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2025

• Wolff, M., Eilers, F. und Jiang, X., (im Druck) CVKAN: Complex-valued Kolmogorov-Arnold Networks. In: Proc. of IJCNN [Preprint]. [preprint]. doi: 10.48550/arXiv.2502.02417.
• Tu, P., Ye, H., Shi, H., Young, J., Xie, M., Zhao, P., Zheng, C., Jiang, X. und Chen, X., 2025. Phase-specific augmented reality guidance for microscopic cataract surgery using spatiotemporal fusion network: Information Fusion, 113, S. 102604.
• Xiao, J., Li, Y., Tian, Y., Jiang, X., Wang, Y. und Wang, S., 2025. Optimising allocation of marketing resources among offline channel retailers: A bi-clustering-based model: Journal of Business Research, 186, S. 114914.
• Zhang, J. und Jiang, X., (im Druck) Evolutionary training set pruning for boosting interpolation kernel machines. In: Pattern Recognition Applications and Methods
• Chen, J., Pi, D., Jiang, X., Gao, F., Wang, B. und Chen, Y., 2025. EEGCiD: EEG condensation into diffusion model: IEEE Transactions on Automation Science and Engineering, 22, S. 8502–8518.
• Xu, J., Gao, J., Jiang, S., Wang, C., Smedby, Ö., Wu, Y., Jiang, X. und Chen, X., (im Druck) Automatic segmentation of bone graft in maxillary sinus via distance constrained network guided by prior anatomical knowledge: IEEE Journal of Biomedical and Health Informatics
• Lixiang, Xu., Jiawang, P., Xiaoyi, J., Enhong, C. und Bin, L., 2025. Graph neural network based on graph kernel: A survey: Pattern Recognition, 161, S. 111307.
• Elischberger, F. und Jiang, X., 2025. Deep Learning meta architecture to detect spatially coherent coarse grain regions in ultrasonic data: NDT and E International, 153, S. 103342.
• Jin, X., Sihan, Li., Yuhang, T., Jing, H., Xiaoyi, J. und Shouyang, W., 2025. Example dependent cost sensitive learning based selective deep ensemble model for customer credit scoring: Scientific Reports, 15, Artikel 6000. doi: 10.1038/s41598-025-89880-7.
• Wei, Xu., Lixiang, Xu. und Xiaoyi, J., (im Druck) Resistance distance guided node injection attack on graph neural networks. In: Prof. of GbR
• Eilers, F. und Jiang, X., (im Druck) Initializing complex-valued neural networks from their pretrained real-valued counterparts. In: Proc. of IJCNN

2024

• Steinhorst, P., Duhme, C., Jiang, X. und Vahrenhold, J., 2024. Recognizing Patterns in Productive Failure. In: L. Battestilli, S. Rebelsky und L. Shoop Hrsg. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1. New York, NY: ACM Press. S. 1293–1299. doi: 10.1145/3626252.3630915.
• Xiao, J., Zhong, Y., Jia, Y., Wang, Y., Jiang, X. und Wang, S., 2024. A novel deep ensemble model for imbalanced credit scoring in internet finance: International Journal of Forecasting, 40(1), S. 348–372.
• Xiao, J., Wen, Z., Jiang, X., Yu, L. und Wang, S., 2024. Three-stage research framework to assess and predict the financial risk of SMEs based on hybrid method: Decision Support Systems, 177, S. 114090.
• Chen, J., Pi, D., Jiang, X., Xu, Y., Chen, Y. und Wang, X., 2024. Denosieformer: A transformer based approach for single-channel EEG artifact removal: IEEE Transactions on Instrumentation and Measurement, 73, S. 1–16.
• Eminaga, o., Saad, F., Tian, Z., Wolffgang, U., Karakiewicz, P., Ouellet, V., Azzi, F., Spieker, T., Helmke, B., Graefen, M., Jiang, X., Xing, L., Witt, J., Trudel, D. und Leyh-Bannurah, SM., 2024. Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images: npj Imaging, 2, S. 6.
• Zhang, J. und Jiang, X., 2024. Classification performance boosting for interpolation kernel machines by training set pruning using genetic algorithm. In: M. Castrillon-Santana, M.De. Marsico und A. Fred Hrsg. Prof. of ICPRAM. Rome: SciTePress. S. 428–435.
• Hegselmann, S., Shen, Z., Gierse, F., Agrawal, M., Sontag, D. und Jiang, X., 2024. A data-centric approach to generate faithful and high quality patient summaries with large language models. In: N.A. Hrsg. Conference on Health, Inference, and Learning (CHIL). New York: MLResearchPress. S. 339–379.
• Rexeisen, R. und Jiang, X., 2024. Challenging the robustness of image registration similarity metrics with adversarial attacks. In: M. Modat, I. Simpson, Ž. Špiclin, W. Bastiaansen, A. Hering und T. Mok Hrsg. Biomedical Image Registration. N.A.: Springer. S. 112–126.
• Zhang, J., Liu, CL. und Jiang, X., 2024. Polynomial kernel learning for interpolation kernel machines with application to graph classification: Pattern Recognition Letters, 186, S. 7–13.
• Manongga, WC., Chen, RC. und Jiang, X., 2024. Enhancing road marking sign detection in low-light conditions with YOLOv7 and contrast enhancement techniques: International Journal of Applied Science and Engineering, 21(3), S. 2023515.
• Zhang, J. und Jiang, X., 2024. Regularization of interpolation kernel machines. In: A.A. al. Hrsg. Proc. of ICPR. Cham: Springer Nature. S. 208–223.
• Rezaei, S. und Jiang, X., 2024. Superpixel-based sparse labeling for efficient and certain medical image annotation. In: A.A. al. Hrsg. Proc. of ICPR. Cham: Springer Nature. S. 376–390.
• Duhme, C., Lippe, C., Voerr, V. und Jiang, X., 2024. Transformer-based parameter fitting of models derived from Bloch-McConnell Equations for CEST MRI analysis. In: X. Xu, Z. Cui, I. Rekik, X. Ouyang und K. Sun Hrsg. Proc. of MLMI@MICCAI. Cham: Springer Publishing. S. 108–116.
• Leenings, R., Konowski, M., Winter, NR., Ernsting, J., Fisch, L., Barkhau, C., Dannlowski, U., Lügering, A., Jiang, X. und Hahn, T., 2024. C-TRUS: A novel dataset and initial benchmark for colon wall segmentation in transabdominal ultrasound . In: A. Gómez, V. Khanal, A. King und A. Namburete Hrsg. Proc. of ASMUS@MICCAI. Cham: Springer Publishing. S. 101–111.
• Oehm, JB., Wenning, O., Storck, M., Jiang, X. und Varghese, F., (im Druck) Automatic extraction of medication data from semi-structured prescriptions. In: Proc. of MIE
• Kara, AC., Rana, D., Glorius, F. und Jiang, X., (im Druck) ChemGraph Explainer: A graphical user interface for explaining predictions of graph neural networks in chemistry. In: Proc. of ECML Workshop ML4CCE

