2023

• Jarvers C, Neumann H: Shape-selective processing in deep networks: integrating the evidence on perceptual integration. Frontiers in Computer Science 5: 1113609, 2023; doi: 10.3389/fcomp.2023.1113609 (PDF)
• Hampiholi B, Jarvers C, Mader W, Neumann H: Convolutional Transformer fusion blocks for multi-modal gesture recognition. IEEE Access 11, 34094-34103, 2023; doi:10.1109/ACCESS.2023.3263812 (PDF)
• Schmid D, Oess T, Neumann H: Listen to the brain - auditory sound source localization in neuromorphic computing architectures. Sensors 23, 4451, 2023; doi:10.3390/s23094451 (PDF)
• Schmid D, Jarvers C, Neumann H: Canonical circuit computations for computer vision. Biological Cybernetics , e-print, 2023; doi:10.1007/s00422-023-00966-9 (PDF)

2022

• Adrian DB, Kupcsik AG, Spies M, Neumann H: Efficient and robust training of dense object nets for multi-object robot manipulation. 2022 IEEE Int'l Conf. on Robotics and Automation, ICRA'22, Philadelphia, PA, USA, 23-27 May, 2022 (PDF)
• Schmid D, Neumann H: Task-dependent incremental binding explainedby cortioc-thalamo-cortical interactions - a neuro-dynamical model of mental contour tracing. 2022 Conf. Cognitive Computational Neuroscience, CCN'22, San Francisco, CA, USA; doi: 10.32470/CCN.2022.1125-0 (PDF)
• Graf C, Adrian DB, Weil J, Gabriel M, Schillinger P, Spies M, Neumann H, Kupcsik AG: Learning dense visual descriptors using image augmentations for robot manipulation tasks. 6th Conf. on Robotic Learning, CoRL'22, Auckland, New Zealand, 14-18 Dec., 2022 (PDF)
• Winter M, Neumann H, Pryss R, Probs T, Reichert M: Defininig gaze patterns for process model literacy - exploring visual routines in process models with diverse mappings. Expert Systems With Applications , 2022; (PDF)

2021

• Thiam P, Kessler V, Amirian M, Bellmann P, Layher G, Zhang Y, Velana M, Gruss S, Walter S, Traue HC, Schork D, Kim J, Andre e, Neumann H, Schwenker F: Multi-modal pain intensity recognition based on the SenseEmotion Database. IEEE Trans. on Affective Computing 12(3), 743-760, 2021; doi:10.1109/TAFFC.2019.2892090 (PDF)
• Oess T, Neumann H: Brain-inspired visual-auditory integration yieding near optimal performance - modelling and neuromorphic algorithms. ERCIM News 125 April, 32-33, 2021 (PDF)

2020

• Oess T, Neumann H, Ernst MO: Binaural signal integration improves vertical sound source localization. bioRxiv; doi: https://doi.org/10.1101/2020.09.10.291468; Sept. 11, 2020 (PDF)
• Müller A, Lausser L, Wilhelm A, Ropinski T, Platzer M, Neumann H, Kestler HA: A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data. Advances in Data Analysis and Classification , 2020; doi:10.1007/s11634-020-00416-5 (PDF)
• Oess T, Löhr M, Jarvers C, Schmid D, Neumann H: A bio-inspired model of sound source localization on neuromorphic hardware. 2nd IEEE Int'l Conf. on Artificial Intelligence Circuits and Systems, AICAS'20, Genoa, Italy, 31 Aug.-2 Sept., 2020 (PDF)
• Löhr M, Jarvers C, Neumann H: Complex Neuron Dynamics on the IBM TrueNorth Neurosynaptic System. 2nd IEEE Int'l Conf. on Artificial Intelligence Circuits and Systems, AICAS'20, Genoa, Italy, 31 Aug.-2 Sept., 2020 (PDF)
• Oess T, Ernst MO, Neumann H: Computational principles of neural adaptation for binaural signal integration. PLoS Computational Biology 16(7): e1008020, 2020; doi:10.1371/journal.pcbi.1008020 (PDF)
• Schmid D, Jarvers C, Neumann H: Learning visual contour tracing in a deep recurrent network based on a cortical columnar architecture. Annual Meeting of the Vision Sciences Society (VSS'20), Virtual, June 19-24, 2020.
• Zhang Y, Hassan M, Neumann H, Black MJ, Tang S: Generating 3D people in scenes without people. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR 2020, in print (PDF)
• Oess T, Löhr MPR, Schmid D, Ernst MO, Neumann H: From near-optimal Bayesian integration to neuromorphic hardware: a neural network model of multisensory integration. Frontiers in Neurorobotics 14, article 29, 2020; doi: 10.3389/fnbot.2020.00029 (PDF)
• Bäuerle A, Neumann H, Ropinski T: Classifier-guided visual correction of noisy labels for image classification tasks. Computer Graphics Forum 39(3): 1-14, 2020 (Eurographics Conf on Visualization (EuroVis) 2020, Gleicher M, Landesberger von Antburg T, Viola I (guest eds)) (PDF)

2019

• Thiam P, Kessler V, Amirian M, Bellmann P, Layher G, Zhang Y, Velana M, Gruss S, Walter S, Traue HC, Schork D, Kim J, Andre E, Neumann H, Schwenker F: “Multi-modal pain intensity recognition based on the SenseEmotion database. IEEE Transactions on Affective Computing , 2019, accepted
• Oess T, Ernst MO, Neumann H: Computational principles of neural adaptation for binaural signal integration. bioRxiv; doi: https://doi.org/10.1101/863258 (PDF)
• Jarvers C, Neumann H: Incorporating feedback in convolutional neural networks. 2019 Conf. Cognitive Computational Neuroscience, CCN'19, Berlin, Germany, September 13-16, 2019; doi: https://doi.org/10.32470/CCN.2019.1191-0 (PDF)
• Schmid D, Löhr M, Neumann H: Perceptual motion illusions as a tool to probe neural mechanisms of motion integration in the V1-MT-MSTl feedforward-feedback system. 2019 Conf. Cognitive Computational Neuroscience, CCN'19, Berlin, Germany, September 13-16, 2019; doi: https://doi.org/10.32470/CCN.2019.1413-0 (PDF)
• Oess T, Ernst MO, Neumann H: Computational investigation of visually guided learning of spatially aligned auditory maps in the colliculus. Int'l Symp. on Auditory and Audiological Research, ISAAR'19, Auditory Learning in Biological and Artificial Systems, Nyborg, Denmark, August 21-23, 2019 (PDF)
• Jarvers C, Schmid D, Neumann H: Temporal learning of dynamics in complex neuron models using backpropagation. Int'l Joint Conf. on Neural Networks, IJCNN'19, Budapest, Hungary, July 14-19, 2019 (PDF)
• Löhr M, Schmid D, Neumann H: Motion integration and disambiguation by spiking V1-MT-MSTl feedforward-feedback interaction. Int'l Joint Conf. on Neural Networks, IJCNN'19, Budapest, Hungary, July 14-19, 2019 (PDF)
• Zhang Y, Tang S, Muandet K, Jarvers C, Neumann H: Local temporal bilinear pooling for fine-grained action parsing. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR 2019, June 16-20, 2019, Long Beach, CA, USA (PDF)
• Habtegiorgis SW, Jarvers C, Rifai K, Neumann H, Wahl S: The role of bottom-up and top-down cortical interactions in adaptation to natural scene statistics. Frontiers in Neural Circuits 13, article 9, 2019; doi: 10.3389/fncir.2019.00009 (PDF)

