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Michael Arens - IEEE Xplore Author Profile
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Michael Arens

Also published under: M. Arens

Publications
51
Citations
576
Publications by Year
20082023
Co-Authors:
Show All Co-Authors (232)
Image of author Michael Arens

Michael Arens

Also published under: M. Arens

Affiliation

Fraunhofer IOSB, Germany

Publication Topics

Biography

Michael Arens received the Diploma degree in computer science and the Ph.D. degree (Dr.rer.nat.) from the University of Karlsruhe, in 2001 and 2004, respectively. He is currently the Head of the Department of the Object Recognition (OBJ), Fraunhofer Institute for Optronics, System Technology, and Image Exploitation IOSB.(Based on document published on 3 April 2020).
Publications
51
Citations
576
Publications by Year
20082023
Co-Authors:
Show All Co-Authors (232)
Author's Published Works

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The First Visual Object Tracking Segmentation VOTS2023 Challenge Results

Matej Kristan;Jiří Matas;Martin Danelljan;Michael Felsberg;Hyung Jin Chang;Luka Čehovin Zajc;Alan Lukežič;Ondrej Drbohlav;Zhongqun Zhang;Khanh-Tung Tran;Xuan-Son Vu;Johanna Björklund;Christoph Mayer;Yushan Zhang;Lei Ke;Jie Zhao;Gustavo Fernández;Noor Al-Shakarji;Dong An;Michael Arens;Stefan Becker;Goutam Bhat;Sebastian Bullinger;Antoni B. Chan;Shijie Chang;Hanyuan Chen;Xin Chen;Yan Chen;Zhenyu Chen;Yangming Cheng;Yutao Cui;Chunyuan Deng;Jiahua Dong;Matteo Dunnhofer;Wei Feng;Jianlong Fu;Jie Gao;Ruize Han;Zeqi Hao;Jun-Yan He;Keji He;Zhenyu He;Xiantao Hu;Kaer Huang;Yuqing Huang;Yi Jiang;Ben Kang;Jin-Peng Lan;Hyungjun Lee;Chenyang Li;Jiahao Li;Ning Li;Wangkai Li;Xiaodi Li;Xin Li;Pengyu Liu;Yue Liu;Huchuan Lu;Bin Luo;Ping Luo;Yinchao Ma;Deshui Miao;Christian Micheloni;Kannappan Palaniappan;Hancheol Park;Matthieu Paul;HouWen Peng;Zekun Qian;Gani Rahmon;Norbert Scherer-Negenborn;Pengcheng Shao;Wooksu Shin;Elham Soltani Kazemi;Tianhui Song;Rainer Stiefelhagen;Rui Sun;Chuanming Tang;Zhangyong Tang;Imad Eddine Toubal;Jack Valmadre;Joost van de Weijer;Luc Van Gool;Jash Vira;Stèphane Vujasinović;Cheng Wan;Jia Wan;Dong Wang;Fei Wang;Feifan Wang;He Wang;Limin Wang;Song Wang;Yaowei Wang;Zhepeng Wang;Gangshan Wu;Jiannan Wu;Qiangqiang Wu;Xiaojun Wu;Anqi Xiao;Jinxia Xie;Chenlong Xu;Min Xu;Tianyang Xu;Yuanyou Xu;Bin Yan;Dawei Yang;Ming-Hsuan Yang;Tianyu Yang;Yi Yang;Zongxin Yang;Xuanwu Yin;Fisher Yu;Hongyuan Yu;Qianjin Yu;Weichen Yu;YongSheng Yuan;Zehuan Yuan;Jianlin Zhang;Lu Zhang;Tianzhu Zhang;Guodongfang Zhao;Shaochuan Zhao;Yaozong Zheng;Bineng Zhong;Jiawen Zhu;Xuefeng Zhu;Yueting Zhuang;ChengAo Zong;Kunlong Zuo

