nils-herrmann/clean_scirepeval huggingface dataset:
A Knowledge Structuring Technique for Image Classification

A system for image analysis and classification based on a knowledge structuring technique is presented. The knowledge structuring technique automatically creates a relevance map from salient areas of natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of the knowledge structuring technique 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. Classification is achieved by simulating high-level top-down visual information perception and classifying 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.
A topology synthesizing approach for classification of visual information

A system for classification of visual information based on a topology synthesizing approach is presented. The topology synthesizing approach automatically creates a relevance map from essential regions of visual information. It also derives a set of well-organized representations from low-level description to drive the final classification. The backbone of the topology synthesizing approach is a mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a self-organizing tree algorithm. Classification is achieved by simulating high-level top-down visual information perception and classifying 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.
Mpeg2 Watermarking Channel Protection Using Duo-Binary Turbo Codes

This paper describes scheme for protection of watermarking channel and its capacity enhancement using state-of-the-art error correction technique - turbo coding. Duo-binary codes were used for protection since they are perform better then classical turbo coders in terms of better convergence for iterative decoding, a large minimum distance and also computational expensiveness. A spread spectrum watermarking technique is used to insert watermark. Proposed pseudo-random watermarking bits spreading, amplitude adjustment in DCT domain based on block classification and bit-rate preserving increased signal to noise ratio of the watermarking channel. However, it was essential to introduce an error correction technique in order to achieve high capacity and robustness. In addition, experimental results on robustness to transcoding are presented
Approximated RPCA for fast and efficient recovery of corrupted and linearly correlated images and video frames

This paper presents an approximated Robust Principal Component Analysis (ARPCA) framework for recovery of a set of linearly correlated images. Our algorithm seeks an optimal solution for decomposing a batch of realistic unaligned and corrupted images as the sum of a low-rank and a sparse corruption matrix, while simultaneously aligning the images according to the optimal image transformations. This extremely challenging optimization problem has been reduced to solving a number of convex programs, that minimize the sum of Frobenius norm and the l1-norm of the mentioned matrices, with guaranteed faster convergence than the state-of-the-art algorithms. The efficacy of the proposed method is verified with extensive experiments with real and synthetic data.
Fast and Robust Edge Extraction in Unorganized Point Clouds

Edges provide important visual information in scene surfaces. The need for fast and robust feature extraction from 3D data is nowadays fostered by the widespread availability of cheap commercial depth sensors and multi-camera setups. This article investigates the challenge of detecting edges in surfaces represented by unorganized point clouds. Generally, edge recognition requires the extraction of geometric features such as normal vectors and curvatures. Since the normals alone do not provide enough information about the geometry of the cloud, further analysis of extracted normals is needed for edge extraction, such as a clustering method. Edge extraction through these techniques consists of several steps with parameters which depend on the density and the scale of the point cloud. In this paper we propose a fast and precise method to detect sharp edge features by analysing the eigenvalues of the covariance matrix that are defined by each point's k-nearest neighbors. Moreover, we evaluate quantitatively, and qualitatively the proposed methods for sharp edge extraction using several dihedral angles and well known examples of unorganized point clouds. Furthermore, we demonstrate the robustness of our approach in the noisier real-world datasets.
Comparison of MPEG-7 descriptors for long term selection of reference frames

During the last years, the amount of multimedia content has greatly increased. This has multiplied the need of efficient compression of the content but also the ability to search, retrieve, browse, or filter it. Generally, video compression and indexing have been investigated separately. However, as the amount of multimedia content grows, it will be very interesting to study representations that, at the same time, provide good compression and indexing functionalities. Moreover, even if the indexing metadata is created for functionalities such as search, retrieval, browsing, etc., it can also be employed to increase the efficiency of current video codecs. Here, we use it to improve the long term prediction step of the H.264/AVC video codec. This paper focuses on the comparison between four different MPEG-7 descriptors when used in the proposed scheme.
Registration of images to unorganized 3D point clouds using contour cues

Low resolution commercial 3D sensors contribute to computer vision tasks even better when the analysis is carried out in a combination with higher resolution image data. This requires registration of 2D images to unorganized 3D point clouds. In this paper we present a framework for 2D-3D data fusion to obtain directly the camera pose of a 2D color image in relation to a 3D point cloud. It includes a novel multiscale intensity feature detection algorithm and a modified ICP procedure based on point-to-line distances. The framework is generic for several data types (such as CAD designs or LiDAR data without photometric information), and results show that performance is comparable to the state of the art, while avoiding manual markers or specific patterns on the data.
Robust segmentation and representation of foreground key regions in video sequences

This paper deals with the extraction and characterization of foreground objects in video sequences. The algorithm first computes the mosaic image representing the background information and then extracts foreground objects. In this last step, the foreground objects are progressively extracted taking into account the reliability of the contour information. This extraction step is based on morphological tools. Finally, the foreground objects are characterized by their shape, texture and motion trajectory. Moreover, some information about the temporal evolution of non rigid objects is also extracted. This feature extraction algorithm is particularly suitable for the indexing, search and retrieval applications.
Interactive registration method for 3D data fusion