2018

• Layher G, Neumann H: Points and stripes: a novel technique for masking biological motion point-light stimuli. Frontiers in Psychology 9, article 1455, 2018; doi: 10.3389/fpsyg.2018.01455 (PDF)
• Zhang Y, Neumann H: An empirical study towards understanding how deep convolutional nets recognize falls. 15th European Conf. on Computer Vision, ECCV'18, Munich, Germany, Sept. 8-14, 2018; 6th Workshop on Assistive Computer Vision and Robotics, ACVR 2018, Munich, Germany, Sept. 9, 2018; http://iplab.dmi.unict.it/acvr2018/ (PDF, final version upon request)
• Zhang Y, Tang S, Sun H, Neumann H: Human motion parsing by hierarchical dynamic clustering. 29th British Machine Vision Conf., BMVC'18, Newcastle upon Tyne, UK, Sept. 3-6, 2018 (PDF)
• Bäuerle A, Neumann H, Ropinski T: Training de-confusion: an interactive, network-supported visual analysis system for resolving errors in image classification training data. arXiv cs.AI, Aug. 2018; http://arxiv.org/abd/1808.03114 (PDF)
• Saeed A, Al-Hamadi A, Neumann H: Facial point localization via neural networks in a cascade regression framework. Multimedia Tools & Applications 77, 2261–2283, 2018; doi: 10.1007/s11042-016-4261-x (PDF)
• Zimoch M, Pryss R, Layher G, Neumann H, Probst T, Schlee W, Reichert M: Utilizing the capabilities offered by eye-tracking to foster novices' comprehension of business process models. In: J Xiao et al. (eds.), IEEE Int'l Conf. on Cognitive Computing (ICCC 2018), LNCS 10971, Springer, 2018, pp.155-163. (PDF)
• Zhang Y, Sun H, Tang S, Neumann H: Temporal human action segmentation via dynamic clustering. arXiv cs.AI, Mar. 2018; https://arxiv.org/abs/1803.05790 (PDF)
• Löhr M, Neumann H: Contrast detection in event-streams from dynamic vision sensors with fixational eye movements. IEEE Int'l Symp. on Circuits and Systems, ISCAS'18, Florence, Italy, May 27-30, 2018; doi:10.1109/ISCAS.2018.8351084 (PDF)

2017

• Jarvers C, Neumann H: Adaptive dynamic network architectures for companion systems. IEEE Int'l Conf. on Companion Technology, ICCT'17, Ulm, Germany, Sept. 11-13, 2017; doi:10.1109/COMPANION.2017.8287081 (PDF)
• Layher G, Brosch T, Neumann H: Real-time biologically inspired action recognition from key poses using a neuromorphic architecture. Frontiers in Neurorobotics 11, article 13, 2017; doi: 10.3389/fnbot.2017.00013 (PDF)
• Zhang Y, Layher G, Neumann H: Continuous activity understanding based on accumulative pose-context visual patterns. IEEE 7th Int'l Conf. on Image Processing Theory, Tools, and Applications (IPTA 2017), Nov. 28-Dec. 1, Montreal, QC, Canada; doi: 10.1109/IPTA.2017.8310114. (PDF)
• Saeed A, Al-Hamadi A, Neumann H: Facial point localization via neural networks in a cascade regression framework. Multimedia Tools and Applications , 1573-7721, 2017; doi="10.1007/s11042-016-4261-x
• Riemer V, Fromme J, Layher G, Neumann H, Schrader C: Identifying features of bodily expression as indicators of emotional experience during multimedia learning. Frontiers in Psychology 8, article 1303, 2017; doi: 10.3389/fpsyg.2017.01303 (PDF)
• König D, Adam M, Jarvers C, Layher G, Neumann H, Teutsch M: Fully convolutional region proposal networks for multispectral person detection. 30th IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'17, Honolulu, Hawaii, USA, July 21-30, 2017; 13th IEEE Workshop on Perception Beyond the Visual Spectrum, PBVS 2017, Honolulu, Hawaii, USA, July 21, 2017; doi:10.1109/CVPRW.2017.36 (PDF)
• Habtegiorgis SW, Jarvers C, Rifai K, Neumann H, Wahl S: Evaluation of a distortion induced motion aftereffect - psychophysics and modelling. Perception (Suppl., 40th ECVP): (in print), 2017.
• Velana M, Gruss S, Layher G, Thiam P, Zhang Y, Schork D, Kessler V, Meudt S, Neumann H, Kim J, Schwenker F, Andre E, Traue HC, Walter S: The SenseEmotion database: a multimodal database for the development and systematic validation of an automatic pain and emotion-recognition system. In: F Schwenker, S Scherer (eds.), Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2017, LNAI 10183, Springer, pp.127–139, 2017 (PDF)
• Zhang Y, Layher G, Walter S, Kessler V, Neumann H: Visual confusion recognition in movement patterns from walking path and motion energy. In: M. Mokhtari et al. (eds.), 15th Int'l Conf. on Smart Homes and Health Telematics (ICOST 2017), LNCS 10461, Springer, 2017, pp.124-135. (PDF)
• Layher G, Glodek M, Neumann H: Analysis of articulated motion for social signal processing. In: Companion Technology - A Paradigm Shift in Human-Technology Interaction, S Biundo, A Wendemuth (eds.), Springer, Cham, 2017, 345-364.
• Niese R, Al-Hamadi A, Neumann H: Automated analysis of head pose, facial expression and affect. In: Companion Technology - A Paradigm Shift in Human-Technology Interaction, S Biundo, A Wendemuth (eds.), Springer, Cham, 2017, 365-386.

2016

• Al-Hamadi A, Saeed A, Niese R, Handrich S, Neumann H: Emotional trace: mapping of facial expression to valence-arousal space. British J of Applied Science & Technology 16(6): 1-14, 2016, Article no.BJAST.27294. (PDF)
• Brosch T, Neumann H: Event-based optical flow on neuromorphic hardware. Proc. 9th Int'l Conf. on Bio-inspired Information and Communication Technologies, BICT 2015, Dec. 3-5, New York City, NY, USA, Copyright © 2016 ICST; doi:10.4108/eai.3-12-2015.2262447 (PDF)
• Layher G, Brosch T, Neumann H: Towards a mesoscopic-level canonical circuit definition for visual cortical processing. Proc. 9th Int'l Conf. on Bio-inspired Information and Communication Technologies, BICT 2015, Dec. 3-5, New York City, NY, USA, Copyright © 2016 ICST; doi:10.4108/eai.3-12-2015.2262447 (PDF)
• Medathati NVK, Neumann H, Masson GS, Kornprobst P: Bio-inspired computer vision: towards a synergistic approach of artificial and biological vision. Computer Vision and Image Understanding 150: 1-30, 2016 (PDF)
• Gomez O, Neumann H: Biologically inspired model for inference of 3D shape from texture. PLoS ONE 11(9): e0160868, 2016; doi:10.1371/journal.pone.0160868 (PDF)
• Geier T, Glodek M, Layher G, Neumann H, Biundo S, Palm G: On stacking probabilistic temporal models with bidirectional information flow. JMLR Workshop and Conf. Proc., Vol. 52: Proc. 8th Int'l Conf. on Probabilistic Graphical Models (PGM'16), A Antonucci, G Corani, C Polpo de Campos (eds.), pp.195-206 (PDF)
• Brosch T, Tschechne S, Neumann H: Visual processing in cortical architecture from neuroscience to neuromorphic computing. In: K. Amunts, L. Grandinetti, T. Lippert, N. Petkov (eds.), Proc. 2nd Int'l Workshop on Brain-Inspired Computing (BrainComp 2015), Cetraro, Italy, July 6–10, 2015, LNCS 10087, Springer, 2016. (PDF, final version upon request)
• Jarvers C, Brosch T, Brechmann A, Woldeit M, Schulz AL, Ohl FW, Lommerzheim M, Neumann H: Reversal learning in humans and gerbils: dynamic control network facilitates learning. Frontiers in Neuroscience 10, article 535, 2016; doi: 10.3389/fnins.2016.00535 (PDF)