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitti...Show More

The First Visual Object Tracking Segmentation VOTS2023 Challenge Results

Matej Kristan;Jiří Matas;Martin Danelljan;Michael Felsberg;Hyung Jin Chang;Luka Čehovin Zajc;Alan Lukežič;Ondrej Drbohlav;Zhongqun Zhang;Khanh-Tung Tran;Xuan-Son Vu;Johanna Björklund;Christoph Mayer;Yushan Zhang;Lei Ke;Jie Zhao;Gustavo Fernández;Noor Al-Shakarji;Dong An;Michael Arens;Stefan Becker;Goutam Bhat;Sebastian Bullinger;Antoni B. Chan;Shijie Chang;Hanyuan Chen;Xin Chen;Yan Chen;Zhenyu Chen;Yangming Cheng;Yutao Cui;Chunyuan Deng;Jiahua Dong;Matteo Dunnhofer;Wei Feng;Jianlong Fu;Jie Gao;Ruize Han;Zeqi Hao;Jun-Yan He;Keji He;Zhenyu He;Xiantao Hu;Kaer Huang;Yuqing Huang;Yi Jiang;Ben Kang;Jin-Peng Lan;Hyungjun Lee;Chenyang Li;Jiahao Li;Ning Li;Wangkai Li;Xiaodi Li;Xin Li;Pengyu Liu;Yue Liu;Huchuan Lu;Bin Luo;Ping Luo;Yinchao Ma;Deshui Miao;Christian Micheloni;Kannappan Palaniappan;Hancheol Park;Matthieu Paul;HouWen Peng;Zekun Qian;Gani Rahmon;Norbert Scherer-Negenborn;Pengcheng Shao;Wooksu Shin;Elham Soltani Kazemi;Tianhui Song;Rainer Stiefelhagen;Rui Sun;Chuanming Tang;Zhangyong Tang;Imad Eddine Toubal;Jack Valmadre;Joost van de Weijer;Luc Van Gool;Jash Vira;Stèphane Vujasinović;Cheng Wan;Jia Wan;Dong Wang;Fei Wang;Feifan Wang;He Wang;Limin Wang;Song Wang;Yaowei Wang;Zhepeng Wang;Gangshan Wu;Jiannan Wu;Qiangqiang Wu;Xiaojun Wu;Anqi Xiao;Jinxia Xie;Chenlong Xu;Min Xu;Tianyang Xu;Yuanyou Xu;Bin Yan;Dawei Yang;Ming-Hsuan Yang;Tianyu Yang;Yi Yang;Zongxin Yang;Xuanwu Yin;Fisher Yu;Hongyuan Yu;Qianjin Yu;Weichen Yu;YongSheng Yuan;Zehuan Yuan;Jianlin Zhang;Lu Zhang;Tianzhu Zhang;Guodongfang Zhao;Shaochuan Zhao;Yaozong Zheng;Bineng Zhong;Jiawen Zhu;Xuefeng Zhu;Yueting Zhuang;ChengAo Zong;Kunlong Zuo

Year: 2023 | Conference Paper |

Uncertainty-Aware Vision-Based Metric Cross-View Geolocalization

Florian Fervers;Sebastian Bullinger;Christoph Bodensteiner;Michael Arens;Rainer Stiefelhagen

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (17)
This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous driving, i.e. using expensive high-defi...Show More

Uncertainty-Aware Vision-Based Metric Cross-View Geolocalization

Florian Fervers;Sebastian Bullinger;Christoph Bodensteiner;Michael Arens;Rainer Stiefelhagen

Year: 2023 | Conference Paper |

Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors

Florian Fervers;Sebastian Bullinger;Christoph Bodensteiner;Michael Arens;Rainer Stiefelhagen

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2022 | Conference Paper |
Cited by: Papers (12)
This paper proposes a novel method for geo-tracking, i.e. continuous metric self-localization in outdoor environments by registering a vehicle's sensor information with aerial imagery of an unseen target region. Geo- tracking methods offer the potential to supplant noisy signals from global navigation satellite systems (GNSS) and expensive and hard to maintain prior maps that are typically used fo...Show More

Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors

Florian Fervers;Sebastian Bullinger;Christoph Bodensteiner;Michael Arens;Rainer Stiefelhagen

Year: 2022 | Conference Paper |

Deep Saliency Map Generators for Multispectral Video Classification

Jens Bayer;David Münch;Michael Arens

2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Despite their black box nature, deep neural networks have been successfully used in practical applications lately. In areas where the results of these applications can lead to safety hazards or decisions of ethical relevance, the application provider is accountable for the resulting decisions and should therefore be able to explain, how, and why a specific decision was made. For image processing n...Show More

Deep Saliency Map Generators for Multispectral Video Classification

Jens Bayer;David Münch;Michael Arens

Year: 2022 | Conference Paper |

Revisiting Click-Based Interactive Video Object Segmentation

Stéphane Vujasinović;Sebastian Bullinger;Stefan Becker;Norbert Scherer-Negenborn;Michael Arens;Rainer Stiefelhagen

