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Multimedia Benchmark Workshop, MediaEval 2015

作者： Vlastelica, Marin P. Hayrapetyan, Sergey Tapaswi, Makarand Stiefelhagen, Rainer Computer Vision for Human Computer Interaction Karlsruhe Institute of Technology Germany

We present the approach and results of our system on the MediaEval Affective Impact of Movies Task. The challenge involves two primary tasks: affect classification and violence detection. We test the performance of multiple visual features followed by linear SVM classifiers. Inspired by successes in different vision fields, we use (i) GIST features used in scene modeling, (ii) features extracted from a deep convolutional neural network trained on object recognition, and (iii) improved dense trajectory features encoded using Fisher vectors commonly used in action recognition.

关键词： Object recognition

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Kinect unbiased

Kinect unbiased

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作者： Martinez, Manuel Stiefelhagen, Rainer Computer Vision for Human-Computer Interaction Lab Vincenz-Priessnitz-Str. 3 Karlsruhe76131 Germany

ISBN: (纸本)9781479957514

Since its release, Kinect has been the de facto standard for low-cost RGB-D sensors. An infrared laser ray shot through an holographic diffraction grating projects a fixed dot pattern which is captured using an infrared camera. The pseudorandom pattern ensures that a simple block matching algorithm suffices to provide reliable depth estimates, allowing a cost-effective implementation. In this paper, we analyze the software limitations of Kinect's method, which allows us to propose algorithms that provide better precision. First, we analyze the dot pattern: we measure its pincushion distortion and its effect on the dot density, which is smaller towards the edges of the image. Then, we analyze the behavior of Block Matching algorithms, we show how Kinect's Block Matching implementation is;in general;limited by the dot density of the pattern, and a significant spatial bias is introduced as a result. We propose an efficient approach to estimate the disparity of each dot, allowing us to produce a point cloud with better spatial resolution than Block Matching algorithms. © 2014 IEEE.

关键词： Cost effectiveness

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Exploiting colour information for better scene text recognition 25

Exploiting colour information for better scene text recognit...

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25th British Machine vision Conference, BMVC 2014

作者： Fraz, Muhammad Sarfraz, M. Saquib Edirisinghe, Eran A. Department of Computer Science Loughborough University Loughborough United Kingdom Computer Vision for Human Computer Interaction Lab. Karlsruhe Institute of Technology Karlsruhe Germany

This paper presents an approach to text recognition in natural scene images. The main contribution of this paper is the efficient exploitation of colour information for the identification of text regions in the presence of surrounding noise. We propose a pipeline of image processing operations involving the bilateral regression for the identification of characters in the images. A pre-processing step has been proposed to increase the performance of bilateral regression based character identification. The proposed method segments the characters so well that a combination of an off the shelf feature representation and classification technique achieves state-of-the-art character recognition performance. The capability of the framework is further extended for word recognition where a basic string matching criterion is applied to match recognized strings against a lexicon to eliminate errors that occur due to inaccuracies in character classification. We performed extensive experiments to evaluate our method on challenging datasets (Chars74K, ICDAR03, ICDAR11 and SVT) and results show that the proposed method superseded the existing state-of-the-art techniques. © 2014. The copyright of this document resides with its authors.

关键词： Image processing

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A simple, usable and robust solution for pen-based interaction on projection displays

A simple, usable and robust solution for pen-based interacti...

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1st International Workshop on Pen-Based Learning Technologies, PLT 2007

作者： Iannizzotto, Giancarlo Rosa, Francesco La VisiLab Computer Vision and Human-Computer Interaction Lab. Faculty of Engineering University of Messina C.da Di Dio 98166 Messina Italy

ISBN: (纸本)0769529429

In this paper we describe a pen-based user interface for easy and natural user interaction with a wall projection display. VisualPen is a vision-based system for real-time detection and tracking of a pen that completely replaces mouse and keyboard. The low computational complexity of its algorithms renders it easy to implement with low-cost, lowpower embedded hardware, thus allowing portability and integrability. The architecture of the system is described, and experimental results are presented and commented.

