Publications

Complete list of my research publications

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Journal Articles

2024

1. BladeView: Towards Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle

   Yang, Cong , Zhou, Hua , Liu, Xun , Ke, Yan , Gao, Bo , Grzegorzek, Marcin , Boukhers, Zeyd , Chen, Tao , See, John

   IEEE Transactions on Automation Science and Engineering 22 7530-7545 2024

   Link DOI

2. A non-enhanced CT based deep learning diagnostic system for high-risk COVID-19 infection in lung cancer patients

   Du, Tianming , Sun, Yihao , Wang, Xinghao , Jiang, Tao , Xu, Ning , Boukhers, Zeyd , Grzegorzek, Marcin , Sun, Hongzan , Li, Chen

   Frontiers in Medicine 11 1444708 2024

   Link DOI

   Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

3. Context-agnostic data visitation to exchange health data for scientific research: the example of PADME PHT

   Hallinan, Dara , Nestor, Marc , Akhtar, Muhammad Hamza , Breß, Constantin , Jaberansary, Mehrshad , Lange, Christoph , Maia, Macedo , Molnár-Gábor, Fruzsina , Yediel, Yeliz Ucer , Boukhers, Zeyd

   International Data Privacy Law 2024

   Link DOI

4. R4-SLAM: Toward Real-time, Robust, and Resource-Restricted Visual SLAM in Dynamic Environments

   Zhang, Zhijie , Yang, Cong , Qin, Xue , Sui, Wei , Boukhers, Zeyd , Kong, Hui , Liu, Jie , Li, Zhijun

   IEEE Transactions on Instrumentation and Measurement 2024

2023

1. Skeleton ground truth extraction: methodology, annotation tool and benchmarks

   Yang, Cong , Indurkhya, Bipin , See, John , Gao, Bo , Ke, Yan , Boukhers, Zeyd , Yang, Zhenyu , Grzegorzek, Marcin

   International Journal of Computer Vision 132 1219-1241 2023

   Link DOI

   Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN), but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target’s context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.

2. Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text

   Boukhers, Zeyd , Goswami, Prantik , Jürjens, Jan

   Neural Computing and Applications 2023

   Link DOI

   One challenge often encountered when using Deep Neural Network models for automatic ICD coding is their potential inability to effectively handle unseen clinical texts, especially when these models are only trained on a limited number of examples. This is because these models rely solely on the patterns and relationships present in the training data, and may not be able to effectively incorporate additional knowledge about the relationships between medical entities. To address this issue, we introduce KG-MultiResCNN - Knowledge Guided Multi-filter Residual Convolutional Neural Network model, which combines training examples with external knowledge from the Wikidata Knowledge Graph (KG) in order to better capture the relationships between medical entities. The KG is a structured database that contains a wealth of information about various entities, including medical concepts and their relationships with one another. By incorporating this external knowledge into our model, we are able to improve its ability to predict ICD codes for new clinical texts. In our experiments with the MIMIC-III dataset, we found that the KG-MultiResCNN model significantly outperformed the baseline approaches. This demonstrates the effectiveness of using external knowledge, in addition to training examples, to improve the performance of deep learning models for automatic ICD coding.

3. Deep author name disambiguation using DBLP data

   Boukhers, Zeyd , Asundi, Nagaraj Bahubali

   International Journal on Digital Libraries 25 431-441 2023

   Link DOI

   In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.

2022

1. COIN: Counterfactual Image Generation for VQA Interpretation

   Boukhers, Zeyd , Hartmann, Timo , Jürjens, Jan

   Sensors 22 2245 2022

   Link DOI

   Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models’ behaviour.

2020

1. Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy

   Koltsov, Sergei , Ignatenko, Vera , Boukhers, Zeyd , Staab, Steffen

   Entropy 22 394 2020

   Link DOI

   Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)—we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.