2015

• Abdul-Kreem LI, Neumann H: Bio-inspired model for motion estimation using an address-event representation. Proc. 10th Int'l. Conf. on Vision Theory and Application, VISAPP 2015, Proc. 10th Int'l. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2015; March 11-14, Berlin, Germany, 2015
• Abdul-Kreem LI, Neumann H: Neural mechanisms of cortical motion computation based on a neuromorphic sensory system. PLoS ONE 10(11): e0142488, 2015; doi:10.1371/journal.pone.0142488 (PDF)
• Brosch T, Tschechne S, Neumann H: On event-based optical flow detection. Frontiers in Neuroscience 9, article 137, 2015; doi: 10.3389/fnins.2015.00137 (PDF)
• Brosch T, Neumann H, Roelfsema PR: Reinforcement learning of linking and tracing contours in recurrent neural networks. PLoS Computational Biology 11(10): e1004489, 2015; doi:10.1371/journal.pcbi.1004489 (PDF)
• Brosch T, Neumann H: Event-based optical flow on neuromorphic hardware. Proc. 9th Int'l Conf. on Bio-inspired Information and Communication Technologies, BICT 2015, 1st Int'l Workshop on Computational Models of the Visual Cortex: Hierarchies, Layers, Sparsity, Saliency and Attention (CMVC'15), Dec.3-5, New York, NY, USA, ACM digital library, 2015 (PDF)
• Glodek M, Layher G, Heilemann F, Gawrilowicz F, Palm G, Schwenker F, Neumann H: uulmMAD – a human action recognition dataset for ground-truth evaluation and investigation of view invariances. In: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS'14, LNAI 8869, 77–91, 2015
• Layher G, Tschechne S, Niese R, Al-Hamadi A, Neumann H: Towards the separation of rigid and non-rigid motions for facial expression analysis. Proc. Int'l Conf. on Intelligent Environments (IE'15), July 15-17, Prague, Czech Republic, pp.176-179, IEEE, 2015 (PDF)
• Layher G, Brosch T, Neumann H: Towards a mesoscopic-level canonical circuit definition for visual cortical processing. Proc. 9th Int'l Conf. on Bio-inspired Information and Communication Technologies, BICT 2015, 1st Int'l Workshop on Computational Models of the Visual Cortex: Hierarchies, Layers, Sparsity, Saliency and Attention (CMVC'15), Dec.3-5, New York, NY, USA, ACM digital library, 2015 (PDF)
• Rodriguez-Sanchez A, Neumann H, Piater J: Beyond simple and complex neurons: Towards intermediate-level representations of shapes and objects. KI - Künstliche Intelligenz 29(1): 19-29. (PDF)
• Schrodt F, Layher G, Neumann H, Butz MV: Embodied learning of a generative neural model for biological motion perception and inference. Frontiers in Computational Neuroscience 9: article 79, 2015; doi: 10.3389/fncom.2015.00079 (PDF)

2014

• Belardinelli A, Kurz JM, Kutter EF, Neumann H, Karnath H-O, Butz MV: Modeling simultanagnosia. In: P Bello, M Guarini, M McShane, B Scassellati (eds.), Proc. 36th Annual Conf. of the Cognitive Science Society (CogSci 2014), Quebec City, Canada, 23-26 July, Cognitive Science Society, pp.1911-1916. (PDF)
• Brosch T, Neumann H: Interaction of feedforward and feedback streams in visual cortex in a firing-rate model of columnar computation. Neural Networks 54: 11-16, 2014. (PDF, supplementary material: PDF)
• Brosch T, Neumann H: Computing with a canonical neural circuits model with pool normalization and modulating feedback. Neural Computation 26: 2735–2789, 2014. (PDF)
• Hochdorfer S, Neumann H, Schlegel C: Landmark rating and selection for SLAM in dynamic environments. In: Proc. Int'l Conf. on Intelligent Autonomous Systems (IAS14), July 15-18, 2014, Padova, Italy
• Layher G, Giese MA, Neumann H: Learning representations of animated motion sequences - a neural model. Topics in Cognitive Science 6: 170-182, 2014. doi:10.1111/tops.12075 (PDF document upon request)
• Layher G, Schrodt F, Butz MV, Neumann H: Adaptive learning in a compartmental model of visual cortex - how feedback enables stable category learning and refinement. Frontiers in Psychology 5, article 1287: 1-19, 2014. (PDF)
• Paul J, Divkovic E, Wundrak S, Bernhardt P, Rottbauer W, , Neumann H, Rasche V: High-resolution respiratory self-gated golden angle cardiac MRI: Comparison of self-gating methods in combination with k-t SPARSE SENSE. Magnetic Resonance in Medicine , 2014 (in press). doi:10.1002/mrm.25102
• Schroth F, Layher G, Neumann H, Butz MV: Modeling perspective-taking by correlating visual and proprioceptive dynamics. In: P Bello, M Guarini, M McShane, B Scassellati (eds.), Proc. 36th Annual Conf. of the Cognitive Science Society (CogSci 2014), Quebec City, Canada, 23-26 July, Cognitive Science Society, pp.1383-1388. (PDF)
• Schrodt F, Layher G, Neumann H, Butz MV: Modeling perspective-taking upon observation of 3D biological motion. In: Proc. 4th Joint IEEE Int'l Conf. on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2014, Oct.13-16, Palazzo Ducale, Genoa, Italy (PDF)
• Glodek M, Layher G, Heilemann F, Gawrilowicz F, Palm G, Schwenker F, Neumann H: uulmMAD – a Human action recognition dataset for ground-truth evaluation and investigation of view invariances. In: F Schwenker, S Scherer, L-P Morency (eds.), Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2014, LNAI 8869, Springer, pp. 77–91, 2014 (PDF)
• Tschechne S, Neumann H: Hierarchical representation of shapes in visual cortex - from localized features to figural shape segregation. Frontiers in Computational Neuroscience 8, article 93: 1-20, 2014. (PDF) (corrected version)
• Tschechne S, Sailer R, Neumann H: Bio-inspired optic flow from event-based neuromorphic sensor input. In: N El-Gayar et al. (eds.), Proc. 6th IAPR TC 3 Int'l Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2014), Montreal, QC, Canada, Oct.6–8, LNCS 8774, Springer, 2014. (PDF, final version upon request)
• Tschechne S, Brosch T, Sailer R, von Egloffstein N, Abdul-Kreem L, Neumann H: On event-based motion detection and integration. Proc. 8th Int'l Conf. on Bio-inspired Information and Communication Technologies, BICT 2014, Dec.1-3, Boston, MA, USA, ACM digital library, 2014 (PDF)

