nils-herrmann/clean_scirepeval huggingface dataset:
A Biologically Inspired System for Classification of Natural Images

A system for visual information analysis and classification based on a biologically inspired visual selective attention model with knowledge structuring is presented. The system is derived from well-known analogous processes in the visual system of primates and inference procedures of the human brain. It consists of three main units: biologically inspired visual selective attention, knowledge structuring, and clustering of visual information. The biologically inspired visual selective attention unit closely follow the mechanisms of the visual what pathway and where pathway in the primates' brain. It uses a bottom-up approach to generate a salient area based on low-level features extracted from natural images. The scale selection to determine suitable size of salient areas uses a maximum entropy approach. This unit also contains a low-level top-down selective attention module that performs decisions on interesting objects by human interaction. In this module, a reinforcement/inhibition mechanism is exploited. The knowledge structuring unit automatically creates a relevance map from salient image areas generated by the biologically inspired unit. It also derives a set of well-structured representations from low-level descriptions to drive the final classification. The knowledge structuring unit relys on human knowledge to produce suitable links between low-level descriptions and high-level representation on a limited training set. The backbone of this unit is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a growing cell structure network. The third unit of the system classification is achieved by simulating high-level top-down visual information perception and clustering using an incremental Bayesian parameter estimation method. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available
An Entropy Coding Scheme for Multi-Component Scalable Motion Information

Fully scalable video bit-stream requires layered structure of most of its components. For that reason few methods targeting scalability on motion information have been proposed over the last decade. However, layered representation requires new entropy coding strategies able to efficiently handling of redundancies between different layers. In this paper three methods for entropy coding of layered motion information are proposed. The influence of these schemes on the reconstructed video quality has been also studied
Towards semantic tagging in collaborative environments

Tags pose an efficient and effective way of organization of resources, but they are not always available. A technique called SCM/THD investigated in this paper extracts entities from free-text annotations, and using the Lin similarity measure over the WordNet thesaurus classifies them into a controlled vocabulary of tags. Hypernyms extracted from Wikipedia are used to map uncommon entities to Wordnet synsets. In collaborative environments, users can assign multiple annotations to the same object hence increasing the amount of information available. Assuming that the semantics of the annotations overlap, this redundancy can be exploited to generate higher quality tags. A preliminary experiment presented in the paper evaluates the consistency and quality of tags generated from multiple annotations of the same image. The results obtained on an experimental dataset comprising of 62 annotations from four annotators show that the accuracy of a simple majority vote surpasses the average accuracy obtained through assessing the annotations individually by 18%. A moderate-strength correlation has been found between the quality of generated tags and the consistency of annotations.
A novel architecture of semantic web reasoner based on transferable belief model

As the Semantic Web gains popularity, the technical challenges of representing and reasoning with imprecise and uncertain information still remain an outstanding issue. The foundation of the Semantic Web is the assertion of relations between entities, but these relations usually do not carry a degree or level of relationship. Using a simple subject-predicate-object triple we can say that “Alice” (subject) “likes” (predicate) “Rock music” (object), but we can not say that she does so with a confidence or level of 80%. We propose the use of the Transferable Belief Model (TBM) as a way to achieve this. Two contributions are presented in this work: an ontology to represent information in a way which is consistent with the TBM, and a reasoner to assess the knowledge of a given system. Tests show the feasibility of applying this model on large scale Semantic Web information, but further optimisations and tests must be performed.
Dynamic tree-structured sparse RPCA via column subset selection for background modeling and foreground detection

Video analysis often begins with background subtraction, which consists of creation of a background model that allows distinguishing foreground pixels. Recent evaluation of background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Processing per-pixel basis from the background is not only time-consuming but also can dramatically affect foreground region detection, if region cohesion and contiguity is not considered in the model. We present a new method in which we regard the image sequence to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix, and solve the decomposition using our approximated Robust Principal Component Analysis method extended to handle camera motion. Furthermore, to reduce the curse of dimensionality and scale, we introduce a low-rank background modeling via Column Subset Selection that reduces the order of complexity, decreases computation time, and eliminates the huge storage need for large videos.
Optimized packet scheduling for live streaming on peer-to-peer network with network coding