2019

1. Fractal approach for determining the optimal number of topics in the field of topic modeling

   Koltsov, Sergei , Ignatenko, Vera , Boukhers, Zeyd , Staab, Steffen

   Journal of Physics: Conference Series 1163 012025 2019

   Link DOI

2017

1. Example-based 3D trajectories of objects from 2D videos

   Boukhers, Zeyd , Shirahama, Kimiaki , Grzegorzek, Marcin

   IEEE Transactions on Circuits and Systems for Video Technology 28 2246-2260 2017

   Link DOI

2. Less Restrictive Camera Odometry from Monocular Camera

   Boukhers, Zeyd , Shirahama, Kimiaki , Grzegorzek, Marcin

   Multimedia Tools and Applications 77 16199-16222 2017

   Link DOI

Conference Papers

2026

1. Unleashing Semantic and Geometric Priors for 3D Scene Completion

   Chen, Shiyuan , Sui, Wei , Zhang, Bohao , Boukhers, Zeyd , See, John , Yang, Cong

   Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence 2026

2025

1. ELMTEX: Fine-tuning large language models for structured clinical information extraction. A case study on clinical reports

   Guluzade, Aynur , Heiba, Naguib , Boukhers, Zeyd , Hamiti, Florim , Polash, Jahid Hasan , Mohamad, Yehya , Velasco, Carlos A

   International Conference on Artificial Intelligence in Medicine (AIME 2025) 181-185 2025

   Link DOI

2. MESD: Metadata Extraction from Scholarly Documents – A Shared Task Overview

   Boukhers, Zeyd , Yang, Cong

   ESWC International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs (NSLP 2025) 2025

3. Future-Proofing Heritage with ARGUS: A Multimodal Digital Twin Approach for Sustainable Preservation

   Pavlidis, George , Koutsoudis, Anestis , Tsiafaki, Despoina , Sevetlidis, Vasileios , Arampatzakis, Vasileios , Sarris, Apostolos , Polidorou, Miltiadis , Klinkenberg, Victor , Boukhers, Zeyd , Kong, Lingxiao , Farinetti, Emeri , Moreno-Navarro, Fernando , Kakogiannos, Ioannis , Aparicio, Sofia , Ortega, Javier , Ramonet, Fernando , Agapiou, Athos , Hadjipetrou, Stylianos , Michaelides, Kyriakos , Patsalidis, Stavros , Kyriakidis, Phaedon , Papakonstantinou, Apostolos , Athanasoulis, Demetrios , Maris, Christos

   International Conference on Computer Applications & Quantitative Methods in Archaeology 2025: "Digital Horizons" 2025

   Link DOI

4. Q-Chain: A Causal-Aware Framework for Structural and Educational Question Generation

   Xu, Junqi , Wang, Lvcheng , Boukhers, Zeyd , Indurkhya, Bipin , Yang, Cong

   The 15th ACM International Conference on Multimedia Retrieval 2083-2087 2025

   Link DOI

5. A unified framework for cultural heritage data historicity and migration: The ARGUS approach

   Kong, Lingxiao , Sarris, Apostolos , Polidorou, Miltiadis , Klingenberg, Victor , Sevetlidis, Vasilis , Arampatzakis, Vasilis , Pavlidis, George , Yang, Cong , Boukhers, Zeyd

   IEEE International Conference on Cyber Humanities 2025

6. Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper

   Ramadan, Qusai , Ruohonen, Jukka , Tiwari, Abhishek , Alami, Adam , Boukhers, Zeyd

   2025 IEEE 33rd International Requirements Engineering Conference Workshops 405-411 2025

   Link DOI

7. EMORL: Ensemble multi-objective reinforcement learning for efficient and flexible LLM fine-tuning

   Kong, Lingxiao , Yang, Cong , Neufang, Susanne , Beyan, Oya Deniz , Boukhers, Zeyd

   26th Meeting of the Special Interest Group on Discourse and Dialogue 2025

8. Exploring the limits of model compression in LLMs: a knowledge distillation study on QA tasks

   Datta, Joyeeta , Doll, Niclas , Ramadan, Qusai , Boukhers, Zeyd

   26th Meeting of the Special Interest Group on Discourse and Dialogue 2025

9. FAIR Data Assessment Using LLMs: The Fair-Way

   Sharma, Anmol , Sowe, Sulayman K. , Kim, Soo-Yon , Hoseini, Sayed , Limani, Fidan , Boukhers, Zeyd , Lange, Christoph , Decker, Stefan

   The Conference on Information and Knowledge Management (CIKM) 5228-5232 2025

   Link DOI

10. A survey on metadata for Machine Learning models and datasets: standards, practices, and harmonization challenges

    Genet, Asefa , Zongxiong, Chen , Zoubia, Oussama , Limani, Fidan , Silva, Kanishka , Suryani, Muhammad Asif , Zapilko, Benjamin , Castro, Leyla Jael , Kutafina, Ekaterina , Solanki, Dhwani , Fliegl, Heike , Schimmler, Sonja , Boukhers, Zeyd , Sack, Harald