2013

• Brosch T, Schwenker F, Neumann H: Attention-gated reinforcement learning in neural networks — a unified view. In: V. Mladenov et al. (eds.), Proc. 23rd Int'l Conf. on Artificial Neural Networks (ICANN 2013), LNCS 8131, Springer, 2013, pp.272–279. (PDF)
• Layher G, Giese MA, Neumann H: Learning representations of animated motion sequences - a neural model. In: M Knauff, M Pauen, N Sebanz, I Wachsmuth (eds.), Proc. 35th Annual Conf. of the Cognitive Science Society (CogSci 2013), Berlin, Germany, 31 July-3 August, Cognitive Science Society, pp.870-875. (PDF, awarded for the computational modeling prize on Perception/Action)
• Lindauer E, Dupois L, Müller H-P, Neumann H, Ludolph AC, Kassubek J: Adipose tissue distribution predicts survival in amyotrophic lateral sclerosis. PLoS ONE 8(6): e67783. doi:10.1371/journal.pone.0067783 (PDF)
• Raudies F, Neumann H: Modeling heading and path perception from optical flow in the case of independently moving objects. Frontiers in Behavioral Neuroscience 7 Article 23: 1-19, 2013. doi:10.3389/fnbeh.2013.00023 (PDF)
• Raudies F, Neumann H: Modeling binocular and motion transparency processing by local center-surround interactions. In: Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts, Ch.6, M Pomplun, J Suzuki (eds.), IGI Publ., 2013, 121-153. (preprint PDF)
• Raudies F, Ringbauer S, Neumann H: A bio-inspired, computational model suggests velocity gradients of optic flow locally encode ordinal depth at surface borders and globally they encode self-motion. Neural Computation 25(9): 2421-2449, 2013 (PDF)
• Schels M, Glodek M, Meudt S, Scherer S, Schmidt M, Layher G, Tschechne S, Brosch T, Hrabal D, Walter S, Palm G, Neumann H, Traue H, Schwenker F: Multi-modal classifier-fusion for the recognition of emotions. In: Coverbal Synchrony in Human-Machine Interaction, Ch.4, M Rojc, N Campbell (eds.), CRC Press, 2013, 73-98.
• Tschechne S, Layher G, Neumann H: A biologically inspired model for the detection of external and internal head motions. In: V. Mladenov et al. (eds.), Proc. 23rd Int'l Conf. on Artificial Neural Networks (ICANN 2013), LNCS 8131, Springer, 2013. (PDF)

2012

• Brosch T, Neumann H: The combination of HMAX and HOGs in an attention guided framework for object localization. Proc. 1st Int'l Conf. on Pattern Recognition Applications and Methods, ICPRAM-11, Vol.2, Feb. 6-8, 2012, Vilamoura, Algarve, Portugal, pp.281-288. (PDF)
• Brosch T, Neumann H: The brain's sequential parallelism: Perceptual decision-making and early sensory responses. In: T Huang et al. (eds.), Proc. 19th Int'l Conf. on Neural Information Processing (ICONIP 2012), Part II, LNCS 7664, Springer, 2012, pp.41-50. (PDF)
• Hopfensitz M, Müssel C, Wawra C, Maucher M, Kühl M, Neumann H, Kestler HA: Multiscale binarization of gene expression data for reconstructing Boolean networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(2): 487-498, 2012. (PDF)
• Layher G, Giese MA, Neumann H: Learning representations for animated motion sequence and implied motion recognition. In: AEP Villa et al. (eds.), Int'l Conf. on Artificial Neural Networks (ICANN 2012), Part I, LNCS 7552, Springer, 2012, pp.288–295. (PDF)
• Neumann H, Raudies F: Neural mechanisms for form and motion detection and integration: Biology meets machine vision. In: A Fusiello et al. (eds.), European Conf. on Computer Vision (ECCV 2012) Ws/Demos, Part I, LNCS 7583, Springer, 2012, pp.468-473. (PDF)
• Niese R, Al-Hamadi A, Farag A, Neumann H, Michaelis B: Facial expression recognition based on geometric and optical flow features in colour image sequences. IET Computer Vision 6(2): 79-89, 2012. doi: 10.1049/iet-cvi.2011.0064 (PDF)
• Poort J, Raudies F, Wanning A, Lamme VAF, Neumann H, Roelfsema PR: The role of attention in figure-ground segregation in areas V1 and V4 of the visuel cortex. Neuron 75: 143-156, 2012. (PDF)
• Raudies F, Neumann H: A review and evaluation of methods estimating ego-motion. Computer Vision and Image Understanding 116: 606-633, 2012. (PDF)
• Raudies F, Mingolla E, Neumann H: Active gaze control improves optic flow-based segmentation and steering. PLoS ONE 7(6): e38446, 2012. doi:10.1371/journal.pone.0038446 (PDF)
• Raudies F, Neumann H: A bio-inspired, motion-based analysis of crowd behavior attributes relevance to motion transparency, velocity gradients, and motion patterns. PLoS ONE 7(12): e53456, 2012. doi:10.1371/journal.pone.0053456 (PDF)
• Scherer S, Layher G, Kane J, Neumann H, Campbell N: An audiovisual political speech analysis incorporating eye-tracking and perception data. Proc. 8th ELRA Conf. on Language Resources and Evaluation, LREC 2012, May 23-25, 2012, Istanbul, Turkye, pp.1114-1120. (PDF)
• Scherer S, Glodek M, Layher G, Schels M, Schmidt M, Brosch T, Tschechne S, Schwenker F, Neumann H, Palm G: A generic framework for the inference of user states in human computer interaction - How patterns of low level behavioral cues support complex user states in HCI. J Multimodal User Interfaces : e-version, DOI 10.1007/s12193-012-0093-9 (PDF)

2011

• Beck C, Neumann H: Combining feature selection and integration - A neural model for MT motion selectivity. PLoS ONE 6(7): e21254, 2011. doi:10.1371/journal.pone.0021254 (PDF)
• Bouecke JD, Tlapale E, Kornprobst P, Neumann H: Neural mechanisms of motion detection, integration, and segregation: From biology to artificial image processing systems. EURASIP Journal on Advances in Signal Processing Vol.2011, Article ID 781561, 22 pages (doi:10.1155/2011/781561). (PDF)
• Endres D, Neumann H, Kolesnik M, Giese MA: Hooligan detection: the effects of saliency and expert knowledge. Proc. 4th Int'l Conf. on Imaging for Crime Detection and Prevention, ICDP-11, Nov. 3-4, 2011, London, UK. (PDF, best paper award)
• Glodek M, Tschechne S, Layher G, Schels M, Brosch T, Scherer S, Kächele M, Schmidt M, Neumann H, Palm G, Schwenker F: Multiple classifier systems for the classification of audio-visual emotional states. In: S D'Mello et al. (eds.). Affective Computing and Intelligent Interaction (ACII 2011), Part II, LNCS 6975, Springer, 2011, pp.359-368. (PDF)
• Layher G, Liebau H, Niese R, Al-Hamadi A, Michaelis B, Neumann H: Robust stereoscopic head pose estimation in human-computer interaction and a unified evaluation framework. in: G Maino, GL Foresti (eds.), Image Analysis and Processing (ICIAP 2011), Part I, LNCS 6978, Springer, 2011, pp.227-236. (PDF)
• Layher G, Neumann H, Scherer S, Tschechne S, Brosch T, Curio C: Social signal processing in companion systems: Challenges ahead. In: Informatik 2011 - Informatik schafft Communities, H.-U. Heiß, P. Pepper, H. Schlingloff, J. Schneider (Hrsg.), 41. Jahrestagung der GI, Workshop "Companion-Systeme und Mensch-Companion-Interaktion", 4.-7.10.2011, TU Berlin, LNI P-192, Springer. (PDF)
• Müller H-P, Raudies F, Unrath A, Neumann H, Ludolph AC, Kassubek J: Quantification of human body fat tissue percentage by MRI. NMR Biomedicine 24: 17-24, 2011. (PDF)
• Huth J, Buchholz M, Kraus JM, Mølhaved K, Gradinarud C, v. Wichert G, Gress TM, Neumann H, Kestler HA: TimeLapseAnalyzer: Multi-target analysis for live-cell imaging and time-lapse microscopy Computer Methods and Programs in Biomedicine 104(2): 227-234, 2011.
• Raudies F, Mingolla E, Neumann H: A neural model for transparent motion processing. Neural Computation 23: 2868-2914, 2011. (PDF)
• Ringbauer S, Tschechne S, Neumann H: Mechanisms of adaptive spatial integration in a neural model of cortical motion processing. In: A Dobnikar, U Lotric, B Ster (eds.), Adaptive and Natural Computing Algorithms (ICANNGA 2011), Part I, LNCS 6593, Springer, 2011, pp.110-119. (PDF)