2022 IEEE International Conference on Image Processing (ICIP)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
While current methods for interactive Video Object Segmentation (iVOS) rely on scribble-based interactions to generate precise object masks, we propose a Click-based interactive Video Object Segmentation (CiVOS) framework to simplify the required user workload as much as possible. CiVOS builds on de-coupled modules reflecting user interaction and mask propagation. The interaction module converts c...Show More

Revisiting Click-Based Interactive Video Object Segmentation

Stéphane Vujasinović;Sebastian Bullinger;Stefan Becker;Norbert Scherer-Negenborn;Michael Arens;Rainer Stiefelhagen

Year: 2022 | Conference Paper |

Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

Ronny Hug;Stefan Becker;Wolfgang Hübner;Michael Arens

IEEE Access
Year: 2021 | Volume: 9 | Journal Article |
Cited by: Papers (3)
Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype...Show More

Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

Ronny Hug;Stefan Becker;Wolfgang Hübner;Michael Arens

Year: 2021 | Volume: 9 | Journal Article |

DecaWave Ultra-Wideband Warm-Up Error Correction

Juri Sidorenko;Volker Schatz;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

IEEE Transactions on Aerospace and Electronic Systems
Year: 2021 | Volume: 57, Issue: 1 | Journal Article |
Cited by: Papers (11)
In the field of indoor localization, ultra-wideband (UWB) technology is no longer dispensable. The market demands that the UWB hardware has to be cheap, precise, and accurate. These requirements lead to the popularity of the DecaWave UWB system. The great majority of the publications about this system deal with the correction of the signal power, hardware delay, or clock drift. It has traditionall...Show More

DecaWave Ultra-Wideband Warm-Up Error Correction

Juri Sidorenko;Volker Schatz;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

Year: 2021 | Volume: 57, Issue: 1 | Journal Article |

Error Corrections for Ultrawideband Ranging

Juri Sidorenko;Volker Schatz;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

IEEE Transactions on Instrumentation and Measurement
Year: 2020 | Volume: 69, Issue: 11 | Journal Article |
Cited by: Papers (33)
Precise indoor localization is a major challenge in the field of localization. In this article, we investigate multiple error corrections for the ultrawideband (UWB) technology, in particular the DecaWave DW1000 transceiver. Both the time-of-arrival (TOA) and the time-difference-of-arrival (TDOA) methods are considered. Various clock-drift correction methods for TOA from the literature are reviewe...Show More

Error Corrections for Ultrawideband Ranging

Juri Sidorenko;Volker Schatz;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

Year: 2020 | Volume: 69, Issue: 11 | Journal Article |

Context Sensitivity of Spatio-Temporal Activity Detection using Hierarchical Deep Neural Networks in Extended Videos

Felix Hertlein;David Münch;Michael Arens

2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)
Year: 2020 | Conference Paper |
The amount of available surveillance video data is increasing rapidly and therefore makes manual inspection impractical. The goal of activity detection is to automatically localize activities spatially and temporally in a large collection of video data. In this work we will answer the question to what extent context plays a role in spatio-temporal activity detection in extended videos. Towards thi...Show More

Context Sensitivity of Spatio-Temporal Activity Detection using Hierarchical Deep Neural Networks in Extended Videos

Felix Hertlein;David Münch;Michael Arens

Year: 2020 | Conference Paper |

Self-Calibration for the Time-of-Arrival Positioning

Juri Sidorenko;Volker Schatz;Dimitri Bulatov;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Self-calibration of time-of-arrival positioning systems is made difficult by the non-linearity of the relevant set of equations. This work applies dimension lifting to this problem. The objective function is extended by an additional dimension to allow the dynamics of the optimization to avoid local minima. Next to the usual numerical optimization, a partially analytical method is suggested, which...Show More

Self-Calibration for the Time-of-Arrival Positioning

Juri Sidorenko;Volker Schatz;Dimitri Bulatov;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

Year: 2020 | Volume: 8 | Journal Article |

Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences

Nikolas Hesse;Sergi Pujades;Michael J. Black;Michael Arens;Ulrich G. Hofmann;A. Sebastian Schroeder

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2020 | Volume: 42, Issue: 10 | Journal Article |
Cited by: Papers (43)
Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, calibration procedures, and is limited to cooperative subjects who can understand and follow instructions, such as adults. We present a method for learn...Show More

Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences

Nikolas Hesse;Sergi Pujades;Michael J. Black;Michael Arens;Ulrich G. Hofmann;A. Sebastian Schroeder