    Proceedings of the Sci-K Workshop at the International Semantic Web Conference (ISWC 2025) 2025

11. Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective

    Kong, Lingxiao , Yang, Cong , Beyan, Oya , Boukhers, Zeyd

    Proceedings of the Multi-Objective Decision Making Workshop at ECAI 2025 2025

2024

1. YawnNet: A Visual-Centric Approach for Yawning Detection

   Sun, Ruoxi , Yang, Xinyu , Qian, Cong , Zhu, Chenyu , Sui, Wei , Boukhers, Zeyd , Yang, Cong

   Proceedings of the 2024 International Conference on Multimedia Retrieval 1140–1144 2024

   Link DOI

2. Falcon 7b for software mention detection in scholarly documents

   Khan, Ameerali , Ramadan, Qusai , Yang, Cong , Boukhers, Zeyd

   ESWC Workshop on Natural Scientific Language Processing and Research Knowledge Graphs 1-9 2024

   Link DOI

   This paper aims to tackle the challenge posed by the increasing integration of software tools in research across various disciplines by investigating the application of Falcon-7b for the detection and classification of software mentions within scholarly texts. Specifically, the study focuses on solving Subtask I of the Software Mention Detection in Scholarly Publications (SOMD), which entails identifying and categorizing software mentions from academic literature. Through comprehensive experimentation, the paper explores different training strategies, including a dual-classifier approach, adaptive sampling, and weighted loss scaling, to enhance detection accuracy while overcoming the complexities of class imbalance and the nuanced syntax of scholarly writing. The findings highlight the benefits of selective labelling and adaptive sampling in improving the model’s performance. However, they also indicate that integrating multiple strategies does not necessarily result in cumulative improvements. This research offers insights into the effective application of large language models for specific tasks such as SOMD, underlining the importance of tailored approaches to address the unique challenges presented by academic text analysis.

3. Moiré pattern detection: Stability and efficiency with evaluated loss function

   Li, Zhuocheng , Shen, Xizhu , Luan, Simin , Guo, Shuwei , Boukhers, Zeyd , Wang, Yuyi , Yang, Cong

   International Conference on Pattern Recognition 1-16 2024

   Link DOI

4. Transition in focus of prediction tasks for skeleton graph component detection with Transformer

   Wang, Zhiyuan , Yang, Cong , Zhang, Yulu , Boukhers, Zeyd , Sui, Wei , Ji, Yi , Liu, Chunping

   ACM Multimedia Asia 1-7 2024

   Link DOI

5. Large language model in medical informatics: direct classification and enhanced text representations for automatic ICD coding

   Boukhers, Zeyd , Khan, AmeerAli , Ramadan, Qusai , Yang, Cong

   IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 1-4 2024

   Link DOI

6. From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors

   Glombiewski, Nikolaus , Boukhers, Zeyd , Beilschmidt, Christian , Drönner, Johannes , Mattig, Michael , Piet, Artur , Pietrzynski, Robert , Jaberansary, Mehrshad , Maia, Macedo , Beyvers, Sebastian , Yediel, Yeliz Üçer , Akhtar, Muhammad Hamza , Oberkampf, Heiner , Hartman, Jonathan , Lange, Christoph

   The 40th ACM/SIGAPP Symposium On Applied Computing (SAC) 1-4 2024

   Link DOI

7. FDO manager: minimum viable FAIR digital object implementation

   Zoubia, Oussama , Asundi, Nagaraj Bahubali , Dogan, Sezin , Koumpis, Adamantios , Lange, Christoph , Beyan, Oya , Boukhers, Zeyd

   FDO Summit Berlin 2024 Proceedings 5 1-2 2024

   Link DOI

   In the digital age, data has emerged as one of the most valuable assets across various sectors, including academia, industry, and healthcare. Effective data preservation involves the management of data to ensure its long-term accessibility and usability. Given the importance and sensitivity of data, the need for effective management is a crucial necessity. One of the big recent proposed approaches for data management is the FAIR Digital Objects (FDOs) which has emerged to revolutionize the field of data management and preservation. Central to this revolution is the alignment of FDOs with the FAIR principles (Findable, Accessible, Interoperable, Reusable), particularly emphasizing machine-actionability and interoperability across diverse data ecosystems. This paper presents "FDO Manager," a Minimum Viable Implementation of FDOs, tailored specifically for the use case and field of research artefacts such as datasets, publications, and code. The paper discusses the core ideas behind the FDO Manager, its architecture, usage and implementation details, as well as its potential impact, demonstrating a simple and abstract implementation of FDOs in the research realm.