2010

• Beck C, Neumann H: Interactions of motion and form in visual cortex - A neural model. Journal of Physiology Paris 104: 61-70, 2010. (PDF)
• Müller HP, Raudies F, Unrath A, Neumann H, Ludolph AC, Kassubek J: Quantification of human body fat tissue percentage by MRI. NMR in Biomedicine, 2010 (www.interscience.wiley.com) DOI:10.1002/nbm.1549. (PDF)
• Panning A, Al-Hamadi A, Michaelis B, Neumann H: Colored and anchored active shape models for tracking and form description of the facial features under image-specific disturbances. Proc. IEEE 5th Int.'l Symp. on Image/Video Communication over Fixed and Mobile Networks, ISIVC'10, Sept.30-Oct.2, 2010, Rabat, Marocco, doi: 10.1109/ISVC.2010.5656197. (PDF)
• Raudies F, Neumann H: A neural model of figure-ground segregation in motion perception. Neural Networks 23: 160-176, 2010. (PDF)
• Raudies F, Neumann H: A model of neural mechanisms in monocular transparent motion perception. Journal of Physiology Paris 104: 71-83, 2010. (PDF)
• Tlapale E, Kornprobst P, Bouecke J, Neumann H, Masson GS: Towards a bio-inspired evaluation methodology for motion estimation models. INRIA RR No.7317 (June), 2010. (PDF)
• Tlapale E, Kornprobst P, Masson GS, Faugeras O, Bouecke JD, Neumann H: Bio-inspired motion estimation - From modelling to evaluation, can biology be a source of inspiration? INRIA RR No.7447 (November), 2010. (PDF)
• Tlapale E, Kornprobst P, Masson GS, Faugeras O, Bouecke JD, Neumann H: Evaluating motion estimation models from behavioural and psychophysical data. In: J Suzuki, T Nakano (eds.). Bioinspired Models of Network, Information, and Computing Systems, BIONETICS'10, LNICST 87, Springer, 2012, pp.483-496. (PDF)

2009

• Allen HA, Humphreys GW, Colin J, Neumann H: Ventral extra-striate cortical areas are required for human visual texture segmentation. Journal of Vision 9(9): 2, 1-14, 2009. (PDF)
• Beck C, Olchese U, Montagner A, Ringbauer S, Neumann H, Frisoli A, Almeida R, Bergamasco M, Deco G: A neuroinspired cognitive behavioral control architecture for visually driven mobile robotics. Proc. 2008 IEEE Int'l Conf. on Robotics and Biomimetics (Robio 2008), Bangkok, Thailand, Feb.21-26, 2009, pp.11-20. (PDF)
• Cardanobile S, Cohen M, Corchs S, Mugnolo D, Neumann H: Investigation of input-output gain in dynamical systems for neural information processing. In: Mathematical Analysis of Evolution, Information, and Complexity, Ch.14, W Arendt, W Schleich (eds.), Wiley-VHC, Weinheim, 2009, 379-393. (paper proofs PDF, final version upon request)
• Janzer HS, Raudies S, Neumann H, Steiner F: Image processing and feature extraction from a perspective of computer vision and physical cosmology. In: Mathematical Analysis of Evolution, Information, and Complexity, Ch.10, W Arendt, W Schleich (eds.), Wiley-VHC, Weinheim, 2009, 273-310. (paper proofs PDF, final version upon request)
• Raudies F, Neumann H: An efficient linear method for the estimation of ego-motion from optical flow. In: J Denzler, G Notni, and H Süsse (eds.), Pattern recognition (Proc. 31st DAGM Symp., Jena, Sept.9-12, 2009), LNCS 5748, Springer, 2009, pp. 11-20. (preprint PDF, final version upon request)
• Weidenbacher U, Neumann H: Extraction of surface-related features in a recurrent model of V1-V2 interactions. PLoS ONE 4(6): e5909, 2009. (PDF)

2008

• Beck C, Thorpe S, Neumann H: Neural rank-order coding with spiking neurons for cortical motion detection and integration. Proc. Int'l. Conf. on Cognitive Systems, CogSys 2008, Karlsruhe, Germany, Apr. 2-4, 2008.
• Beck C, Neumann H: Interactions of motion and form in visual cortex - A neural model. In L.U. Perrinet, E. Dauce (eds.). Proc. 2nd French Conf. on Computational Neuroscience, NeuroComp 2008, Marseille, France, Oct. 8-11, 2008, pp.1-6. (PDF)
• Beck C, Ognibeni T, Neumann H: Object segmentation from motion discontinuities and temporal occlusions - a biologically inspired model. PLoS ONE 3(11): e3807, 2008. (PDF)
• Hansen T, Neumann H: A recurrent model of contour integration in primary visual cortex. Journal of Vision 8(8): 8, 1-25, 2008. (PDF)
• Raudies F, Neumann H: Biologically inspired attentive motion analysis for video surveillance. Proc. Int'l. Conf. on Computer Vision Theory and Applications, Funchal, Portugal, Jan. 22-25, 2008. (PDF)
• Raudies F, Neumann H: Neural model for the perception of form and implied motion. Proc. Int'l. Conf. on Cognitive Systems, CogSys 2008, Karlsruhe, Germany, Apr. 2-4, 2008.
• Raudies F, Bayerl P, Neumann H: A model of neural mechanisms in monocular transparent motion perception. In L.U. Perrinet, E. Dauce (eds.). Proc. 2nd French Conf. on Computational Neuroscience, NeuroComp 2008, Marseille, France, Oct. 8-11, 2008, pp.87-92. (PDF)
• Thielscher A, Neumann H: Globally consistent depth sorting of overlapping 2D surfaces in a model using local recurrent interactions. Biological Cybernetics , 2008, DOI 10.1007/s00422-008-0211-7. (PDF)
• Thielscher A, Kölle M, Neumann H, Spitzer M, Grön G: Texture segmentation in human perception: A combined modeling and fMRI study. Neuroscience 151: 730-736, 2008. (PDF)
• Weidenbacher U, Neumann H: Unsupervised learning of head pose through spike-timing dependent plasticity. in: E Andre, L Dybkjaer, W Minker, H Neumann, M Weber (eds.). Perception and Interactive Technologies (PIT'08), LNAI 5078, Springer, 2008, pp.123-131. (preprint PDF, final version upon request)

2007

• Bayerl P, Neumann H: A fast biologically inspired algorithm for recurrent motion estimation. IEEE Trans. on PAMI 29(2): 246-260, 2007. (PDF)
• Bayerl P, Neumann H: Disambiguating visual motion by form-motion interaction - a computational model. Int.'l Journal of Computer Vision 72: 27-45, 2007. (PDF)
• Bayerl P, Neumann H: A neural model of feature attention in motion perception. BioSystems 89: 208-215, 2007. (PDF)
• Beck C, Gottbehüt T, Neumann H: Integration of multiple temporal and spatial scales for robust optic flow estimation in a biologically inspired architecture. Proc. 12th Int'l Conf. on Computer Analysis of Images and Patterns, CAIP'07, LNCS, 53-60, Springer. (preprint PDF, final version upon request)
• Carranza N, Cristobal G, Bayerl P, Neumann H: Motion estimation of magnetic resonance cardiac images using the Wigner-Ville and Hough transforms. Optics and Spectroscopy 103(6): 877-885, 2007. (PDF)
• Fischer S, Bayerl P, Neumann H, Redondo R, Cristobal G: Iterated tensor voting and curvature improvement. Signal Processing 87: 2503-2515, 2007. (PDF)
• Neumann H, Yazdanbakhsh A, Mingolla E: Seeing surfaces: The brain's vision of the world. Physics of Life Reviews 4: 189-222, 2007. (PDF)
• Ringbauer S, Bayerl P, Neumann H: Neural mechanisms for mid-level optical flow pattern detection. Proc. Int'l Conf. on Artificial Neural Networks, ICANN'07, LNCS, Springer, 2007, pp.282-290. (preprint PDF, final version upon request)
• Thielscher A, Neumann, H: A computational model to link psychophysics and cortical cell activation patterns in human texture processing. Journal of Computational Neuroscience 22: 255-282, 2007. (PDF)
• Weidenbacher U, Layher G, Strauss P-M, Neumann H: A comprehensive head pose and gaze database. Proc. 3rd Int'l Conf. on Intelligent Environments, IE'07, Sept. 24-25, 2007, Ulm, Germany. (PDF)