2023

• Dewi, C., Chen, RC., Yu, H. und Jiang, X., 2023. Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling: Journal of Ambient Intelligence and Humanized Computing, 14(7), S. 8135–8152.
• Nienkötter, A. und Jiang, X., 2023. Kernel-based generalized median computation for consensus learning: IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), S. 5872–5888.
• Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X. und Sontag, D., 2023. TabLLM: Few-shot classification of tabular data with large language models. In: N.A. Hrsg. PMLR - Proceedings of Machine Learning Research, Band 206: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS). 20. Aufl.. 2023: MLResearchPress. S. 5549–5581.
• Tistarelli, M., Dubey, S., Singh, S. und Jiang, X. Hrsg., 2023. Computer Vision and Machine Intelligence. Cham, Switzerland: Springer Nature.
• Eilers, F. und Jiang, X., 2023. Building blocks for a complex-valued transformer architecture. In: N/A Hrsg. Proc. of ICASSP. N/A: Wiley-IEEE Press. S. 1–5.
• Xiao, J., Tian, Y., Jia, Y., Jiang, X., Yu, L. und Wang, S., 2023. Black-box attack-based security evaluation framework for credit card fraud detection models: INFORMS Journal on Computing, 35(5), S. 986–1001.
• Sandmann, S., Richter, S., Jiang, X. und Varghese, J., 2023. Reconstructing clonal evolution - a systematic evaluation of current bioinformatics approaches: International journal of environmental research and public health, 20, S. 5128.
• Dewi, C., Chen, RC., Zhuang, YC., Jiang, X. und Yu, H., 2023. Recognizing road surface traffic signs based on Yolo models considering image flips: Big Data and Cognitive Computing, 7, S. 54.
• Zhang, J., Liu, CL. und Jiang, X., 2023. Quadratic kernel learning for interpolation kernel machine based graph classification. In: M. Vento, P. Foggia, D. Conte und V. Carletti Hrsg. Proc. of Int. Workshop on Graph-Based Representations in Pattern Recognition (GbR). Cham, Switzerland: Springer. S. 3–14.
• Zhang, J., Liu, CL. und Jiang, X., 2023. Interpolation kernel machines: Reducing multiclass to binary. In: N. Tsapatsoulis und e. al. Hrsg. Proc. of Int. Conf. on Computer Analysis of Images and Patterns (CAIP). Cham, Switzerland: Springer. S. 174–184.
• Jiang, X. und Nienkötter, A., 2023. Generalized median computation for consensus learning: A brief survey. In: N, Tsapatsoulis und e. al. Hrsg. Proc. of Int. Conf. on Computer Analysis of Images and Patterns (CAIP). Cham, Switzerland: Springer. S. 120–130.
• Kuhlmann, F., Rothaus, K., Jiang, X., Faatz, H., Pauleikhoff, D. und Gutfleisch, M., 2023. 3D retinal vessel segmentation in OCTA volumes: Annotated dataset MORE3D and hybrid U-net with flattening transformation. In: U.K. Rother Hrsg. Proc. of DAGM GCPR. Heidelberg: Springer. S. 291–306.
• Dewi, C., Chen, RC., Yu, H. und Jiang, X., 2023. XAI for image captioning using SHAP: Journal of Information Science and Engineering, 39, S. 711–724.

2022

• Dewi, C., Chen, RC., Jiang, X. und Yu, H., 2022. Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks: PeerJ Computer Science, 8, S. e943.
• Zhang, J., Liu, CL. und Jiang, X., 2022. Interpolation kernel machine and indefinite kernel methods for graph classification. In: M. Yacoubi, E. Granger, P. Yuen, U. Pal und N. Vincent Hrsg. Proc. of ICPRAI. Cham, Switzerland: Springer Nature. S. 467–479.
• Elischberger, F., Bamberg, B. und Jiang, X., 2022. Deep learning based detection of segregations for ultrasonic testing: IEEE Transactions on Instrumentation and Measurement, 71
• Sandmann, S., Richter, S., Jiang, X. und Varghese, J., 2022. Exploring current challenges and perspectives for automatic reconstruction of clonal evolution: Cancer Genomics and Proteomics, 19, S. 194–204. doi: 10.21873/cgp.20314.
• Kuhlmann, J., Rothaus, K., Jiang, X., Heimes-Bussmann, B., Faatz, H., Book, M. und Pauleikhoff, D., 2022. Axial stretching of vessels in the retinal vascular plexus with 3D OCT-Angiograph: Translational Vision Science & Technology, 11(2)
• Wang, TQ., Jiang, X. und Liu, CL., 2022. Query pixel guided stroke extraction with model-based matching for offline handwritten Chinese characters: Pattern Recognition, 123, S. 108416.
• Cebollada, S., Paya, L., Jiang, X. und Reinoso, O., 2022. Development and use of a convolutional neural network for hierarchical appearance-based localization: Artificial Intelligence Review, 55(4), S. 2847–2874.
• Drees, D., Eilers, F. und Jiang, X., 2022. Hierarchical random walker segmentation for large volumetric biomedical images: IEEE Transactions on Image Processing, 31, S. 4431–4446. doi: 10.1109/TIP.2022.3185551.
• Drees, D., Eilers, F., Bian, A. und Jiang, X., 2022. A Bhattacharyya coefficient-based framework for noise model-aware random walker image segmentation. In: B. Andres, F. Bernard, D. Cremers, S. Frintrop, B. Goldlücke und I. Ihrke Hrsg. Proc. of GCPR. Cham, Switzerland: Springer Nature. S. 166–181.
• Dewi, C., Chen, RC., Jiang, X. und Yu, H., 2022. Deep convolutional neural network for enhancing traffic sign recognition developed on Yolo V4: Multimedia Tools and Applications, 81(26), S. 37821–37845.
• Xu, J., Zeng, B., Egger, J., Wang, C., Smedby, Ö., Jiang, X. und Chen, X., 2022. A review on AI-based medical image computing in head and neck surgery: Physics in Medicine and Biology, 67, S. 17TR01.
• Dubey, A., Singh, SK. und Jiang, X., 2022. Leveraging CNN and transfer learning for classification of histopathology images. In: Khare N, Tomar DS, Ahirwal MK, Semwal VB und V. Soni Hrsg. Proc. of MIND. Cham, Switzerland: Springer Nature. S. 3–13.
• Chen, A., Wang, S., Jiang, X., Yan, S., Bian, A., Xu, W., Zeng, J. und Deng, T., 2022. Optical aerosol sizing method without prior refractive index: Measurement, 204, S. 112072.
• Gupta, S., Singh, SK. und Jiang, X., (im Druck) Leveraging tri-planar views and weighted average fusion technique to classify lung nodule malignancy. In: Proc. of CVIP
• Dewi, C., Chen, RC., Chang, CW., Wu, SH., Jiang, X. und Yu, H., 2022. Eye aspect ratio for real-time drowsiness detection to improve driver safety: Electronics, 11(19), Artikel 3183. doi: 10.3390/electronics11193183.