2023

1. Data trading and monetization: challenges and open research directions

   Ramadan, Qusai , Boukhers, Zeyd , AlShaikh, Muath , Lange, Christoph , Jürjens, Jan

   The 7th International Conference on Future Networks and Distributed Systems 344-351 2023

   Link DOI

2. NFDI4DS Infrastructure and Services

   Schimmler, Sonja , Wentzel, Bianca , Bleier, Arnim , Dietze, Stefan , Karmakar, Saurav , Mutschke, Peter , Kraft, Angelie , Taffa, Tilahun A , Usbeck, Ricardo , Boukhers, Zeyd , Auer, Sören , Castro, Leyla J , Ackermann, Marcel R , Neumuth, Thomas , Schneider, Daniel , Abedjan, Ziawasch , Latif, Atif , Limani, Fidan , Ahmad, Raia Abu , Rehm, Georg , Khorasani, Sima Attar , Lieber, Matthias

   INFORMATIK 2023 - Designing Futures: Zukünfte gestalten 2023

   Link

3. Research knowledge graphs in NFDI4DS

   Karmakar, Saurav , Zloch, Matthäus , Limani, Fidan , Zapilko, Benjamin , Upadhyaya, Sharmila , D’Souza, Jennifer , Castro, Leyla J , Rehm, Georg , Ackermann, Marcel R , Sack, Harald , Boukhers, Zeyd , Schimmler, Sonja , Dessí, Danilo , Mutschke, Peter , Dietze, Stefan

   INFORMATIK 2023 - Designing Futures: Zukünfte gestalten 2023

   Link

4. FAIR Data Spaces - Building a Common Cloud-Based Data Space for Industry and Science by Linking Gaia-X and NFDI

   Boukhers, Zeyd , Mockler, Daniela

   1st Conference on Research Data Infrastructure (CoRDI) - Connecting Communities 2023

   Link DOI

5. Beyond trading data: the hidden influence of public awareness and interest on cryptocurrency volatility

   Boukhers, Zeyd , Bouabdellah, Azeddine , Yang, Cong , Jürjens, Jan

   32nd ACM International Conference on Information and Knowledge Management (CIKM) 142-151 2023

   Link DOI

6. Enhancing Reproducibility in Research through FAIR Digital Objects

   Boukhers, Zeyd , Castro, Leyla Jael Garcia

   1st Conference on Research Data Infrastructure 1 2023

   Link DOI

   The FAIR principles were introduced to enhance data reuse by providing guidelines for effective data management practices. In the broader context of research, assets encompass not only data but also artifacts such as code, software, and publications. FAIRifying these artifacts is as essential as FAIRifying data, given the increasing complexity of current AI approaches that make reproducibility extremely challenging. Therefore, the reuse of these artifacts is growing in importance. The concept of FAIR Digital Objects (FDOs) presents a solution to FAIRify these artifacts, treating them as FDOs. NFDI4DataScience is embracing FDOs and proposing an architecture to efficiently manage them.

7. Pedestrian collision prediction using a monocular camera

   Chen, Shiyuan , Qin, Xue , Boukhers, Zeyd , See, John , Sui, Wei , Yang, Cong

   8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence 1-8 2023

   Link DOI

8. PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences

   Boukhers, Zeyd , Bleier, Arnim , Yediel, Yeliz Ucer , Hienstorfer-Heitmann, Mio , Jaberansary, Mehrshad , Koumpis, Adamantios , Beyan, Oya

   ACM/IEEE JCDL 2023 - Joint Conference on Digital Libraries 251-252 2023

   Link DOI

9. Enhancing data space semantic interoperability through Machine Learning: a visionary perspective

   Boukhers, Zeyd , Beyan, Oya , Lange, Christoph

   Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion) 1462-1467 2023

   Link DOI

2022

1. Evaluation scheme of FAIRness in scholarly data

   Boukhers, Zeyd

   Workshop on Open Citations and Open Scholarly Metadata 2022

   Link

2. NFDI4DataScience Registry for Reproducible Data Science and Artificial Intelligence

   Rebholz-Schuhmann, Dietrich , Castro, Leyla Jael , Boukhers, Zeyd , Giraldo, Olga , Koumpis, Adamantios , Beyan, Oya