This paper proposed an optimized packet scheduling algorithm for live peer-to-peer streaming system, where network coding technique is extended to improve the efficiency in bandwidth utilization. We identify a problem of undesirable non-innovative packet transmission due to the latency of buffer-map update among peers, which, with many previous proposed techniques, yields to bandwidth inefficiencies. In the proposed system, we present an optimized packet scheduling algorithm for forecasting the number of required packets at parent nodes in advance, a selection mechanism for selecting and encoding packets for forwarding, and an adaptive push algorithm for smart frame skipping. The proposed optimized packet scheduling algorithm calculates the minimum number of required packets for full rate transmission. Transmitting as the scheduled results can reduce non-innovative packet transmission, save limited bandwidth for innovative transmission, thereby improving the streaming continuity. The simulation results show that the proposed scheme provides significantly better video quality, delivery ratio, lower redundancy rate, and higher innovative video packet rate compared with previous packet scheduling algorithms.
Accurate stereo 3D point cloud generation suitable for multi-view stereo reconstruction

This paper proposes a novel methodology for generating 3D point clouds of good accuracy from stereo pairs. Initially, the methodology defines some conditions for the proper selection of image pairs. Then, the selected stereo images are used to estimate dense correspondences using the Daisy descriptor. An efficient two-phase strategy to remove outliers is then introduced. Finally, the 3D point cloud is refined by combining sub-pixel accuracy correspondences estimation and the moving least squares algorithm. The proposed methodology can be exploited by multi-view stereo algorithms due to its good accuracy and its fast computation.
Systems for disparity-based multiple-view interpolation

Viewpoint adaptation from multiple-viewpoint video captures is an important tool for telepresence illusion in stereoscopic presentation of natural scenes, and for the integration of real-world video objects into virtual 3D worlds. This paper describes different low-complexity approaches for generation of virtual-viewpoint camera signals, which are based on disparity-processing techniques and can hence be implemented with much lower complexity than full 3D analysis of natural objects or scenes. A realtime hardware system, which is based on one of our algorithms, has already been developed.
Image CAPTCHA based on distorted faces

An image recognition-based CAPTCHA is proposed for increasing security in web applications. The proposed method uses distorted faces to create an image for a CAPTCHA test. The user has to recognise the well-known person that appears in the image choosing the name from a list. The method uses a feature-line morphing technique to distort the faces which morphs the well-known person's face into a cartoon or an animal. The performance of this approach is evaluated through different face recognition systems. The results show an improvement in human recognition in comparison with word-based CAPTCHAs and an increment in robustness against robots when trying to break through the tests. (6 pages)
Successive approximations for scalable content and content descriptors in multimedia applications

Interactive media aims to provide seamless access to digital content in a proactive and user centred manner. Regardless of the nature of the delivery channel, homogeneous or heterogeneous, and regardless of the user platform, media should be accessible efficiently and at optimum quality over the full available bandwidths and independently of terminal capabilities. In this paper, we argue that the development of efficient applications underpinning interactive media systems need to be based on successive approximations of content and metadata. The presented discussion relates to the content engineering side of media applications and describes how it can be used to easy, if not to achieve, the overall goal of a new generation of media applications. Three examples combining available technologies and ongoing developments to produce successive approximations of content and metadata are presented. Their application in the development of interactive multimedia systems is discussed and results of computer experiments aiming to demonstrate the suitability of these techniques are reported.
Linear programming concept visualization

A visualization scheme as a tool to find the solution of any 3D-linear programming problem is introduced. The presented approach is highly suitable to draw and visualize interactively the feasible region of a given 3D-linear programming problem. It can be used for a better understanding of the solution process when different methods devoted to solve the underlying problem are applied, e.g., the simplex method. The proposed technique comprises sensitive analysis during the solution process and interactive visualization of the feasible region. The analysis leading to the introduced schema also shows that the design of an appropriate and simple vertex representation is crucial to manage any order of degeneracy. To deal with this paradigm and to some extent to formalize it, the concept of adjacency invariance is introduced. Several experiments have been conducted to test and assess the performance of the introduced concepts and techniques.
Efficient Text Color Modulation for Printed Side Communications and Data Hiding