2006

• Bausch T, Bayerl P, Neumann H: Multi-level face tracking for estimating human head orientation in video sequences. in: E Andre, L Dybkjaer, W Minker, H Neumann, M Weber (eds.). Perception and Interactive Technologies (PIT'06), LNAI 4021, Springer, 2006, pp.179-182. (PDF)
• Bayerl P, Neumann H: Feature attention in motion perception - a computational account. Journal of Vision 6(6), 513a, 2006.
• Beck C, Bayerl P, Neumann H: Optic flow integration at multiple spatial frequencies - Neural mechanism and algorithm. Int'l. Symp. on Visual Computing (ISVC'06), LNCS 4291, Springer, 741-750. (preprint PDF, final version upon request)
• Keil MS, Cristobal G, Neumann H: Gradient representation and perception in the early visual system - A novel account of Mach band formation. Vision Research 46: 2659-2674, 2006. (PDF)
• Mahler T, Bayerl P, Neumann H, Weber M: Visual attention in auditory display. in: E Andre, L Dybkjaer, W Minker, H Neumann, M Weber (eds.). Perception and Interactive Technologies (PIT'06), LNAI 4021, Springer, 2006, pp.65-72. (PDF)
• Weidenbacher U, Bayerl P, Neumann H: Generation of sketch-like feature encodings in oriented faces - A neural model. Journal of Vision 6(6), 1069a, 2006.
• Weidenbacher U, Layher G, Bayerl P, Neumann H: Detection of head pose and gaze direction for human-computer interaction. in: E Andre, L Dybkjaer, W Minker, H Neumann, M Weber (eds.). Perception and Interactive Technologies (PIT'06), LNAI 4021, Springer, 2006, pp.9-19. (PDF)
• Weidenbacher U, Bayerl P, Neumann H, Fleming R: Sketching shiny surfaces: 3D shape extraction and depiction of specular surfaces. ACM Trans. on Applied Perception 3(3): 262-285, 2006. (PDF)

2005

• Bayerl P, Neumann H: Attention and figure-ground segregation in a model of motion perception. Journal of Vision 5(8): 659a, 2005.
• Bayerl P, Neumann H: Judgement of gaze direction is affected by the looker’s head silhouette. Perception (Suppl.), 34, 206, 2005
• Clauss M, Bayerl P, Neumann H: Segmentation of independently moving objects using a maximum-likelihood principle. In: P Levi, M Schanz, R Lafrenz, V Avrutin (Hrsg.). Autonome Mobile Systeme 2005. Informatik Aktuell, Springer, Berlin, 2005, pp.81-87.
• Keil MS, Cristobal G, Hansen T, Neumann H: Recovering real-world images from single-scale boundaries with a novel filling-in architecture. NeuralNetworks 18: 1319–1331, 2005 (PDF)
• Kessler H, Hoffmann H, Bayerl P, Neumann H, Basic A, Deighton RM, Traue HC: Die Messung von Emotionserkennung mittels Computer-Morphing. Nervenheilkunde 24: 611-614, 2005.
• Schweiger R, Neumann H, Ritter W: Multiple-cue data fusion with particle filters for vehicle detection in night view automotive applications. Proc. IEEE Intelligent Vehicles Symp., IV'05, Las Vegas, Nevada, June 6-8, 2005.
• Thielscher A, Neumann H: Neural mechanisms of human texture processing: Texture boundary detection and visual search. Spatial Vision 18(2): 227-257, 2005. (PDF)
• Weidenbacher U, Bayerl P, Fleming RW, Neumann H: Perception of mirrored surfaces. Journal of Vision 5(8): 526a, 2005.
• Weidenbacher U, Bayerl P, Fleming RW, Neumann H: Extracting and depicting the 3D shape of specular surfaces. ACM SIGGRAPH, 2nd Symp. on Applied Perception in Graphics and Visualization (APGV 2005) A Coruna, Spain, Aug. 26-28, 2005, 83-86.

2004

• Bayerl P, Neumann H: Disambiguating visual motion by form-motion interaction - a computational model. EcoVision Early Cognitive Vision Workshop (Isle of Skye, Scotland, May 28-June 1), 2004 (http://www.cn.stir.ac.uk/ecovision-ws/).
• Bayerl P, Neumann H: Disambiguating visual motion through contextual feedback modulation. Neural Computation 16: 2041-2066, 2004. (PDF)
• Bayerl P, Neumann H: Unified mechanisms in the disambiguation and grouping of visual information in motion, stereo, and monocular depth perception. Perception 33 (Suppl.): 79, 2004.
• Bayerl P, Neumann H: A model of motion, stereo, and monocular depth perception. In: C.E. Rassmussen, HH Bülthoff, MA Giese, B Schölkopf(eds.). Pattern Recognition (Proc. 26th DAGM Symp., Tübingen, Aug.30-Sept.1, 2004), LNCS 3175, Springer, 2004, pp.95-102.
• Clauss M, Bayerl P, Neumann H: A statistical measure for evaluating regions-of interest based attention algorithms. In: CE Rassmussen, HH Bülthoff, MA Giese, B Schölkopf (eds.). Pattern Recognition (Proc. 26th DAGM Symp., Tübingen, Aug.30-Sept.1, 2004), LNCS 3175, Springer, 383-390. (preprint PDF, final version upon request)
• Clauss M, Bayerl P, Neumann H: Evaluation of regions-of-interest based attention algorithms using a probabilistic measure. In: Dynamic Perception, UJ Ilg, HH Bülthoff, HA Mallot (eds.) (Proc. 5th Workshop Dynamische Perzeption, Tübingen, Nov. 18-19), IOS Press, infix, 2004, pp. 227-232. (PDF)
• Fischer S, Bayerl P, Neumann H, Christobal G, Redondo R: Are iterations and curvature useful for tensor voting?. In: European Conf. on Computer Vision 2004, ECCV '04, T Pajdla, J Matas (eds.), LNCS 3023, 2004, pp.158-169.
• Hansen T, Neumann H: Neural mechanisms for the robust representation of junctions. Neural Computation 16: 1013-1037, 2004. (PDF)
• Hansen T, Neumann H: A simple cell model with dominating opponent inhibition for robust image processing. Neural Networks 17: 647-662, 2004. (PDF)
• Hansen T, Neumann H: Robust contour extraction and junction detection by a neural model utilizing recurrent long-range interactions. In: Dynamic Perception, UJ Ilg, HH Bülthoff, HA Mallot (eds.) (Proc. 5th Workshop Dynamische Perzeption, Tübingen, Nov.18-19), IOS Press, infix, 2004, pp.177-182.
• Reichle R, Bayerl P, Neumann H: A self-organized neural network for pattern recognition. In: Dynamic Perception, UJ Ilg, HH Bülthoff, HA Mallot (eds.) (Proc. 5th Workshop Dynamische Perzeption, Tübingen, Nov. 18-19), IOS Press, infix, 2004, pp.183-188.
• Schweiger R, Bayerl P, Neumann H: Neural architecture for temporal emotion classification. In: E Andre, L Dybkjaer, W Minker, P Heisterkamp (eds.). Affective Dialogue Systems (ADS'04), LNCS/LNAI 3068, Springer, 2004, pp.49-52. (preprint PDF, final version upon request)