   Workshop on Open Citations and Open Scholarly Metadata 2022

   Link

3. Whois? Deep author name disambiguation using bibliographic data

   Boukhers, Zeyd , Bahubali, Nagaraj

   The 26th International Conference on Theory and Practice of Digital Libraries 201-215 2022

   Link DOI

4. Vision and Natural Language for Metadata Extraction from PDF Documents: A Multimodal Approach

   Boukhers, Zeyd , Bouabdallah, Azeddine

   ACM/IEEE Joint Conference on Digital Library 1-5 2022

   Link DOI

2021

1. MexPub: Deep transfer learning for metadata extraction from German publications

   Boukhers, Zeyd , Goswami, Prantik , Beili, Nada , Zafar, Muhammad Arslan , Hartmann, Timo

   ACM/IEEE Joint Conference on Digital Library 1-8 2021

   Link DOI

2. LaHAR: Latent Human Activity Recognition using LDA

   Boukhers, Zeyd , Wete, Danniene , Staab, Steffen

   The AAAI 2021 Workshop on Plan, Activity, and Intent Recognition 1-8 2021

3. Early detection of COVID lies without evidences and propagation information

   Baris, Ipek , Boukhers, Zeyd

   CONSTRAINT @ AAAI2021 1-8 2021

4. Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data

   Boukhers, Zeyd , Bahubali, Nagaraj , Chandrasekaran, Abinaya Thulsi , Anand, Adarsh , Prasad, Soniya Manchenahalli Gnanendra , Aralappa, Sriram

   BiblioDAP workshop @ SIGKDD2021 1-9 2021

5. BiblioDAP’21: The 1st workshop on bibliographic data analysis and processing

   Boukhers, Zeyd , Mayr, Philipp , Peroni, Silvio

   Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 4110–4111 2021

   Link DOI

2019

1. An end-to-end approach for extracting and segmenting high-variance references from PDF documents

   Boukhers, Zeyd , Ambhore, Shrihash , Staab, Steffen

   ACM/IEEE Joint Conference on Digital Library 1-10 2019

   Link DOI

2. EXCITE – A toolchain to extract, match and publish open literature references

   Hosseini, Azam , Ghavimi, Behnam , Boukhers, Zeyd , Mayr, Philipp

   ACM/IEEE Joint Conference on Digital Library 432-433 2019

   Link DOI

2016

1. Automatic recognition of movement patterns in the Vojta therapy using RGB-D data

   Khan, Muhammad Hassan , Helsper, Jullien , Boukhers, Zeyd , Grzegorzek, Marcin

   Image Processing (ICIP), IEEE International Conference on 1235-1239 2016

   Link DOI

2. Convoy detection in crowded surveillance videos

   Boukhers, Zeyd , Wang, Yicong , Shirahama, Kimiaki , Uehara, Kuniaki , Grzegorzek, Marcin

   Human Behavior Understanding (HBU) at ACM Multimedia, International Workshop on 137-147 2016

   Link DOI

2015

1. Shape-Based Eye Blinking Detection and Analysis

   Boukhers, Zeyd , Jarzyński, Tomasz , Schmidt, Florian , Tiebe, Oliver , Grzegorzek, Marcin

   Computer Recognition Systems (CORES), International Conference on 327-335 2015

   Link DOI

2. Environmental microbiological content-based image retrieval system using internal structure histogram

   Zou, Yan Ling , Li, Chen , Boukhers, Zeyd , Shirahama, Kimiaki , Jiang, Tao , Grzegorzek, Marcin

   Computer Recognition Systems (CORES), International Conference on 543-552 2015

   Link DOI

3. Object detection and depth estimation for 3D trajectory extraction

   Boukhers, Zeyd , Shirahama, Kimiaki , Li, Frédéric , Grzegorzek, Marcin

   Content-Based Multimedia Indexing (CBMI), International Workshop on 1-6 2015

   Link DOI

4. Extracting 3D Trajectories of Objects from 2D Videos Using Particle Filter

   Boukhers, Zeyd , Shirahama, Kimiaki , Li, Frédéric , Grzegorzek, Marcin

   Multimedia Retrieval, ACM International Conference on 83-90 2015

   Link DOI

Books & Book Chapters

2017

1. 3D Trajectory Extraction from 2D Videos for Human Activity Analysis

   Boukhers, Zeyd

   44 2017

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