Year: 2020 | Volume: 42, Issue: 10 | Journal Article |

Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals

Patrick Schlosser;David Münch;Michael Arens

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
In this paper, several variants of two-stream architectures for temporal action proposal generation in long, untrimmed videos are presented. Inspired by the recent advances in the field of human action recognition utilizing 3D convolutions in combination with two-stream networks and based on the Single-Stream Temporal Action Proposals (SST) architecture, four different two-stream architectures uti...Show More

Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals

Patrick Schlosser;David Münch;Michael Arens

Year: 2019 | Conference Paper |

3D Object Trajectory Reconstruction using Instance-Aware Multibody Structure from Motion and Stereo Sequence Constraints

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens;Rainer Stiefelhagen

2019 IEEE Intelligent Vehicles Symposium (IV)
Year: 2019 | Conference Paper |
Three-dimensional environment perception is a key element of autonomous driving and driver assistance systems. A common image based approach to determine three-dimensional scene information is stereo matching, which is limited by the stereo camera baseline. In contrast to stereo matching based methods, we present an approach to reconstruct three-dimensional object trajectories combining temporal a...Show More

3D Object Trajectory Reconstruction using Instance-Aware Multibody Structure from Motion and Stereo Sequence Constraints

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens;Rainer Stiefelhagen

Year: 2019 | Conference Paper |

3D Object Trajectory Reconstruction using Stereo Matching and Instance Flow based Multiple Object Tracking

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens

2019 16th International Conference on Machine Vision Applications (MVA)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
This paper presents a method to reconstruct three-dimensional object motion trajectories in stereo video sequences. We apply stereo matching to each image pair of a stereo sequence to compute corresponding binocular disparities. By combining instance-aware semantic segmentation techniques and optical flow cues, we track two-dimensional object shapes on pixel level. This allows us to determine for ...Show More

3D Object Trajectory Reconstruction using Stereo Matching and Instance Flow based Multiple Object Tracking

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens

Year: 2019 | Conference Paper |

Multispectral Matching using Conditional Generative Appearance Modeling

Christoph Bodensteiner;Sebastian Bullinger;Michael Arens

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
The precise determination of correspondences between pairs of images is still a fundamental building block of many computer vision systems. Despite the maturity of modern feature matchers, multispectral methods are still lacking robustness and speed. We focus on the problem of finding point correspondences in a multispectral imaging setup. Most methods aim at invariant feature transforms (e.g. mul...Show More

Multispectral Matching using Conditional Generative Appearance Modeling

Christoph Bodensteiner;Sebastian Bullinger;Michael Arens

Year: 2018 | Conference Paper |

Joint Detection and Online Multi-object Tracking

Hilke Kieritz;Wolfgang Hübner;Michael Arens

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (26)
Most multiple object tracking methods rely on object detection methods in order to initialize new tracks and to update existing tracks. Although strongly interconnected, tracking and detection are usually addressed as separate building blocks. However both parts can benefit from each other, e.g. the affinity model from the tracking method can reuse appearance features already calculated by the det...Show More

Joint Detection and Online Multi-object Tracking

Hilke Kieritz;Wolfgang Hübner;Michael Arens

Year: 2018 | Conference Paper |

Particle-based Pedestrian Path Prediction using LSTM-MDL Models

Ronny Hug;Stefan Becker;Wolfgang Hübner;Michael Arens

2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Year: 2018 | Conference Paper |
Cited by: Papers (25)
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a probability density function over the state space. Therefore, recurrent models are a natural choice to address path prediction tasks, where a trained model is used to generate future expectations from past observations. When applied to security applications, like predicting pedestrian path...Show More

Particle-based Pedestrian Path Prediction using LSTM-MDL Models

Ronny Hug;Stefan Becker;Wolfgang Hübner;Michael Arens

Year: 2018 | Conference Paper |

Monocular 3D Vehicle Trajectory Reconstruction Using Terrain Shape Constraints

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens

2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
This work proposes a novel approach to reconstruct three-dimensional vehicle trajectories in monocular video sequences. We leverage state-of-the-art instance-aware semantic segmentation and optical flow methods to compute object video tracks on pixel level. This approach uses Structure from Motion to determine camera poses relative to vehicle instances and environment structures. We parameterize v...Show More

Monocular 3D Vehicle Trajectory Reconstruction Using Terrain Shape Constraints

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens

Year: 2018 | Conference Paper |

Improved Time of Arrival Measurement Model for Non-Convex Optimization with Noisy Data