2003

• Seifart F, Bayerl P, Neumann H: A neural model of heading detection from optic flow. Proc. European Symp. on Artificial Neural Networks, ESANN'03, Bruges (Belgium), April 23-25, 2003, d-side publi., ISBN 2-930307-03-X, pp. 319-324. (PDF)
• Zehender A, Bayerl P, Neumann H: A view-based approach for object recognition from image sequences. Proc. European Symp. on Artificial Neural Networks, ESANN'03, Bruges (Belgium), April 23-25, 2003, d-side publi., ISBN 2-930307-03-X, pp. 457-462. (PDF)
• Bayerl P, Neumann H: Bewegungswahrnehmung - die Illusion der Realität. Nervenheilkunde 22: 322-324, 2003.
• Bayerl P, Neumann H: Complementary computations for motion binding, segregation, and the neural solution to the aperture problem. Perception 32(Suppl.): 19-20, 2003.
• Groen G, Neumann H, Spitzer M, Wunderlich A, Thielscher A: Texture segmentation in human perception: A combined modeling and fMRI study. Human Brain Mapping 2003, Abstracts, June 18-22, New York, 2003.
• Meis U, Ritter W, Neumann H: Detection and classification of obstacles in night vision traffic scenes based on infrared imagery. Proc. IEEE Int. Conf. on Intelligent Transportation Systems, ITS'03, Shanghai, P.R.China, Oct. 12-15, 2003, pp.1140-1145.
• Neumann H, Mingolla E: Contour and surface perception. In: Handbook of Brain Theory and Neural Networks, 2nd ed., MA Arbib (ed.), MIT Press, Cambridge, 2003, 271-276.
• Neumann, H.: Completion phenomena in vision: A computational approach. In: Filling-In - From perceptual completion to cortical reorganization, L Pessoa, P De Weerd (eds.), Oxford Univ. Press, New York, 2003, 151-173.
• Thielscher A, Neumann H: Neural mechanisms of cortico-cortical interaction in texture boundary detection: A modeling approach. Neuroscience 122: 921-939, 2003. (PDF)

2002

• Bayerl P, Neumann H: Recurrent processing in the dorsal pathway underlies the robust integration and segregation of motion patterns. Proc. 2nd Annual Meeting of the Vision ScienceS Society (VSS'02), Sarasota, Florida, USA, 226, 2002.
• Bayerl P, Neumann H: Cortical mechanisms of processing visual flow - Insights from the Pinna-Brelstaff illusion. In: Dynamic Perception, RP Würtz, M Lappe (eds.) (Proc. 4th Workshop Dynamische Perzeption, Bochum, Nov. 14-15), AKA, 2002, pp.23-28.
• Bayerl P, Neumann H: Neural mechanisms of visual flow integration and segregation - Insights from the Pinna-Brelstaff illusion and variations of it. In: HH Bülthoff, S-W Lee, TA Poggio, C Wallraven (eds.). Biologically Motivated Computer Vision (Proc. 2nd Int. Workshop, BMCV'02, Tübingen, Germany, Nov. 22-24, 2002), LNCS 2525, Springer, pp.301-310.
• Hansen T, Neumann H: A computational model of recurrent, colinear long-range interaction in V1 for contour enhancement and junction detection. Proc. 2nd Annual Meeting of the Vision ScienceS Society (VSS'02), Sarasota, Florida, USA, 42, 2002.
• Hansen T, Neumann H: A biologically motivated scheme for robust junction detection. In: HH Bülthoff, S-W Lee, TA Poggio, C Wallraven (eds.). Biologically Motivated Computer Vision (Proc. 2nd Int. Workshop, BMCV'02, Tübingen, Germany, Nov. 22-24, 2002), LNCS 2525, Springer, pp.16-26.
• Keil MS, Christobal G, Neumann H: Brightness perception and real world image processing - A unifying account. In: Dynamic Perception, RP Würtz, M Lappe (eds.) (Proc. 4th Workshop Dynamische Perzeption, Bochum, Nov. 14-15), AKA, 2002, pp.215-220.
• Thielscher A, Schuboe A, Neumann H: A neural model of human texture processing: Texture segmentation vs. visual search. In: HH Bülthoff, S-W Lee, TA Poggio, C Wallraven (eds.). Biologically Motivated Computer Vision (Proc. 2nd Int. Workshop, BMCV'02, Tübingen, Germany, Nov. 22-24, 2002), LNCS 2525, Springer, pp.99-108.

2001

• Gavrila DM, Giebel J, Neumann H: Learning shape models from examples. In: Pattern Recognition (B Radig, S Florczyk, eds.). Proc. 23. DAGM-Symposium, Munich, LNCS 2191, Springer, Berlin, 2001, 369-376.
• Grebe O, Höher M, Schwenker F, Neumann H, Wöhrle J, Palm G, Hombach V, Kestler HA: New markers for diastolic function by cardiac magnetic resonance imaging. Computers in Cardiology 28: 625-628, 2001.
• Hansen T, Sepp W, Neumann H: Recurrent long-range interactions in early vision. In: Emergent Neural Computational Architectures, S. Wermter (ed.). LNAI 2036, Springer, Heidelberg, 2001, 127-138.
• Hansen T, Neumann H: Neural mechanisms for representing surface and contour features. In: Emergent Neural Computational Architectures, S. Wermter (ed.), LNAI 2036, Springer, Heidelberg, 2001, 139-153.
• Neumann H, Hansen T, Pessoa L: Visual filling-in for computing perceptual surface properties. Biological Cybernetics 85: 355-369, 2001. (PDF)
• Neumann H, Mingolla E: Computational neural models of spatial integration in perceptual grouping. In: From Fragments to Objects - Segmentation and Grouping in Vision, TF Shipley, PJ Kellman (eds.), Elsevier Science, Amsterdam, 2001, 353-400. (PDF)

2000

• Ahrns I, Neumann H: Space-variant active vision for object manipulation based on partial depth reconstruction. In: Dynamische Perzeption (G Baratoff, H Neumann, eds.). Proceedings in Artificial Intelligence 9, 75-80. Akademische Verlagsgesellschaft Aka Gmbh, Berlin, 2000. (PDF)
• Ahrns I, Neumann H: Space-variant image processing. In: Dynamische Perzeption (G Baratoff, H Neumann, eds.). Proceedings in Artificial Intelligence 9. Akademische Verlagsgesellschaft Aka Gmbh, Berlin, 2000, 203-206. (PDF)
• Baratoff G, Schönfelder , Ahrns I, Neumann H: Orientation contrast detection in space-variant images. In: BMVC 2000 (SW Lee, HH Bülthoff, T Poggio, eds.), 2000, pp.554-563.
• Baratoff G, Toepfer C, Neumann H: Combined space-variant maps for optical-flow based navigation. Biological Cybernetics 83: 199-209, 2000. (PDF)
• Hansen T, Baratoff G, Neumann H: A simple cell model with dominating opponent inhibition for robust contrast detection. Kognitionswissenschaft 9: 93-100, 2000.
• Neumann H, Sepp W: Perceptual strength and time course of illusory contour generation explained by a neural model of recurrent cortico-cortical interaction. Perception 29 (Suppl., 23rd ECVP): 71, 2000.
• Schoenfelder R, Baratoff G, Ahrns I, Neumann H: Attentional capture by feature contrast detection in space-variant images. In: Dynamische Perzeption (G Baratoff, H Neumann, eds.). Proceedings in Artificial Intelligence 9. Akademische Verlagsgesellschaft Aka Gmbh, Berlin, 2000, 227-230. (PDF)
• Viviani R, Neumann H, Spitzer M: Modelling attention with neural maps and learning theory. In: Cognitive Neuroscience Society Annual Meeting, San Francisco, USA, April 2000.

