machine early vision tasks. Among other tasks, object segmentation is perhaps the most important problem in single image processing, whereas pixel-correspondence estimation is the crucial task in multiview image analysis. The solution of these two problems is the key for the development of the majority of leading-edge interactive video-communication technologies and telepresence systems. In this paper, we present a robust framework comprised of joined pixel-correspondence estimation and image segmentation in video sequences taken simultaneously from different perspectives. An improved concept for stereo-image analysis based on block matching with a local adaptive window is introduced. The size and shape of the reference window is calculated adaptively according to the degree of reliability of disparities estimated previously. Considerable improvements are obtained just within object borders or image areas that become occluded by applying the proposed block-matching model. An initial object segmentation is obtained by merging neighboring sampling positions with disparity vectors of similar size and direction. Starting from this initial segmentation, true object borders are detected using a contour-matching algorithm. In this process, the contour of the initial segmentation is taken as a reference pattern, and the edges extracted from the original images, by applying a multiscale algorithm, are the candidates for the true object contour. The performance of the introduced methods has been verified.
Surveillance centric coding

In this paper we introduce the paradigm of Surveillance Centric Coding (SCC), in which coding aims to achieve bit- rate optimisation and adaptation of surveillance videos for storing and transmission purposes. In the proposed approach the SCC encoder communicates with Video Content Analysis (VCA) module that detects events of interests in video captured by CCTV. Bit-rate optimization and adaptation is achieved by exploiting the scalability properties of the employed codec. Time segments containing events relevant to surveillance application are encoded using high spatio- temporal resolution and quality while the irrelevant portions from the surveillance standpoint are encoded at low spatio- temporal resolution and / or quality. Thanks to the scalability of the produced compressed bit-stream, additional bit-rate adaptation is possible, for instance for the transmission purposes. Experimental evaluation shows that significant reduction in bit-rate can be achieved by the proposed approach without loss of information relevant to surveillance applications.
An ill-posed operator for secure image authentication

Many problems in science and technology are modeled by ill-posed operators and the main difficulty in obtaining accurate solutions is caused by the high instability of such operators. The paper introduces a new schema for secure image authentication. It is based on a rather unconventional approach - the extremely high sensitivity of ill-posed operators to any change in the input data is turned into a tool to achieve fragile watermarking for secure image verification. The ill-posed operator of concern is based on a highly ill-conditioned matrix interrelating the watermark and the original image. Authentication is achieved by solving the least squares problem associated with the underlying linear operator. Regarding general requirements for secure watermark-based authentication, analytical and practical aspects of the introduced technique are discussed. It is shown that the proposed authentication schema is highly secure while providing excellent tamper localization. Several experiments were conducted to demonstrate the effectiveness of the technique when it is subjected to different attacks, including vector quantization counterfeiting and cropping.
An object-based system for stereoscopic viewpoint synthesis

This paper describes algorithms that were developed for a real-time stereoscopic videoconferencing systems with viewpoint adaptation. The goal is a real telepresence illusion, which is achieved by synthesis of intermediate views from a stereoscopic camera shot with a rather large baseline. The actual viewpoint will be adapted according to the head position of the viewer, such that the impression of motion parallax is produced. The object-based system first identifies foreground and background regions and applies disparity estimation to the foreground object. A hierarchical block matching algorithm is employed for this purpose which takes into account the position of high-activity feature points and the object/background border positions. Using the disparity estimator's output, it is possible to generate arbitrary intermediate views by projections from the left- and right-view images. For this purpose, we have also developed an object-based interpolation algorithm, taking into account a very simple convex-surface model of a person's face and body. Though the algorithms had to be held rather simple under the constraint of hardware feasibility, we obtain a good quality of the intermediate-view images. Finally, we describe the hardware concept for the disparity estimator, which is the most complicated part of the algorithm.
Combining segmentation by nonlinear diffusion and shape adaptive block-matching for content based coding

A technique for image simplification and segmentation in scale-space is presented. Segmentation masks are then used to estimate accurately the parameters describing motion of single objects in the scene. The segmentation approach relies on a non-linear diffusion model in which multiscale image simplification and subsequent segmentation of the resulting smoothed images is performed. Motion parameters describing the dynamic of each single object are estimated by applying a generalised block-matching approach. The main strategy behind this technique is to use bilinear transformations to establish a spatial correspondence between the points in the input and output images. The performance of the presented techniques is evaluated by processing natural video sequences. (6 pages)
Decomposition and matching: Towards efficient automatic Chinese character stroke extraction

In this paper, given images of Chinese characters, we present an automatic stroke extraction system, which consists of character decomposition, shape matching, cross area extraction and stroke segment combination. First, a character is decomposed into isolated stroke structures according to the connection. Then, we extract shape contexts of stroke structures and find the matched counterparts in a standard database by shape matching. Cross points are computed, and cross areas are extracted according to a proposed adaptive cross area extraction method based on point-to-boundary orientation distance. For a shape structure with the matched structure, we optimize cross point set and combine its stroke segments according to the correct cross point set and combination way of its matched counterpart. For those without matched structures, we propose an angle based stroke segment combination method to combine the segments into a complete stroke. Experimental results indicate that the proposed system achieves high accuracy and demonstrates prominent augmentation on efficiency.
Two-Pass Rate Control for Improved Quality of Experience in UHDTV Delivery