关键词： User interfaces

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Total cluster: A person agnostic clustering method for broadcast videos 14

Total cluster: A person agnostic clustering method for broad...

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9th Indian Conference on computer vision, Graphics and Image Processing, ICVGIP 2014

作者： Tapaswi, Makarand Parkhi, Omkar M. Rahtu, Esa Sommerlade, Eric Stiefelhagen, Rainer Zisserman, Andrew Computer Vision for Human Computer Interaction Karlsruhe Institute of Technology Germany Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom Center for Machine Vision Research University of Oulu Finland

ISBN: (纸本)1595930361

The goal of this paper is unsupervised face clustering in edited video material - where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. This is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people. The novelty we bring is three fold: first, we show that a form of weak supervision is available from the editing structure of the material - the shots, threads and scenes that are standard in edited video;second, we show that by first clustering within scenes the number of face tracks can be significantly reduced with almost no errors;third, we propose an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure. The method is demonstrated on multiple episodes from two very different TV series, Scrubs and Buffy. For both series it is shown that we move towards our goal, and also outperform a number of baselines from previous works. Copyright is held by the authors.

关键词： Cluster analysis

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RealityGit: Cross Reality Version Control of R&D Optical Workbench

RealityGit: Cross Reality Version Control of R&D Optical Wor...

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2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Li, Ke Rolff, Tim Bacher, Reinhard Steinicke, Frank Deutsches Elektronen-Synchrotron Desy Germany Universität Hamburg Human-Computer Interaction Department of Informatics Germany Universität Hamburg Computer Vision Department of Informatics Germany

ISBN: (纸本)9798350328912

Multi-user collaboration at research and development (R&D) workbenches in advanced optics laboratories involves updating and documenting the status of hundreds of components. Although there are abundant mixed reality (MR) systems to support the collaboration of spatially co-located users, limited methods are available to facilitate cooperation in the spatial-temporal domain. Inspired by the version control workflow used in software development, we propose RealityGit, a novel cross-reality (CR) system design that leverages the recent advancements of neural radiance fields (NeRF). We illustrate how the NeRF model can be used to channel users with different extended reality (XR) experiences by providing an accurate immersive visual documentation of the historical states of a complex R&D workbench. We demonstrate the feasibility of such a system through an implementation where users could contribute to the version control workflows by integrating historical NeRF scans into their MR devices or providing status reviews by annotating or editing a NeRF model in virtual reality (VR). © 2023 IEEE.

关键词： Mixed reality

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Manifold Alignment for Person Independent Appearance-Based Gaze Estimation

Manifold Alignment for Person Independent Appearance-Based G...

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International Conference on Pattern Recognition

作者： Timo Schneider Boris Schauerte Rainer Stiefelhagen Computer Vision for Human Computer Interaction Lab Karlsruhe Institute of Technology Germany

We show that dually supervised manifold embedding can improve the performance of machine learning based person-independent and thus calibration-free gaze estimation. For this purpose, we perform a manifold embedding for each person in the training dataset and then learn a linear transformation that aligns the individual, person-dependent manifolds. We evaluate the effect of manifold alignment on the recently presented Columbia dataset, where we analyze the influence on 6 regression methods and 8 feature variants. Using manifold alignment, we are able to improve the person-independent gaze estimation performance by up to 31.2 % compared to the best approach without manifold alignment.