Commercial depth sensors represent an opportunity for automation of certain 3D production and analysis tasks. One way to overcome some of their inherent limitations is by capturing the same scene with several depth sensors and merging their data, i.e. by performing 3D data fusion, which requires the registration of point clouds from different sensors. We propose a new interactive, fast and user-friendly method for depth sensor registration. We replace the traditional checkerboard pattern used to extract key points in the scene by a finger detector. This provides a main advantage: the method is easier to use and does not require external objects, while the elapsed time and the registration error are similar to those obtained through the classical method. We test the proposed approach with an interactive hand tracking application, improved to use more than a single sensor, and we show the increase in detection area by more than 70%.
Collaborative voting of 3D features for robust gesture estimation

Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each selected point (anchor point) contributes to the estimation by casting votes. The main idea is to detect a body part by accumulating the inference of other trained body parts. The collaborative voting encodes the global context of human pose, while 3D features represent local appearance. Body parts contributing to the detection are interpreted as a voting process. Experimental results for different 3D features prove the validity of the proposed algorithm.
Multi-Resolution Illumination Compensation for foreground extraction

Illumination changes may lead to false foreground (FG) segmentation and tracking results. Most of the existing FG extraction algorithms obtain a background (BG) estimation from temporal statistical parameters. Such algorithms consider a quasi-static BG which does not change but slowly. Therefore, fast illumination changes are not taken into account by the BG estimator and they are considered as FG. The aim of the proposed algorithm is to reduce illumination effects in video sequences in order to improve foreground segmentation performances.
Fusion of colour and depth partitions for depth map coding

3D video coding includes the use of multiple color views and depth maps associated to each view. An adequate coding of depth maps should be adapted to the characteristics of depth maps: smooth regions and sharp edges. In this paper a segmentation-based technique is proposed for improving the depth map compression while preserving the main discontinuities that exploits the color-depth similarity of 3D video. An initial coarse depth map segmentation is used to locate the main discontinuities in depth. The resulting partition is improved by fusing a color partition. We assume that the color image is first encoded and available when the associated depth map is encoded, therefore the color partition can be segmented in the decoder without introducing any extra cost. A new segmentation criterion inspired by super-pixels techniques is proposed to obtain the color partition. Initial experimental results show similar compression efficiency to hevc with a big potential for further improvements.
Scalable Delivery of Navigable and Ultra‐High Resolution Video

In recent years many developments have addressed the generic objective of delivering audiovisual content based on a single representation made available at the source, and where the network gets the ability to adapt the content on an end user basis. This chapter reviews the different aspects related to format-agnostic delivery. It proposes methods that try to combine the benefits of two approaches: scale to many users and spatial interaction at the highest picture quality available. The chapter focuses on a new functionality that enables a client device to interactively and efficiently access a portion of ultra-high definition panoramic encoded content. It presents examples showing how the tiling approach gives some lever to optimise the overall coding performance of the panorama, as well as the selection of tiles to be transmitted to an interactive client. Finally, the chapter addresses two approaches to develop a transport mechanism, each targeting different deployment scenarios.
Surface reconstruction by restricted and oriented propagation

This paper considers the problem of reconstructing a surface from a point set. More specifically, we propose a method which focuses on obtaining fast surface reconstructions for visual purposes. The proposed scheme is based on propagation in a voxelized space, which is performed in the directions defined by a propagation pattern, during an optimal number of iterations. Real-time applications are conceivable thanks to a low execution time and computational cost, keeping an acceptable visual quality of the reconstruction.
Depth map coding based on a optimal hierarchical region representation

Multiview color information used jointly with depth maps is a widespread technique for 3D video. Using this depth information, 3D functionalities such as free view point video can be provided by means of depth-image-based rendering techniques. In this paper, a new technique to encode depth maps is proposed. Based on the usually smooth structure and the sharp edges of depth map, our proposal segments the depth map into homogeneous regions of arbitrary shape and encodes the contents of these regions using different texture coding strategies. An optimal lagrangian approach is applied to the hierarchical region representation provided by our segmentation technique. This approach automatically selects the best encoding strategy for each region and the optimal partition to encode the depth map. To avoid the high coding costs of coding the resulting partition, a prediction is made using the associated decoded color image.
Antipodally Invariant Metrics for Fast Regression-Based Super-Resolution

Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method.
Real-Time Head and Hand Tracking Based on 2.5D Data

A novel real-time algorithm for head and hand tracking is proposed in this paper. This approach is based on data from a range camera, which is exploited to resolve ambiguities and overlaps. The position of the head is estimated with a depth-based template matching, its robustness being reinforced with an adaptive search zone. Hands are detected in a bounding box attached to the head estimate, so that the user may move freely in the scene. A simple method to decide whether the hands are open or closed is also included in the proposal. Experimental results show high robustness against partial occlusions and fast movements. Accurate hand trajectories may be extracted from the estimated hand positions, and may be used for interactive applications as well as for gesture classification purposes.
Half hypersphere confinement for piecewise linear regression