2021

• Flint, C., Cearns, M., Opel, N., Redlich, R., Mehler, D.M., Emden, D., Winter, NR., Leenings, R., Eickhoff, SB., Kircher, T., Krug, A., Nenadic, I., Arolt, V., Clark, S., Baune, BT., Jiang, X., Dannlowski, U. und Hahn, T., 2021. Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression: Neuropsychopharmacology, 46, S. 1510–517. doi: 10.1038/s41386-021-01020-7.
• Bian, A., Jiang, X., Berh, D. und Risse, B., 2021. Resolving colliding larvae by fitting ASM to random walker-based pre-segmentations: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3), S. 1184–1194.
• Nienkötter, A. und Jiang, X., 2021. Distance-preserving vector space embedding for consensus learning: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(2), S. 1244–1257.
• Xu, L., Bai, L., Jiang, X., Tan, M., Li, C. und Luo, B., 2021. Deep Rényi entropy graph kernel: Pattern Recognition, 111, S. 107668.
• Spiesshoefer, J., Hegerfeld, N., Gerdes, M., Klemm, S., Gorbachevski, M., Radke, R., Tuleta, I., Passino, C., Jiang, X., Sciarrone, S., Randerath, W., Dreher, M., Boentert, M. und Giannoni, A., 2021. Effects of central apneas on sympathovagal balance and hemodynamics at night: impact of underlying systolic heart failure: Sleep and Breathing, 25, S. 965–977.
• Kirschnick, N., Drees, D., Redder, E., Erapaneedi, R., Pereira, d.G., Schäfers, M., Jiang, X. und Kiefer, F., 2021. Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures: iScience, 24(6), S. 102650.
• Jiang, X., 2021. Editorial: A wonderful venue for networking neuroscience and computational intelligence: International Journal of Neural Systems, 31(6), S. 2103005:1–2103005:2.
• Chen, X., Li, Y., Xu, L., Sun, Y., Politis, C. und Jiang, X., 2021. A real time image-guided reposition system for the loosed bone graft in orthognathic surgery: Computer Assisted Surgery, 26(1), S. 1–8.
• Welsing, A., Nienkötter, A. und Jiang, X., 2021. Exponential weighted moving average of time series in arbitrary spaces with application to strings. Joint IAPR Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR). Milan, Italy.
• Leenings, R., Winter, NR., Plagwitz, L., Holstein, V., Ernsting, J., Sarink, K., Fisch, L., Steenweg, J., Kleine-Vennekate, L., Gebker, J., Emden, D., Grotegerd, D., Opel, N., Risse, B., Jiang, X., Dannlowski, U. und Hahn, T., 2021. PHOTONAI-A Python API for rapid machine learning model development: PloS one, 16 doi: 10.1371/journal.pone.0254062.
• Frintrop, S., Fink, GA. und Jiang, X., 2021. Editorial: Special Issue: Computer Vision and Pattern Recognition (DAGM GCPR 2019): International Journal of Computer Vision, 129(11), S. 3004–3005.
• Blanger, L., Hirata, N. und Jiang, X., 2021. Reducing the need for bounding box annotations in object detection using image classification data. 34th SIBGRAPI Conference on Graphics, Patterns and Images. online.
• Xu, J., Liu, J., Zhang, D., Zhou, Z., Jiang, X., Zhang, C. und Chen, X., 2021. Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates: International Journal of Computer Assisted Radiology and Surgery, 16, S. 1785–1794.
• Leenings, R., Winter, N., Plagwitz, L., Holstein, V., Ernsting, J., Steenweg, J., Gebker, J., Sarink, K., Emden, D., Grotegerd, D., Opel, N., Risse, B., Jiang, X., Dannlowski, U. und Hahn, T., 2021. PHOTONAI - A Python API for rapid machine learning model development: PloS one, 16(7), S. e0254062.
• Winter, D., Bian, A. und Jiang, X., 2021. Layer-wise relevance propagation based sample condensation for kernel machines. 19th International Conference on Computer Analysis of Images and Patterns. online.
• Dewi, C., Chen, RC., Liu, YT., Jiang, X. und Hartomo, KD., 2021. Yolo V4 for advanced traffic sign recognition with synthetic training data generated by various GAN: IEEE Access, 9, S. 97228–97242.
• Drees, D., Scherzinger, A., Hägerling, R., Kiefer, F. und Jiang, X., 2021. Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets: BMC Bioinformatics, 22(1), Artikel 346. doi: 10.1186/s12859-021-04262-w.
• Kirschnick, N., Drees, D., Redder, E., Erapaneedi, R., Pereira da Graca, A., Schäfers, M., Jiang, X. und Kiefer, F., 2021. Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures: iScience, 24(6), Artikel 102650. doi: 10.1016/j.isci.2021.102650.

2020

• Moreno-García, CF., Serratosa, F. und Jiang, X., 2020. Correspondence edit distance to obtain a set of weighted means of graph correspondences: Pattern Recognition Letters, 134, S. 29–36.
• Xu, L., Wang, X., Bai, L., Xiao, J., Liu, Q., Chen, E., Jiang, X. und Luo, B., 2020. Probabilistic SVM classifier ensemble selection based on GMDH-type neural network: Pattern Recognition, 106, S. 107373.
• Zhang, L., Zhang, JZ., Jiang, X. und Liang, B., 2020. Error correctable hand-eye calibration for stripe-laser vision-guided robotics: IEEE Transactions on Instrumentation and Measurement, 69(10), S. 8314–8327.
• Xiao, J., Jia, Y., Jiang, X. und Wang, S., 2020. Circular complex-valued GMDH-type neural network for real-valued classification problems: IEEE Transactions on Neural Networks and Learning Systems, 31(12), S. 5285–5299.
• Zhang, JQ. und Jiang, X., 2020. Improved computation of affine dynamic time warping. International Conference on Applications of Intelligent Systems. Las Palmas de Gran Canaria.
• Xiao, J., Tian, Y., Xie, L., Jiang, X. und Huang, J., 2020. A hybrid classification framework based on clustering: IEEE Transactions on Industrial Informatics, 16(4), S. 2177–2188.
• Nienkötter, A. und Jiang, X., 2020. A lower bound for generalized median based consensus learning using kernel-induced distance functions: Pattern Recognition Letters, 140, S. 339–347.
• Dewi, C., Chen, RC., Liu, YT., Tai, SK., Jiang, X. und Yu, H., 2020. Deep Learning for traffic sign recognition based on spatial pyramid pooling with scale analysis: Applied Sciences, 10, S. 6997.
• Sun, Q., Mai, Y.Y., Ji, T., Jiang, X. und Chen, X., 2020. Fast and accurate online calibration of optical see-through head-mounted display for AR based surgical navigation using Microsoft HoloLens: International Journal of Computer Assisted Radiology and Surgery, 15(11), S. 1907–1919.
• Xu, J., Wang, S., Zhou, Z., Liu, J., Jiang, X. und Chen, X., 2020. Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net: International Journal of Computer Assisted Radiology and Surgery, 15(9), S. 1457–1465.
• Monteiro, S.A., Hirata, N. und Jiang, X., 2020. Skeletal similarity based performance evaluation for document binarization. 17th International Conference on Frontiers in Handwriting Recognition. Dortmund, Germany.
• Klemm, S., Rexeisen, R., Stummer, W., Jiang, X. und Holling, M., 2020. A video processing pipeline for intraoperative analysis of cerebral blood flow: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 8(4), S. 356–366. doi: 10.1080/21681163.2018.1559771.