This paper improves the use of text color modulation (TCM) as a reliable text document data hiding method. Using TCM, the characters in a document have their color components modified (possibly unperceptually) according to a side message to be embedded. This work presents a detection metric and an analysis determining the detection error rate in TCM, considering an assumed print and scan (PS) channel model. In addition, a perceptual impact model is employed to evaluate the perceptual difference between a modified and a non-modified character. Combining this perceptual model and the results from the detection error analysis it is possible to determine the optimum color modulation values. The proposed detection metric also exploits the orientation characteristics of color halftoning to reduce the error rate. In particular, because color halftoning algorithms use different screen orientation angles for each color channel, this is used as an effective feature to detect the embedded message. Experiments illustrate the validity of the analysis and the applicability of the method.
Bitstream syntax description based adaptation of scalable video

Using scalable coding technology the multimedia content can be easily made accessible on different display terminals connected through heterogeneous channels, as scalable bitstreams can be seamlessly adapted according to the network restrictions and/or terminal properties. To make the adaptation process coding format-independent, a XML description of the bitstream scalable structure is created. Then, the adaptation is performed on the XML description and an adapted bitstream is generated from the adapted XML description. In this paper techniques used in scalable video coding, bitstream descriptions and XML as well as extractor based adaptation of scalable video bitstreams are presented.
Key components for an advanced segmentation system

An advanced image and video segmentation system is proposed. The system builds on existing work, but extends it to achieve efficiency and robustness, which are the two major shortcomings of segmentation methods developed so far. Six different schemes containing several approaches tailored for diverse applications constitute the core of the system. The first two focus on very-low complexity image segmentation addressing real-time applications under specific assumptions. The third scheme is a highly efficient implementation of the powerful nonlinear diffusion model. The other three schemes address the more complex task of physical object segmentation using information about the scene structure or motion. These techniques are based on an extended diffusion model and morphology. The main objective of this work has been to develop a robust and efficient segmentation system for natural video and still images. This goal has been achieved by advancing the state-of-art in terms of pushing forward the frontiers of current methods to meet the challenges of the segmentation task in different situations under reasonable computational cost. Consequently, more efficient methods and novel strategies to issues for which current approaches fail are developed. The performance of the presented segmentation schemes has been assessed by processing several video sequences. Qualitative and quantitative result of this assessment are also reported.
Studying rate control methods for UHDTV delivery using HEVC

Since the early video coding standardisation efforts, rate control has been considered essential for almost any application. With the advent of improved video coding standards and the introduction of advanced flexible coding tools, previous Rate-Distortion (RD) models used for rate control have become obsolete. To address this issue, some rate control methods have been recently proposed specifically for the current state-of-the-art High Efficiency Video Coding (HEVC) standard which introduce many useful features, such as a robust correspondence between the rate and Lagrange multiplier λ. However, when applying these rate control methods on sequences in the new Ultra High Definition Television (UHDTV) format, degraded coding performance was observed. In this paper, an analysis of the state-of-the-art HEVC rate control method was performed and two directions for its improvement were evaluated. These improvements target frame-level bit-allocation and model parameter initialisation. When compared to the rate control method implemented in the HEVC reference software, these improvements result in reduced BD-rate losses of 3.1% and 2.1%, versus the 8.8% provided by the reference algorithm. Moreover, the proposed improvements increase the accuracy in hitting the target bit-rate.
A Push Scheduling Algorithm with Network Coding for Peer-to-Peer Live Streaming

Network coding(NC) appears to bring substantial improvements in terms of throughput and delay in collaborative media streaming applications. A key aspect of NCdriven live peer-to-peer streaming is the packet scheduling policy. In previous peer-to-peer network, the buffer-map is widely used to pull or push packets from parent nodes to achieve data transmission. However, it often leads to undesirable long playback delay in live streaming applications. In this paper, we propose a new push scheduling policy to address this problem. In the proposed scheme, the packet scheduling is calculated at parent client nodes in advance. Then the parent nodes push the networkencoded packets to the children nodes according to the pre-calculated scheduling algorithm. As a consequence, the proposed packet scheduling algorithm eliminates the processes of buffer-map updating and packet requesting, which in turn reduces the number of redundant packets and leads to less traffic of redundant video data over the Internet. The simulation results show that the proposed scheme provides significantly better delivery ratio, less control traffic and fewer redundant packets than conventional push-based schemes.
Using 3D structure and anisotropic diffusion for object segmentation