Recent research in piecewise linear regression for Super-Resolution has shown the positive impact of training regressors with densely populated clusters whose datapoints are tight in the Euclidean space. In this paper we further research how to improve the locality condition during the training of regressors and how to better select them during testing time. We study the characteristics of the metrics best suited for the piecewise regression algorithms, in which comparisons are usually made between normalized vectors that lie on the unitary hypersphere. Even though Euclidean distance has been widely used for this purpose, it is suboptimal since it does not handle antipodal points (i.e. diametrically opposite points) properly, as vectors with same module and angle but opposite directions are, for linear regression purposes, identical. Therefore, we propose the usage of antipodally invariant metrics and introduce the Half Hypersphere Confinement (HHC), a fast alternative to Multidimensional Scaling (MDS) that allows to map antipodally invariant distances in the Euclidean space with very little approximation error By doing so, we enable the usage of fast search structures based on Euclidean distances without undermining their speed gains with complex distance transformations. The performance of our method, which we named HHC Regression (HHCR), applied to SuperResolution (SR) improves both in quality (PSNR) and it is faster than any other state-of-the-art method. Additionally, under an application-agnostic interpretation of our regression framework, we also test our algorithm for denoising and depth upscaling with promising results.
Application scenarios and deployment domains

This chapter describes the impact of deploying the format-agnostic media approach, along the lines of three deployment domains and the end user perspective. The chapter elaborates on a series of potential application scenarios. These application scenarios describe features of a potential format-agnostic system from an end user perspective, in order to develop understanding of how technology enables and fits within the overall audio-visual media experience. The chapter explains the implications and benefits of supporting the format-agnostic approach at the presentation stage. It considers the impact of deployment in the user domain by discussing viewers' preferences, both from the perspective of what users appreciate in TV viewing as it is today, and what future TV services should offer to them to be at least comparable with the next generation interactive TV sets.
Accelerating Super-Resolution for 4K upscaling

This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast interpolation methods, we locally estimate the probability of performing a degradation-free upscaling. Our proposed framework explores the usage of supervised machine learning techniques and tackles the problem using binary boosted tree classifiers. The applied upscaler is chosen based on the obtained probabilities: (1) A fast upscaler (e.g. bicubic interpolation) for those regions which are smooth or (2) a linear regression SR algorithm for those which are ill-posed. The proposed strategy accelerates SR by only processing the regions which benefit from it, thus not compromising quality. Furthermore all the algorithms composing the pipeline are naturally parallelizable and further speed-ups could be obtained.
On the use of indexing metadata to improve the efficiency of video compression

For the last few years, video indexing and video compression have been considered as two separate functionalities. However, multimedia content is growing at such a rate that multimedia services will need to consider both the compression and the indexing aspects of the content in order to efficiently manage this audio-video content. Therefore, it is interesting to study the synergy between the representations of compression and indexing and in particular to find new schemas that allow the possibility to exploit indexing/compression information in order to increase the efficiency of video compression/indexing capabilities. The principal contribution of this paper is to study and develop new techniques where the compression efficiency of video codecs can be improved by the use of indexing metadata where indexing metadata refers to information that has been generated to support indexing capabilities.
Long Term Selection of Reference Frame Sub-Blocks using MPEG-7 Indexing Metadata

Traditionally, video indexing and compression have been considered as two separate functionalities. However, the high amount of available multimedia content creates the need for multimedia services to consider both the compression and the indexing aspects of the content in order to efficiently manage it. Therefore, it is interesting to find new techniques that efficiently exploit the indexing/compression information in order to improve the compression/indexing capabilities of the content. This paper focusses on the development of one technique where the compression efficiency of the H.264 encoder is increased by the use of standard indexing information, called indexing metadata. This indexing metadata, even if extracted or generated to support indexing capabilities, can be exploited to enhance current standard video codecs such as H.264.
PSyCo: Manifold Span Reduction for Super Resolution

The main challenge in Super Resolution (SR) is to discover the mapping between the low-and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results. In this paper we present a novel regression-based SR algorithm that benefits from an extended knowledge of the structure of both manifolds. We propose a transform that collapses the 16 variations induced from the dihedral group of transforms (i.e. rotations, vertical and horizontal reflections) and antipodality (i.e. diametrically opposed points in the unitary sphere) into a single primitive. The key idea of our transform is to study the different dihedral elements as a group of symmetries within the high-dimensional manifold. We obtain the respective set of mirror-symmetry axes by means of a frequency analysis of the dihedral elements, and we use them to collapse the redundant variability through a modified symmetry distance. The experimental validation of our algorithm shows the effectiveness of our approach, which obtains competitive quality with a dictionary of as little as 32 atoms (reducing other methods' dictionaries by at least a factor of 32) and further pushing the state-of-the-art with a 1024 atoms dictionary.