Juri Sidorenko;Volker Schatz;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
The quadratic system provided by the Time of Arrival technique can be solved analytical or by non-linear least squares minimization. In real environments the measurements are always corrupted by noise. This measurement noise effects the analytical solution more than non-linear optimization algorithms. On the other hand it is also true that local optimization tends to find the local minimum, instea...Show More

Improved Time of Arrival Measurement Model for Non-Convex Optimization with Noisy Data

Juri Sidorenko;Volker Schatz;Norbert Scherer-Negenborn;Michael Arens;Urs Hugentobler

Year: 2018 | Conference Paper |

Comparing Visual Tracker Fusion on Thermal Image Sequences

Sebastian Thome;Norbert Scherer-Negenborn;Michael Arens

2018 21st International Conference on Information Fusion (FUSION)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Visual object tracking is a challenging task in computer vision, especially if there are no constraints to the scenario and the objects are arbitrary. The number of tracking algorithms is very large and all have diverse advantages and disadvantages. Normally they show various behaviour and their failures in the tracking process occur at different moments in the sequence. So far, there is no tracke...Show More

Comparing Visual Tracker Fusion on Thermal Image Sequences

Sebastian Thome;Norbert Scherer-Negenborn;Michael Arens

Year: 2018 | Conference Paper |

Instance flow based online multiple object tracking

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens

2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Cited by: Papers (10)
We present a method to perform online Multiple Object Tracking (MOT) of known object categories in monocular video data. Current Tracking-by-Detection MOT approaches build on top of 2D bounding box detections. In contrast, we exploit state-of-the-art instance aware semantic segmentation techniques to compute 2D shape representations of target objects in each frame. We predict position and shape of...Show More

Instance flow based online multiple object tracking

Sebastian Bullinger;Christoph Bodensteiner;Michael Arens

Year: 2017 | Conference Paper |

Interactive concepts for shaping generative models of spatial behavior

Ronny Hug;Wolfgang Hübner;Michael Arens

2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Year: 2017 | Conference Paper |
A technique widely used in video based situation assessment, and especially in anomaly detection, is the analysis of spatial behavior in terms of motion profiles recorded along trajectories. An intuitive assessment metric is the deviation from normal behavior, where generative models are a natural choice for capturing the underlying statistics. Applying such outlier methods in open world scenarios...Show More

Interactive concepts for shaping generative models of spatial behavior

Ronny Hug;Wolfgang Hübner;Michael Arens

Year: 2017 | Conference Paper |

Hierarchical Grouping Using Gestalt Assessments

Eckart Michaelsen;Michael Arens

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Year: 2017 | Conference Paper |
Cited by: Papers (9)
Real images contain symmetric Gestalten with high probability. I.e. certain parts can be mapped on other certain parts by the usual Gestalt laws and are repeated there with high similarity. Moreover, such mapping comes in nested hierarchies - e.g. a reflection Gestalt that is made of repetition friezes, whose parts are again reflection symmetric compositions. This can be explicitly modelled by con...Show More

Hierarchical Grouping Using Gestalt Assessments

Eckart Michaelsen;Michael Arens

Year: 2017 | Conference Paper |

Hierarchical Grouping — The Gestalt Assessments Method

Eckart Michaelsen;Michael Arens

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Year: 2017 | Conference Paper |
Real images contain reflection symmetry and repetition in rows with high probability. I.e. certain parts can be mapped on other certain parts by the usual Gestalt laws and are repeated there with high similarity. Moreover, such mapping comes in nested hierarchies - e.g. a reflection Gestalt that is made of repetition friezes, whose parts are again reflection symmetric compositions. It is our inten...Show More

Hierarchical Grouping — The Gestalt Assessments Method

Eckart Michaelsen;Michael Arens

Year: 2017 | Conference Paper |

Body pose estimation in depth images for infant motion analysis

Nikolas Hesse;A. Sebastian Schröder;Wolfgang Müller-Felber;Christoph Bodensteiner;Michael Arens;Ulrich G. Hofmann

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2017 | Conference Paper |
Cited by: Papers (20)
Motion analysis of infants is used for early detection of movement disorders like cerebral palsy. For the development of automated methods, capturing the infant's pose accurately is crucial. Our system for predicting 3D joint positions is based on a recently introduced pixelwise body part classifier using random ferns, to which we propose multiple enhancements. We apply a feature selection step be...Show More

Body pose estimation in depth images for infant motion analysis

Nikolas Hesse;A. Sebastian Schröder;Wolfgang Müller-Felber;Christoph Bodensteiner;Michael Arens;Ulrich G. Hofmann

Year: 2017 | Conference Paper |

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