We describe an advanced technique that can be employed to carry out the segmentation task. The goal is the development of a system capable of solving the segmentation problem in most situations encountered in video sequences taken from real world scenes. For this aim the presented segmentation scheme comprises different processing steps implemented as independent modules. The core of the system is a module for multiscale image simplification by anisotropic diffusion. The remaining modules are concerned with the subsequent image segmentation of the resulting smoothed images. The mathematical model supporting the implemented algorithms is based on the numerical solution of a system of nonlinear partial differential equations introduced by Perona and Malik (1987). The idea at the heart of this approach is to smooth the image in direction parallel to the object boundaries, inhibiting diffusion across the edges. The goal of this processing step is to enhance edges keeping their correct position, reducing noise and smoothing regions with small intensity variations. The techniques for object segmentation presented are based on image simplification by nonlinear diffusion and subsequent extraction of object masks taking into account disparity or motion fields as additional information. Two different strategies are considered in order to extract masks of physical objects in the scene: depth-driven nonlinear diffusion and edge extraction and enhancement in scale-space followed by edge matching in two different views of the same scene.
Robust and Transparent Color Modulation for Text Data Hiding

This paper improves the use of text color modulation (TCM) as a reliable text document data hiding method. Using TCM, the characters in a document have their color components modified (possibly unperceptually) according to a side message to be embedded. This work presents a detection metric and an analysis determining the detection error rate in TCM, considering an assumed print and scan (PS) channel model. In addition, a perceptual impact model is employed to evaluate the perceptual difference between a modified and a non-modified character. Combining this perceptual model and the results from the detection error analysis it is possible to determine the optimum color modulation values. The proposed detection metric also exploits the orientation characteristics of color halftoning to reduce the error rate. In particular, because color halftoning algorithms use different screen orientation angles for each color channel, this is used as an effective feature to detect the embedded message. Experiments illustrate the validity of the analysis and the applicability of the method.
Fast intra mode decision for HEVC based on texture characteristic from RMD and MPM

In this paper, we proposed a fast intra mode decision algorithm for HEVC to further reduce the candidate modes of RDOQ or even skip RDOQ for a PU, which exploited not only the texture consistency of neighbouring PUs reflected by the relation between the optimal RMD mode and MPM, but also the texture characteristic in a PU reflected by the best two RMD modes. Experimental results show that our proposed algorithm can reduce 31.3% intra coding time with 0.56% BD-rate loss on average.
Linear Gaussian blur evolution for detection of blurry images

Even though state-of-the-art digital cameras are equipped with auto-focusing and motion compensation functions, several other factors including limited contrast, inappropriate exposure time and improper device handling can still lead to unsatisfactory image quality such as blurriness. Indeed, blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed. A new technique for automatic detection and removal of blurry pictures is presented. Initially, a set of interest points and local image areas is extracted. These areas are then evolved in time according to the conventional linear scale space. The gradient of the evolution curve through scale is then used to produce a ‘blur graph’ representing the probability of a picture being blurred or not. Complexity is kept low by applying a Monte-Carlo like technique for the selection of representative image areas and interest points and by implicitly estimating the gradient of the scale-space curve evolution. An exhaustive evaluation of the proposed technique is conducted to validate its performance in terms of detection accuracy and efficiency.
Content-Based Guided Image Filtering, Weighted Semi-Global Optimization, and Efficient Disparity Refinement for Fast and Accurate Disparity Estimation

This paper presents a novel approach, which relies on content-based guided image filtering and weighted semi-global optimization for fast and accurate disparity estimation. The approach uses a pixel-based cost term that combines gradient, Gabor-Feature, and color information. The pixel-based matching costs are filtered by applying guided image filtering, which relies on rectangular support windows of two different sizes. In this way, two filtered costs are estimated for each pixel. Among the two filtered costs, the one that will be finally assigned to each pixel depends on the local image content around this pixel. The filtered cost volume is further refined by exploiting weighted semi-global optimization, which improves the disparity estimation accuracy. Finally, the disparity refinement in outlier regions relies on a straightforward and time-efficient outliers handling scheme and on a simple approach which deals with the disparity outliers at depth discontinuities. Experimental results on the Middlebury online stereo evaluation benchmark and 27 additional Middlebury stereo pairs prove that our method is able to generate disparity maps with high accuracy while keeping the computational cost low.