2019

• Chen, X., Hu, Y., Zhang, Z., Wang, B., Zhang, L., Shi, F., Chen, X. und Jiang, X., 2019. A graph-based approach to automated EUS image layer segmentation and abnormal region detection: Neurocomputing, 336, S. 79–91.
• Fink, G., Frintrop, S. und Jiang, X. Hrsg., 2019. LNCS Volume 11824: Pattern Recognition. Springer VDI Verlag.
• Cebollada, S., Paya, L., Vaiente, D., Jiang, X. und Reinoso, O., 2019. An evaluation between global appearance descriptors based on analytic methods and deep learning techniques for localization in autonomous mobile robots. 16th International Conference on Informatics in Control, Automation and Robotics. Prague.
• Hamid, S., Derstroff, A., Klemm, S., Ngo, Q.Q., Jiang, X. und Linsen, L., 2019. Visual ensemble analysis to study the influence of hyper-parameters on training deep neural networks. In: D.a. Archambault und J. Ian and Peltonen Hrsg. Machine Learning Methods in Visualisation for Big Data. Eurographics Association. doi: 10.2312/mlvis.20191160.
• Klemm, S., Ortkemper, RD. und Jiang, X., 2019. Deploying Deep Learning into practice: A case study on fundus segmentation. Proc. of 23rd Conference in Medical Imaging, Understanding and Analysis. Liverpool, UK.
• O'Sullivan, S., Ali, Z., Jiang, X., Abdolvand, R., Ünlü, MS., Placido, d.S., Baca, J., Kim, B., Scott, S., Sajid, M., Moradian, S., Mansoorzare, H. und Holzinger, A., 2019. Developments in connectivity and Machine Learning/AI for point-of-care testing: Sensors, 19(8), S. 1917.
• Drees, D., Scherzinger, A. und Jiang, X., 2019. GERoMe – a method for evaluating stability of graph extraction algorithms without ground truth: IEEE Access, 7, S. 21744–21755. doi: 10.1109/ACCESS.2019.2898754.
• Michels, T., Berh, D. und Jiang, X., 2019. An RJMCMC-based method for tracking and resolving collisions of Drosophila Larvae: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(2), S. 465–474.

2018

• Berh, D., Scherzinger, A., Otto, N., Jiang, X., Klämbt, C. und Risse, B., 2018. Automatic non-invasive heartbeat quantification of Drosophila pupae: Computers in Biology and Medicine, 93, S. 189–199.
• Otto, N., Marelja, Z., Schoofs, A., Kranenburg, H., Bittern, J., Yildirim, K., Berh, D., Bethke, M., Thomas, S., Rode, S., Risse, B., Jiang, X., Pankratz, M., Leimkuehler, S. und Klämbt, C., 2018. The Sulfite Oxidase Shopper controls neuronal activity by regulating glutamate homeostasis in Drosophila ensheathing glia: Nature Communications, 9(1), S. 3514.
• Kiel, M., Berh, D., Daniel, J., Otto, N., Steege, A., Jiang, X., Liebau, E. und Risse, B., 2018. A Multi-Purpose Worm Tracker Based on FIM. [Preprint].
• Valenzuela, D., Jiang, X., Carrillo, A. und Rojas, I., 2018. Multi-objective genetic algorithms to find most relevant volumes of the brain related to Alzheimer's disease and mild cognitive impairment: International Journal of Neural Systems, 28(9), S. 1850022.
• Klemm, S., Scherzinger, A., Drees, D. und Jiang, X., 2018. Barista - a graphical tool for designing and training deep neural networks. arXiv e-print:1802.04626: CoRR.
• Klemm, S., Jiang, X. und Risse, B., 2018. Deep distance transform to segment visually indistinguishable merged objects. Proc. of 40th German Conference on Pattern Recognition (GCPR). Stuttgart.
• Scherzinger, A., Hugenroth, P., Rüder, M., Bogdan, S. und Jiang, X., 2018. Multi-class Cell Segmentation using CNNs with F1-measure Loss Function. Proc. of 40th German Conference on Pattern Recognition (GCPR). Stuttgart.
• Nienkötter, A. und Jiang, X., 2018. Consensus learning for sequence data. In: M. Last, H. Bunke und A. Kandel Hrsg. Data Mining in Time Series and Streaming Databases. World Scientific Publishing. S. 69–91.
• Xu, L., Jiang, X., Bai, L., Xiao, J. und Luo, B., 2018. A hybrid reproducing graph kernel based on information entropy: Pattern Recognition, 73, S. 89–98.
• Mo, L., Xie, L., Jiang, X., Teng, G., Xu, L. und Xiao, J., 2018. GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries: Applied Soft Computing, 62, S. 478–490.
• Frohwein, L., Hess, M., Schlicher, D., Bolwin, K., Büther, F., Jiang, X. und Schäfers, K., 2018. PET attenuation correction for flexible MRI surface coils in hybrid PET/MRI using a 3D depth camera: Physics in Medicine and Biology, 63(2), S. 025033. doi: 10.1088/1361-6560/aa9e2f.

2017

• Seifert, R., Scherzinger, A., Kiefer, F., Hermann, S., Jiang, X. und Schäfers, M., 2017. Statistical permutation-based artery mapping (SPAM): A novel approach to evaluate imaging signals in the vessel wall: BMC Medical Imaging, 17(1), S. 36:1–36:11.
• Risse, B., Otto, N., Berh, D., Jiang, X., Kiel, M. und Klämbt, C., 2017. FIM2c: A multi-colour, multi-purpose imaging system to manipulate and analyse animal behaviour.: IEEE Transactions on Biomedical Engineering, 64(3), S. 610–620.
• Roters, J. und Jiang, X., 2017. FestGPU: A framework for fast robust estimation on GPU: Journal of Real-Time Image Processing, 13(4), S. 759–772.
• Bian, A., Scherzinger, A. und Jiang, X., 2017. An enhanced multi-label random walk for biomedical image segmentation using statistical seed generation. Proc. of 18th Int. Confernce on Advanced Concepts for Intelligent Vision Systems (ACIVS). Antwerp, Belgium.
• Hägerling, R., Drees, D., Scherzinger, A., Dierkes, C., Martin-Almedina, S., Butz, S., Gordon, K., Schäfers, M., Hinrichs, K., Ostergaard, P., Vestweber, D., Goerge, T., Mansour, S., Jiang, X., Mortimer, P. und Kiefer, F., 2017. VIPAR, a quantitative approach to 3D-histopathology applied to lymphatic malformations: JCI Insight, 2(16), S. e93424.
• Xiao, J., Cao, HW., Jiang, X., Gu, X. und Xie, L., 2017. GMDH-based semi-supervised feature selection for customer classification: Knowledge-Based Systems, 132, S. 236–248.
• Broelemann, K., Jiang, X., Mukherjee, S. und Chowdhury, AS., 2017. Variants of k-regular nearest neighbor graph and their construction: Information Processing Letters, 126, S. 18–23.
• Scherzinger, A., Klemm, S., Berh, D. und Jiang, X., 2017. CNN-based background subtraction for long-term in-vial FIM imaging. Proc. of 17th Int. Conf. on Computer Analysis of Images and Patterns (CAIP). Ystad, Sweden.
• Almeida-Carvalho MJ, B.D., ......, J.X., ......, R.B. und ......, Z.M., 2017. The Ol1mpiad: Concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae: Journal of Experimental Biology, 220, S. 2452–2475. doi: 10.1242/jeb.156646.
• Risse, B., Berh, D., Otto, N., Klämbt, C. und Jiang, X., 2017. FIMTrack: An open source tracking and locomotion analysis software for small animals: PLoS Computational Biology, 13(5), S. e1005530. doi: 10.1371/journal.pcbi.1005530.
• Moreno, C.F., Serratosa, F. und Jiang, X., 2017. An edit distance between graph correspondences. Proc. of Int. Workshop on Graph-Based Representations in Pattern Recognition (GbR). Anacapri.
• Drees, D., Scherzinger, A. und Jiang, X., 2017. GERoMe - A novel graph extraction robustness measure. Proc. of Int. Workshop on Graph-Based Representations in Pattern Recognition (GbR). Anacapri, Italy.
• Ungru, K. und Jiang, X., 2017. Dynamic programming based segmentation in biomedical imaging: Computational and Structural Biotechnology Journal, 15, S. 255–264.
• Bian, A. und Jiang, X., 2017. T-Test based adaptive random walk segmentation under multiplicative speckle noise model. Proc. of ACCV-Workshops. Taipei, Taiwan.
• Berh, D., Risse, B., Michels, T., Otto, N., Jiang, X. und Klämbt, C., 2017. A FIM-based long-term in-vial monitoring system for Drosophila larvae: IEEE Transactions on Biomedical Engineering, 64(8), S. 1862–1874.
• Tenbrinck, D. und Jiang, X., 2017. Image segmentation with physical noise models. In: A. El-Baz, X. Jiang und J. Suri Hrsg. Biomedical Image Segmentation: Advances and Trends. CRC Press. S. 461–484.