关键词： Manifolds Estimation Training Principal component analysis Vectors Head Discrete cosine transforms

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Semantic Visual Simultaneous Localization and Mapping: A Survey

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7426-7449页

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Zhejiang University of Technology Institute of Computer Vision College of Computer Science and Technology Hangzhou 310023 China Istituto Italiano di Tecnologia Human-Robot Interfaces and Interaction Laboratory Genoa 16163 Italy National University of Defense Technology College of Intelligence Science and Technology Changsha 410073 China Universitã di Genova Ph.D. Program of National Interest in Robotics and Intelligent Machines (DRIM) Genoa 16126 Italy Hangzhou Normal University School of Information Science and Technology Hangzhou 311121 China Technical University of Denmark Department of Technology Management and Economics Kongens Lyngby 2800 Denmark Tianjin University of Technology Institute of Computer Vision School of Computer Science and Engineering Tianjin 300384 China

Visual Simultaneous Localization and Mapping (vSLAM) is a cornerstone technology in computer vision and robotics, underpinning applications such as autonomous vehicles and robot navigation. While traditional vSLAM systems have shown significant progress in indoor or outdoor environments, their performance often degrades in complex scenes, limiting their adaptability and robustness. Semantic vSLAM, which integrates high-level semantic information into vSLAM systems, has emerged as a promising solution to address these limitations by enabling a richer understanding of the environment. In this paper, we provide a comprehensive review of semantic vSLAM, offering a critical analysis of its evolution, methods, and challenges. We begin by revisiting the development of traditional vSLAM, emphasizing its limitations and the motivation for incorporating semantic information. Subsequently, we delve into the core modules of semantic vSLAM, including semantic extraction, object association, semantic loop closing, back-end optimization, and semantic mapping. Then, we present a performance comparison of semantic vSLAM systems under two different datasets, indoor and outdoor, respectively. Furthermore, we also provide a comparative analysis of widely used SLAM datasets to provide guidance for performance testing and validation. To further enrich the discussion, we identify unresolved challenges in semantic vSLAM, such as long-term semantic perception and association, open and unstructured environments. We propose future research directions, including balancing computational resources and quantifying system risk, large model-based navigation and mapping, and embodied AI SLAM. By providing key insights and forward-looking perspectives, this work aims to stimulate future research and improve the capabilities of semantic vSLAM in real-world applications. © 2000-2011 IEEE.

关键词： localization mapping semantic information Semantic visual SLAM SLAM datasets

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A simple, usable and robust solution for pen-based interaction on projection displays

A simple, usable and robust solution for pen-based interacti...

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International Workshop on Pen-Based Learning Technologies, PLT

作者： Giancarlo Iannizzotto Francesco La Rosa VisiLab Computer Vision and Human-Computer Interaction Laboratory Engineering University of Messina Messina Italy

In this paper we describe a pen-based user interface for easy and natural user interaction with a wall projection display. VisualPen is a vision-based system for real-time detection and tracking of a pen that completely replaces mouse and keyboard. The low computational complexity of its algorithms renders it easy to implement with low-cost, low-power embedded hardware, thus allowing portability and integrability. The architecture of the system is described, and experimental results are presented and commented.

关键词： Robustness Displays User interfaces Real time systems Mice Keyboards Computational complexity Rendering (computer graphics) Hardware computer architecture

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Video Face Clustering with Self-Supervised Representation Learning

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 145-157页

作者： Sharma, Vivek Tapaswi, Makarand Saquib Sarfraz, M. Stiefelhagen, Rainer Department of Informatics Institute for Anthropomatics and Robotics Computer Vision for Human-Computer Interaction Lab Karlsruhe Institute of Technology Karlsruhe76131 Germany University of Toronto TorontoONM5S 3G4 Canada Massachusetts Institute of Technology CambridgeMA02139 United States Vector Institute TorontoON Canada Inria Paris75012 France

Characters are a key component of understanding the story conveyed in TV series and movies. With the rise of advanced deep face models, identifying face images may seem like a solved problem. However, as face detectors get better, clustering and identification need to be revisited to address increasing diversity in facial appearance. In this paper, we propose unsupervised methods for feature refinement with application to video face clustering. Our emphasis is on distilling the essential information, identity, from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, that can also be applied to image collections. We evaluate our methods on three video face clustering datasets. Thorough experiments including generalization studies show that our methods outperform current state-of-the-art methods on all datasets. The datasets and code are available at https://***/vivoutlaw/SSIAM. © 2019 IEEE.

关键词： computer vision

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