Rate control plays an important role in any video coding application and it was extensively studied in the context of previous video coding standards. However, the current state-of-the-art high efficiency video coding (HEVC) standard introduces many flexible tools making previous rate-distortion models used in rate control insufficiently accurate. Recently, a few rate control methods have been developed for HEVC that introduce many useful features, such as a robust correspondence between the rate and Lagrange multiplier <inline-formula><tex-math notation="LaTeX">$\lambda$ </tex-math></inline-formula>. Nonetheless, previous rate control algorithms for HEVC do not address typical content in television applications that consists of frequent scene changes. Furthermore, the new ultra high definition television (UHDTV) format, which is expected to become widespread in the future, demands for even higher compression efficiency. To overcome these issues, a two-pass rate control method is proposed in this paper, targeting the encoding of UHDTV content. In the first pass, a fast encoder with limited set of coding tools is used during pre-encoding step to obtain the data used for rate allocation and model parameter initialization, which will then be used during the second pass. To avoid multiple encoding steps when deriving this information, a variable quantization parameter framework is proposed. Experimental results show that the proposed rate control method outperforms the well-known HEVC rate control method. When compared with variable bit-rate encoding mode, the proposed two-pass rate control method achieves on average <inline-formula><tex-math notation="LaTeX">$2.9\%$</tex-math></inline-formula> BD-rate losses. That is significantly better than the state-of-the-art HEVC rate control method, which achieves an average <inline-formula> <tex-math notation="LaTeX">$8.8\%$</tex-math></inline-formula> BD-rate loss. The proposed method also provides a more consistent quality fluctuation with time, measured with standard deviation of frame PSNR values, required for high Quality of Experience.
Describing Objects with Multiple Features for Visual Information Retrieval and Annotation

This paper describes how a multi-feature merging approach can be applied in semantic-based visual information retrieval and annotation. The goal is to identify the key visual patterns of specific objects from either static images or video frames. It is shown how the performance of such visual-to-semantic matching schemes can be improved by describing these key visual patterns using particular combinations of multiple visual features. A multi-objective learning mechanism is designed to derive a suitable merging metric for different features. The core of this mechanism is a widely used optimisation method - the multi-objective optimisation strategies. Assessment of the proposed technique has been conducted to validate its performance with natural images and videos.
BINARY PARTICLE SWARM AND FUZZY INFERENCE FOR IMAGE CLASSIFICATION

In this paper, binary particle swarm and fuzzy inference considered for the image classification problem. A combination of binary particle swarms and fuzzy inference logic is applied on the classifier output produces with self organizing maps and particle swarm optimization. Several MPEG-7 colour and texture descriptors are used in this paper. The proposed approach, improves precision of the retrieved images whiles keeping high values for recall.
People recognition in ambiguously labeled Photo Collections

We show how to recognize people based on their faces in Consumer Photo Collections while also incorporating context in the form of ambiguous labels. Such labels can be assigned to single photos (depicting multiple people) as well as to entire sets of photos (e.g. relating to events). To achieve this, we devise a unified framework that has a graphical model along a distance-based face description method at its core. We evaluate our probabilistic approach by performing experiments on two datasets, one of which includes around 5000 face appearances spanning nearly ten years.
H.264/AVC to wavelet-based scalable video transcoding supporting multiple coding configurations

Scalable Video Coding (SVC) enables low complexity adaptation of the compressed video, providing an efficient solution for video content delivery through heterogeneous networks and to different displays. However, legacy video and most commercially available content capturing devices use conventional non-scalable coding, e.g., H.264/AVC. This paper proposes an efficient transcoder from H.264/AVC to a wavelet-based SVC to exploit the advantages offerend by the SVC technology. The proposed transcoder is able to cope with different coding configurations in H.264/AVC, such as IPP or IBBP with multiple reference frames. To reduce the transcoder's complexity, motion information and presence of the residual data extracted from the decoded H.264/AVC video are exploited. Experimental results show a good performance of the proposed transcoder in terms of decoded video quality and system complexity.
Augmented reality mirror for virtual facial alterations

We present a system for virtual mirror experience that performs attentive facial geometric alterations in augmented reality. The virtual mirror is simulated using commonly available PC with webcam that capture, process and display video in real-time. High realism is obtained by considerate 3D-aware warping of the 2D captured video. A Kalman-based real-time face tracker is used for 3D head pose estimation and accurate facial features localization. The 3D face model used is adapted to the person in front of the mirror by utilizing active shape models for facial landmarks detection, followed by z-depth progressive refining. Geometric adjustments are performed on 3D face vertices while the 2D warping is calculated utilizing the location of back-projected-to-2D face model vertices. The evaluation of our system shows that realistic facial modifications can be rendered in scenarios that correspond to typical usage of a real mirror.