2016

• Xiao, J., Jiang, X., He, C. und Teng, G., 2016. Churn prediction in customer relationship management via GMDH-based multiple classifiers ensemble: IEEE Intelligent Systems, 31(2), S. 37–44.
• Teng, G., He, C., Xiao, J., He, Y., Zhu, B. und Jiang, X., 2016. Cluster ensemble framework based on the group method of data handling: Applied Soft Computing, 43, S. 35–46.
• Broelemann, K., Jiang, X. und Schwering, A., 2016. Automatic understanding of sketch maps using context-aware classification: Expert Systems with Applications, 45, S. 195–207.
• El-Baz, A., Jiang, X. und Suri, J. Hrsg., 2016. Biomedical Image Segmentation: Advances and Trends. CRC Press.
• Nienkötter, A. und Jiang, X., 2016. Distance-preserving vector space embedding for the closest string problem. Proc. of 23rd Int. Conf. on Pattern Recognition (ICPR). Cancun, Mexico. doi: 10.1109/ICPR.2016.7899854.
• Nienkötter, A. und Jiang, X., 2016. Improved prototype embedding based generalized median computation by means of refined reconstruction methods. Proc. of Joint IAPR Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR). Merida, Mexico.
• Wiesemann, T. und Jiang, X., 2016. Fog augmentation of road images for performance analysis of traffic sign detection algorithms. Proc. of 17th Int. Confernce on Advanced Concepts for Intelligent Vision Systems (ACIVS). Lecce, Italy.
• Bian, A. und Jiang, X., 2016. Statistical modeling based adaptive parameter setting for random walk segmentation. Proc. of 17th Int. Confernce on Advanced Concepts for Intelligent Vision Systems (ACIVS). Lecce, Italy.
• Scherzinger, A., Kleene, F., Dierkes, C., Kiefer, F., Hinrichs, KH. und Jiang, X., 2016. Automated Segmentation of Immunostained Cell Nuclei in 3D Ultramicroscopy Images. Proc. of 38th German Conference on Pattern Recognition (GCPR). Hannover, Germany. Springer.
• Otto, N., Risse, B., Berh, D., Bittern, J., Jiang, X. und Klämbt, C., 2016. Interactions among Drosophila larvae before and during collision: Scientific Reports, 11(6), S. 31564. doi: 10.1038/srep31564.
• Siegert, Y., Jiang, X., Krieg, V. und Bartholomäus, S., 2016. Classification-based record linkage with pseudonymized data for epidemiological cancer registries: IEEE Transactions on Multimedia, 18(10), S. 1929–1941.
• Martinez-Trinidad, J., Carrasco-Ochoa, J., Ayala, R.V., Olvera-Lopez, J. und Jiang, X. Hrsg., 2016. Pattern Recognition. Springer VDI Verlag.

2015

• Tenbrinck, D. und Jiang, X., 2015. Image Segmentation with Arbitrary Noise Models by Solving Minimal Surface Problems: Pattern Recognition, 48(11), S. 2393–3309. doi: 10.1016/j.patcog.2015.01.006.
• Gangapure, V.N., Nanda, S., Chowdhury, A.S. und Jiang, X., 2015. Causal video segmentation using superseeds and graph matching. IAPR International Workshop on Graph-based Representations in Pattern Recognition. Beijing.
• Carletti, V., Foggia, P., Vento, M. und Jiang, X., 2015. Report on the first contest on graph matching algorithms for pattern search in biological databases. IAPR International Workshop on Graph-based Representations in Pattern Recognition. Beijing.
• Jiang, X., Percannella, G. und Vento, M., 2015. A verification-based multithreshold probing approach to HEp-2 cell segmentation. CAIP. Malta.
• Schmid, S., Jiang, X. und Schäfers, K., 2015. Puzzle approach to pose tracking of a rigid object in a multi camera system. CAIP. Malta.
• Araki, T., Ikeda, N., Dey, N., Chakraborty, S., Saba, L., Kumar, D., Godia, E.C., Jiang, X., Gupta, A., Radeva, P., Laird, J.R., Nicolaides, A. und Suri, J.S., 2015. A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound: Computer Methods and Programs in Biomedicine, 118(2), S. 158–172.
• Wu, Z., Jiang, X., Zheng, N. und Cheng, D., 2015. Efficient block-wise temporally consistent contour extraction in image sequences: Neurocomputing, 152, S. 127–138.
• B., Z., C., He. und X., J., 2015. Customer choice prediction based on transfer learning: Journal of the Operational Research Society, 66(6), S. 1044–1051.
• Risse, B., Berh, D., Otto, N., Jiang, X. und Klämbt, C., 2015. Quantifying subtle locomotion phenotypes of drosophila larvae using internal structures based on FIM images: Computers in Biology and Medicine, 63, S. 269–276.
• B., Z., C., He. und X., J., 2015. A consistency-based validation for data clustering: Intelligent Data Analysis, 19(3), S. 471–484.
• Seebach, J., Taha, A.A., Lenk, J., Lindemann, N., Jiang, X., Brinkmann, K., Bogdan, S. und Schnittler, a.H.-J., 2015. The CellBorderTracker, a novel tool to quantitatively analyse spatiotemporal endothelial junction dynamics at the subcellular level: Histochemistry and Cell Biology, 144(6), S. 517–532.
• Schmeing, M. und Jiang, X., 2015. Faithful disocclusion filling in depth image based rendering using superpixel-based inpainting: IEEE Transactions on Multimedia, 17(12), S. 2160–2173.
• Risse, B., 2015. FIM Imaging and the Analysis of Drosophila Locomotion. Dissertationsschrift. Universität Münster
• Wu, Z., Jiang, X., Zheng, N., Liu, Y. und Cheng, a.D., 2015. Exact solution to median surface problem using 3D graph search and application to parameter space exploration: Pattern Recognition, 48(2), S. 380–390.
• Chakraborti, T., Jha, D.K., Chowdhury, A.S. und Jiang, X., 2015. A self-adaptive matched filter for retinal blood vessel detection: Machine Vision and Applications, 26(1), S. 68. doi: 10.1007/s00138-014-0636-z.