1999

• Ahrns I, Neumann H: A probabilistic approach for model-free grasping of unknown objects. In: Proc. 7th International Symposium on Intelligent Robotic Systems (SIRS'99) (H Araujo, J Dias (eds.)), University of Coimbra, Portugal. 369-378, 1999.
• Ahrns I, Neumann H: Space-variant dynamic neural fields for visual attention. In: Proc. International Conference on Computer Vision and Pattern Recognition (CVPR'99), Fort Collins, Colorado, 313-318, 1999.
• Hansen T, Neumann H: A model of V1 visual contrast processing utilizing long-range connections and recurrent interactions. In: Proc. Int.'l Conf. on Artificial Neural Networks (ICANN'99), Edinburgh, GB, 61-66, 1999.
• Kestler HA, Schulé M, Schwenker F, Neumann H, Mattfeld T: Klassifikation zytologischer Abstriche der Zervix mit neuronalen Verfahren. Biomedizinische Technik 44(1-2): 17-24, 1999.
• Neumann H, Sepp W: Recurrent V1-V2 interaction for early visual information processing. In: Proc. 7th European Symposium on Artificial Neural Networks (ESANN'99), (M Verleysen, ed.), 165-170, D-Facto, Brussels, Belgium, 1999.
• Neumann H, Sepp W: Recurrent V1-V2 interaction in early visual boundary processing. Biological Cybernetics 81: 425-444, 1999. (PDF)
• Neumann H, Pessoa L, Hansen T: Interaction of ON and OFF pathways for visual contrast measurement. Biological Cybernetics 81: 515-532, 1999. (PDF)
• Sepp W, Neumann H: A multi-resolution filling-in model for brightness perception. In: Proc. Int.'l Conf. on Artificial Neural Networks (ICANN'99), Edinburgh, GB, 461-466, 1999.

1998

• Ahrns I, Neumann H: Combining Log-polar mapping and Gabor filtering for real-time depth estimation. In: Dynamische Perzeption, Proceedings in Artificial Intelligence 8, 119-125, Infix, Sankt Augustin, 1998.
• Ahrns I, Neumann H: A view-based approach for real-time fixation using the Log-polar mapping. In: Dynamische Perzeption, Proceedings in Artificial Intelligence 8, 89-96, Infix, Sankt Augustin, 1998.
• Ahrns I, Neumann H: Real-time monocular fixation control using the Log-polar transformation and a confidence-based similarity measure. In: Proc. of the 14th Int. Conference on Pattern Recognition (ICPR'98), Vol. I, 310-315, Brisbane, Australia, 1998.
• Ahrns I, Neumann H: Improving phase based disparity estimation by means of filter tuning techniques. In: Proc. of the DAGM'98, 357-364, Stuttgart, 1998. (DAGM paper award)
• Grunewald A, Neumann H: Detection of first and second order motion. In: Advances in Neural Information Processing Systems, NIPS'97 (MI Jordan, MJ Kearns, SA Solla, eds.), 801-807, MIT Press, 1998.
• Neumann H, Pessoa L, Mingolla E: A neural network architecture of brightness perception: Non-linear contrast detection and geometry-driven diffusion. Image and Vision Computing 16(6-7): 423-446, 1998. (PDF)
• Neumann H: Representations, computation, and inverse ecological optics. Behavioral and Brain Sciences 21-6: 766-767, 1998.
• Pessoa L, Neumann H: Why does the brain fill-in? Trends in Cognitive Science 2(11):422-424, 1998. (PDF; commentary PDF)
• Toepfer C, Wende M, Baratoff G, Neumann H: Robot navigation by combining central and peripheral optical flow detection on a space-variant map. In: Proc. 14th International Conference on Pattern Recognition (ICPR'98), Vol. II, Brisbane, Australia, 1804-1807, 1998.
• Toepfer C, Baratoff G, Wende M, Neumann H: Ortsvariante Karte als effektives Datenformat zur Integration visueller Navigationsaufgaben. In: Proc. 20. DAGM-Symposium, Stuttgart, 193-200, 1998.

1997

• Gilchrist I, Humphreys GW, Riddoch MJ, Neumann H: Luminance and edge information in grouping: A study using visual search. Journal of Experimental Psychology: Human Perception and Performance 23(2): 464-480, 1997. (PDF)
• Glatting G, Weber CR, Rentschler M, Weller R, Neumann H, Reske SN: Bildrekonstruktion bei partiellem Informationsausfall: Hilfe durch die Max.-Entropie-Methode? Nuklearmedizin 36: A72, 1997.
• Neumann H, Sepp W, Mössner P: Adaptive resonance in V1-V2 interaction - grouping, illusory contours, and RF organization. In: Computational Neuroscience, J Bower (ed.), Proc. Ann. Computational Neuroscience Conf., July 14-17, Boston, MA, USA, 1996, 753-757. (PDF)
• Pessoa L, Grunewald A, Neumann H, Littmann E: A biological neural network of visual cell responses: Static and motion processing. Journal of the Brazilian Computer Society 1(4): 15-27, 1997.

1996

• Franke T, Neumann H, Seydel R: Anisotropic diffusion based on mean curvature motion: A computational study. In: Mustererkennung 1996, Informatik aktuell (B. Jähne, P Geißler, H Haußecker, F Hering, eds.), Proc. 18. DAGM-Symposium, Heidelberg, Springer, Berlin, 1996, pp.47-54.
• Littmann E, Neumann H, Pessoa L: Neural model for visual contrast detection. In: Proc. 4th European Symposium on Artificial Neural Networks (ESANN'96), Bruges, 193-198, D facto, Brussels, 1996.
• Littmann E, Neumann H, Redouloux L: Extraction of illusory contours by perceptual grouping. In: Mustererkennung 1996. Informatik aktuell (B. Jähne, P Geißler, H Haußecker, F Hering, eds.). Proc. 18. DAGM-Symposium, Heidelberg, Springer, Berlin, 1996, pp.243-251.
• Neumann H: Mechanisms of neural architecture for visual contrast and brightness perception. Neural Networks 9(6): 921-936, 1996. (PDF)
• Neumann H, Mössner P: Neural model of cortical dynamics ib resonant boundary detection and grouping. In: C von der Malsburg, W von Seelen, JC Vorbrüggen, B Senthoff (eds.), Proc. 6th Int'l Conf. on Artificial Neural Networks, ICANN 96, Bochum, Germany, July 16-19, 1996, LNCS 1112, Springer, 1996, pp.857-862 (PDF)

1995

• Pessoa L, Mingolla E, Neumann H: A contrast- and luminance-driven multiscale network model of brightness perception. Vision Research 35(15): 2201-2223, 1995. (PDF)

1994

• Ludwig K-O, Neumann H, Neumann B: Local stereoscopic depth estimation. Image and Vision Computing 12(1): 16-35, 1994. (PDF) (higher resolution version (15MB) PDF)

1992

• Ludwig K-O, Neumann H, Neumann B: Local stereoscopic depth estimation. In: European Conf. on Computer Vision 1992, ECCV '92, LNCS 0588, 1992, pp.373-377. (PDF)