2014

• Franek, L., Jiang, X. und He, C., 2014. Weighted mean of a pair of clusterings: Pattern Analysis and Applications, 17(1), S. 166. doi: 10.1007/s10044-012-0304-8.
• Franek, L. und Jiang, X., 2014. Ensemble clustering by means of clustering embedding in vector spaces: Pattern Recognition, 47(2), S. 842. doi: 10.1016/j.patcog.2013.08.019.
• Broelemann, K., 2014. Automatic Offline Understanding of Hand-Drawn Sketch Maps. Dissertationsschrift. Universität Münster
• Jiang, X., Hornegger, J. und Koch, a.R. Hrsg., 2014. Pattern Recognition. Springer VDI Verlag.
• Gigengack, F., Jiang, X., Dawood, M. und Schäfers, a.K., 2014. Motion Correction in Thoracic Positron Emission Tomography. Springer VDI Verlag.
• Tham, T., Ichikawa, K., Oyama-Higa, M., Coomans, D. und Jiang, X. Hrsg., 2014. Biomedical Informatics and Technology. Springer VDI Verlag.
• Risse, B., Otto, N., Berh, D., Jiang, X. und Klämbt, C., 2014. FIM imaging and FIMTrack: Two new tools allowing high-throughput and cost effective locomotion analysis: Journal of Visualized Experiments, 94
• Ungru, K., Tenbrinck, D., Jiang, X. und Stypmann, a.J., 2014. Automatic classification of left ventricular wall segments in small animal ultrasound imaging: Computer Methods and Programs in Biomedicine, 117(1), S. 2-12.
• Fieseler, M., Gigengack, F., Jiang, X. und Schäfers, a.K., 2014. Motion correction of whole-body PET data with a joint PET-MRI registration functional: BioMedical Engineering OnLine, 13(Suppl 1)
• Schmeing, M., Krauskopf, E. und Jiang, X., 2014. Real-time depth fusion using a low-cost depth sensor array. In: 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (to appear)
• Schmeing, M. und Jiang, X., 2014. Edge-aware depth image filtering using color segmentation: Pattern Recognition Letters, 50, S. 63–71. doi: 10.1016/j.patrec.2014.03.026.
• Schmeing, M. und Jiang, X., 2014. Superpixel-based disocclusion filling in depth image based rendering. In: IEEE International Conference on Pattern Recognition
• Lammel, U., Bechtold, M., B, B.R., Berh, D., Fleige, A., Bunse, I., Jiang, X., Klämbt, C. und Bogdan, S., 2014. The Drosophila FHOD1-like formin Knittrig acts through Rok to promote stress fiber formation and directed macrophage migration during the cellular immune response.: Development, 141(6), S. 1366–1380. doi: 10.1242/dev.101352.
• Schmeing, M.A., 2014. Disocclusion Filling in Depth-Based View Synthesis and Depth Map Enhancement. Dissertationsschrift. Universität Münster
• Chan, Y., Huang, D., Liu, K., Chen, R. und Jiang, X., 2014. An automatic indirect immunofluorescence cell segmentation system: Mathematical Problems in Engineering, 2014 doi: 10.1155/2014/501206.

2013

• Dawood, M., Gigengack, F., Jiang, X. und Schafers, KP., 2013. A mass conservation-based optical flow method for cardiac motion correction in 3D-PET.: Medical Physics, 40(1), S. 012505. doi: 10.1118/1.4770276.
• Gigengack, F., 2013. Mass-Preserving Motion Correction and Multimodal Image Segmentation in Positron Emission Tomography. Dissertationsschrift. Universität Münster
• Tenbrinck, D., 2013. Variational Methods for Medical Ultrasound Imaging. Dissertationsschrift. Universität Münster
• Roters, J., 2013. Incremental Multi-View 3D Reconstruction with Application to Disaster Management. Dissertationsschrift. Universität Münster
• Kropatsch, W., Artner, N., Haxhimusa, Y. und Jiang, a.X. Hrsg., 2013. Graph-Based Representations in Pattern Recognition. Springer VDI Verlag.
• Jiang, X., Bellon, O., Goldgof, D. und Oishi, a.T. Hrsg., 2013. LNCS: Advances in Depth Image Analysis and Applications. Springer VDI Verlag.
• Jiang, L.F., 2013. Orthogonal design of experiments for parameter learning in image segmentation: Signal Processing, 93
• Risse, B., Berh, D., Tao, J., Jiang, X., Klette, R. und Klämbt, a.C., 2013. Comparison of two 3D tracking paradigms for freely flying insects: Eurasip Journal on Image and Video Processing, 2013
• Sawatzky, A., Tenbrinck, D., Jiang, X. und Burger, a.M., 2013. A variational framework for region-based segmentation incorporating physical noise models: Journal of Mathematical Imaging and Vision, 47
• Teng, G.-E., He, C., Xiao, J. und Jiang, a.X., 2013. Customer credit scoring based on HMM-GMDH hybrid model: Knowledge and Information Systems, 36
• Tenbrinck, D., Schmid, S., Jiang, X., Schäfers, K. und Stypmann, J., 2013. Histogram-based optical flow for motion estimation in ultrasound imaging: Journal of Mathematical Imaging and Vision, 47
• Fieseler, M., Kugel, H., Gigengack, F., Kösters, T., Büther, F., Quick, H.H., Faber, C., Jiang, X. und Schäfers., a.K., 2013. A dynamic thorax phantom for the assessment of cardiac and respiratory motion correction in PET/MRI: A preliminary evaluation: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 702, S. 59–63. doi: 10.1016/j.nima.2012.09.039.
• Schmeing, M. und Jiang, X., 2013. Color segmentation based depth image filtering. In: X. Jiang, O. Bellon, D. Goldgof und T. Oishi Hrsg. Lecture Notes in Computer Science, Band 7854: Advances in Depth Image Analysis and Applications. Springer VDI Verlag. S. 68–77. doi: 10.1007/978-3-642-40303-3\_8.
• Schmeing, M. und Jiang, X., 2013. A background modeling-based faithful approach to the disocclusion problem in depth image-based rendering: International Journal of Pattern Recognition and Artificial Intelligence, 27(02), S. 1354005. doi: 10.1142/S0218001413540050.
• Risse, B., Jiang, X. und Klämbt, C., 2013. FIM: Frustrated Total Internal Reflection Based Imaging for Biomedical Applications: ERCIM News, 95(Image Understanding), S. 11–12.
• Sander, M., Squarr, A.J., Risse, B., Jiang, X. und Bogdan, S., 2013. Drosophila pupal macrophages - A versatile tool for combined ex vivo and in vivo imaging of actin dynamics at high resolution.: European Journal of Cell Biology, 2013 doi: 10.1016/j.ejcb.2013.09.003.
• Risse, B., Thomas, S., Otto, N., Löpmeier, T., Valkov, D., Jiang, X. und Klämbt, C., 2013. FIM, a novel FTIR-based imaging method for high throughput locomotion analysis: PloS one, 8(1), S. e53963. doi: 10.1371/journal.pone.0053963.

2012

• Franek, L., 2012. Ensemble Algorithms with Applications to Clustering and Image Segmentation. Dissertationsschrift. Universität Münster
• Schmeing, M. und Jiang, X., 2012. Faithful Spatio-Temporal Disocclusion Filling Using Local Optimization. In: IEEE International Conference on Pattern Recognition. S. 3799–3802.
• Broelemann, K., Dutta, A., Jiang, X. und Llados, J., 2012. Hierarchical Graph Representation for Symbol Spotting in Graphical Document Images. Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition. Hiroshima, Japan.
• Dawood, M., Jiang, X. und Schäfers, K. Hrsg., 2012. Correction Techniques in Emission Tomographic Imaging. CRC Press.
• Xiao, J., Xie, L., He, C. und Jiang, X., 2012. Dynamic classifier ensemble model for customer classification with imbalanced class distribution: Expert Systems with Applications, 39(3), S. 3668–3675. doi: 10.1016/j.eswa.2011.09.059.
• Jiang, X., Wentker, J. und Ferrer, M., 2012. Generalized median string computation by means of string embedding in vector spaces: Pattern Recognition Letters, 33(7)
• Jiang, X., Lambers, M. und Bunke, H., 2012. Structural performance evaluation of curvilinear structure detection algorithms with application to retinal vessel segmentation: Pattern Recognition Letters, 33(15)
• Franek, L., Jiang, X. und Wattuya, P., 2012. Local Instability Problem of Image Segmentation Algorithms: Systematic Study and an Ensemble-Based Solution: International Journal of Pattern Recognition and Artificial Intelligence, 26
• Broelemann, K.J., 2012. A region-based method for sketch map segmentation. In: Y.-B. Kwon und J.-M. Ogier Hrsg. Lecture Notes in Computer Science, Band 7423: Graphics Recognition. New Trends and Challenges. Berlin Heidelberg: Springer. S. 1–14. doi: 10.1007/978-3-642-36824-0_1.
• Gigengack, F., Ruthotto, L., Burger, M., Wolters, C., Jiang, X. und Schafers, K., 2012. Motion Correction in Dual Gated Cardiac PET Using Mass-Preserving Image Registration.: IEEE Transactions on Medical Imaging, 31(3), S. 698–712.

2011

• Fieseler, M. und Jiang, X., 2011. Discontinuity-based registration of depth and video data in depth image based rendering: Signal, Image and Video Processing, 5(3), S. 353–361. doi: 10.1007/s11760-010-0199-z.
• Jiang, X., Ferrer, M. und Torsello, A. Hrsg., 2011. LNCS, Band 6658: Graph-Based Representations in Pattern Recognition. Springer VDI Verlag.
• Jiang, X., Große, A. und Rothaus, K., 2011. Interactive segmentation of non-star-shaped contours by dynamic programming: Pattern Recognition, 44(9), S. 2001–2009. doi: 10.1016/j.patcog.2011.03.010.
• Jiang, X., Broelemann, K., Wachenfeld, S. und Krüger, A., 2011. Graph-based markerless registration of city maps using geometric hashing: Computer Vision and Image Understanding, 115(7), S. 1031–1042. doi: 10.1016/j.cviu.2010.12.014.
• Pham, T., Zhou, X., Tanaka, H., Oyama-Higa, M., Jiang, X., Sun, C., Kowalski, J. und Jia, a.X. Hrsg., 2011. Proc. of Int. Symposium on Computational Models for Life Sciences. Selbstverlag / Eigenverlag.
• Roters, J., Jiang, X. und Rothaus, K., 2011. Recognition of traffic lights in live video streams on mobile devices: IEEE Transactions on Circuits and Systems for Video Technology, 21(10)
• Schmeing, M. und Jiang, X., 2011. Depth Image Based Rendering. In: W.P. S.P. Hrsg. Pattern Recognition, Machine Intelligence and Biometrics. Springer VDI Verlag. S. 279–310.
• Schmeing, M. und Jiang, X., 2011. Time-Consistency of Disocclusion Filling Algorithms in Depth Image Based Rendering. In: Proc. 3DTV Conf.: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON). S. 1–4. doi: 10.1109/3DTV.2011.5877201.

2010

• Abdala, D.D., 2010. Ensemble and Constrained Clustering with Applications. Dissertationsschrift. Universität Münster
• Wattuya, P., 2010. Combination of Multiple Image Segmentations. Dissertationsschrift. Universität Münster
• Xiao J, He. und Z C, J.X., 2010. A dynamic classifer ensemble selection approach for noise data: Information Sciences
• Lewin, S., Jiang, X. und Clausing, A., 2010. Perceptually motivated shape evolution with shape-preserving property: Pattern Recognition Letters, 31(6), S. 447–453.
• Bartenhagen, C., Klein, HU., Ruckert, C., Jiang, X. und Dugas, M., 2010. Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data: Trials, 11, S. 567–577. doi: 10.1186/1471-2105-11-567.
• Jiang, X., Ma, M. und Chen, C. Hrsg., 2010. Multimedia Processing: Fundamentals, Methods, and Applications. Springer VDI Verlag.
• Lewin, S., Jiang, X. und Clausing, A., 2010. Perceptually motivated shape evolution with shape-preserving property: Pattern Recognition Letters, 31(6), S. 447–453.
• Gigengack, F., Ruthotto, L., Burger, M., Wolters, C., Jiang, X. und Schafers, K., 2010. Motion correction of cardiac PET using mass-preserving registration. 2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010. Knoxville, TN, usa. doi: 10.1109/NSSMIC.2010.5874418.
• Wachenfeld, S., Terlunen, S. und Jiang, X., 2010. Robust 1-D barcode recognition on camera phones and mobile product information display. In: Mobile Multimedia Processing: Fundamentals, Methods and Applications. Springer VDI Verlag. S. 53–69.
• Wachenfeld, W., Madeja, M. und Jiang, X., 2010. Developing mobile multimedia applications on Symbian OS devices. In: Mobile Multimedia Processing: Fundamentals, Methods and Applications . Springer VDI Verlag. S. 238–263.
• Jiang, X. und Bunke, H., 2010. Learning by generalized median concept. In: Pattern Recognition and Machine Vision . River Publishers. S. 1–16.
• Riesen, K., Jiang, X. und Bunke, H., 2010. Exact and inexact graph matching- Methodology and applications. In: C. Aggarwal und H. Wang Hrsg. Managing and Mining Graph Data . Springer VDI Verlag. S. 217–247.
• Dawood, M., Brune, C., Jiang, X., Büther, F., Burger, M., Schober, O., Schäfers, M. und Schäfers, K., 2010. A continuity equation based optical flow method for cardiac motion correction in 3D PET data. In: L. Hongen, E.E. P.J. und P. Xiaochuan Hrsg. Medical Imaging and Augmented Reality: 5th International Workshop, MIAR 2010, Beijing, China, September 19-20, 2010, Proceedings (Lecture Notes in ... Vision, Pattern Recognition, and Graphics). Berlin Heidelberg: Springer. S. 88–97. doi: 10.1007/978-3-642-15699-1_10.
• Abdala, D. und Jiang, X., 2010. Fiber segmentation using constrained clustering. Proc. of ICMB. Hong Kong.
• Jiang, X. und Abdala, D., 2010. Exploring the performance limit of cluster ensemble techniques. Proc. of SSPR. Izmir.
• Abdala, D., Wattuya, P. und Jiang, X., 2010. Ensemble clustering via random walker consensus strategy. Proc. of ICPR. Istambul.
• Schmeing, M. und Jiang, X., 2010. Depth Image Based Rendering: A Faithful Approach For the Disocclusion Problem. In: Proc. 3DTV-Conf.: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON). S. 1–4. doi: 10.1109/3DTV.2010.5506596.

2009

• Wachenfeld, S., 2009. Recognition of Screen-Rendered Text. Dissertationsschrift. Universität Münster
• Stadelmann R, J.X. und Mojon, S.D., 2009. A computer-based test to quantify the Pulfrich stereo phenomenon: Ophthalmologica
• Dawood, M., Büther, F., Stegger, L., Jiang, X., Schober, O., Schäfers, M. und Schäfers, K., 2009. Optimal number of respiratory gates in positron emission tomography: A cardiac patient study: Medical Physics, 36(15), S. 1775–1784.
• Von Wangenheim A, B., F R, S.A., Coser L, A.D., Jiang X, R.M. und Priese L, S.F., 2009. Color Image segmentation using an enhanced gradient network method: Pattern Recognition Letters, 30(15), S. 1404–1412.
• Jiang, X., Hsu, -C, J., Rothaus, K., Menning, M., Chen und -F, Y., 2009. Chest X-Ray Based Tumor Growth Assessment for Lung Tumor Diagnosis: Transactions on Mass-Data Analysis of Images and Signals with Applications in Medicine, Biotechnology, Chemistry and Food Industry, 1(1), S. 27–37.
• Xiao, J., He, C. und Jiang, X., 2009. Structure identifcation by Bayesian classifers based on GMDH: Knowledge-Based Systems, 22(6), S. 461–470.
• Rothaus, K., Rhiem, P. und Jiang, X., 2009. Separation of the retinal vascular graph in arteries and veins upon structural knowledge: Image and Vision Computing, 27(7), S. 864–875.
• J, M., T, K., A, S., S, Z., Peitgen, H, O. und X, J., 2009. Matching of tree structures for registration of medical images: Image and Vision Computing, 27(7), S. 923–933.
• Schmeing, M. und Jiang, X., 2009. Robust Background Subtraction for Depth Map Generation. In: Proceedings of 3D Stereo MEDIA 2009
• Jiang, X. und Petkov, N. Hrsg., 2009. LNCS, Band 5702: Computer Analysis of Images and Patterns. Springer VDI Verlag.
• Wachenfeld, S., Broelemann, K., Jiang, X. und Krüger, A., 2009. Graph-based registration of partial images of city maps using geometric hashing. doi: 10.1007/978-3-642-02124-4_10.
• Dawood, M., Büther, F., Stegger, L., Jiang, X., Schober, O., Schäfers, M. und Schäfers, KP., 2009. Optimal number of respiratory gates in positron emission tomography: a cardiac patient study.: Medical Physics, 36(5), S. 1775–1784. doi: 10.1118/1.3112422.
• Franek, L. und Jiang, X., 2009. An instability problem region growing segmentation algorithms and its set median solution. Proc. of 5th Int. Symposium on Visual Computing. Las Vegas.
• Gigengack, F. und Jiang, X., 2009. Improved uncalibrated view synthesis by extended positioning of virtual cameras and image quality optimization. Proc. of ACCV. Xian.
• Rothaus, K., Roters, J. und Jiang, X., 2009. Localization of pedestrian lights on mobile devices. Proc. Of APSIPA Annual Summit and Conference. Sapporo, Japan.
• Chen, Y, .-F., Huang, Y, .-F., X, J., Hsu, Y, .-N., Lin und H, .-H., 2009. Design of clinical support systems using integrated genetic algorithm and support vector machine. 13th Int. Conf. on Computer Analysis of Images and Patterns.
• Cheng, .-C.D., Schmidt-Trucksäss A, L. und -H S, J.X., 2009. Improved arterial inner wall detection using generalized median computation. 13th Int. Conf. on Computer Analysis of Images and patterns.
• Grosse, A., Rothaus, K. und Jiang, X., 2009. Detection of non-convex objects by dynamic programming. 13th Int. Conf. on Computer Analysis of Images and Patterns.
• Abdala, D, D. und X, J., 2009. An evidence accumulation approach to constrained clustering combination. 6th Int. Conf. on Machine Learning and Data Mining in Pattern Recognition.
• Wachenfeld, S., Broelemann, K., Jiang, X. und Küger, A., 2009. Graph-based registration of partial images of city maps using geometric hashing. Proc. Of GbR. Venice.
• Fieseler, M. und Jiang, X., 2009. Registration of depth and video data in depth image based rendering. 3DTV Conference. Potsdam.

2008

• Dawood, M., 2008. Respiratory Motion Correction on 3D Positron Emission Tomography Images. Dissertationsschrift. Universität Münster
• Dawood, M., Büther, F., Lang, N., Jiang, X. und Schäfers, K., 2008. Respiratory motion correction in 3D PET/CT with advanced optical flow algorithms: IEEE Transactions on Medical Imaging, 27(8), S. 1164–1175.
• Cheng, D. und Jiang, X., 2008. Detection of arterial wall in sonographic artery images using dual dynamic programming: IEEE Transactions on Information Technology in Biomedicine, 12(6), S. 792–799.
• Wachenfeld, S., Lohe, T., Fieseler, M. und Jiang, X., 2008. DocXS Distributed operator construction and execution system: Pattern Recognition and Image Analysis, 18(2), S. 328–331.
• Kriegbaum-Stehberger B, J.X. und Mojon, S.D., 2008. Performance of a new, 3D-monitor based random-dot stereotest for children under 4 years of age: Graefes Archive for Clinical and Experimental Ophthalmology, 246(1), S. 1–7.
• Dawood, M., Kösters, T., Fieseler, M., Büther, F., Jiang, X., Wübbeling, F. und Schäfers, KP., 2008. Motion correction in respiratory gated cardiac PET/CT using multi-scale optical flow. doi: 10.1007/978-3-540-85990-1_19.
• Jiang, X., Chen und -F, Y., 2008. Facial image processing. In: Applied Pattern Recognition . Springer VDI Verlag. S. 29–48.
• Wattuya, P. und Jiang, X., 2008. Ensemble combination for solving the parameter selection problem in image segmentation. Proc. of SSPR. Orlando.
• Rothaus, K. und Jiang, X., 2008. Constrained clustering by a novel graph-based distance transformation. Proc. of ICPR. Tampa, Florida.
• Wachenfeld, S., Terlunen, S. und Jiang, X., 2008. Robust recognition of 1-D barcodes using camera phones. Proc. of ICPR. Tampa, Florida.
• Rothaus, S., Rothaus, K. und Jiang, X., 2008. Synthesizing 3D videos by a motion-conditioned background mosaic. Proc. of ICPR. Tampa, Florida.
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