[{"page":"257 - 258","quality_controlled":"1","language":[{"iso":"eng"}],"publisher":"Springer","publication":"International Journal of Computer Vision","_id":"3164","scopus_import":1,"author":[{"first_name":"Matthew","last_name":"Blaschko","full_name":"Blaschko, Matthew"},{"orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"issue":"3","oa_version":"None","publication_status":"published","department":[{"_id":"ChLa"}],"date_created":"2018-12-11T12:01:46Z","month":"09","title":"Guest editorial: Special issue on structured prediction and inference","intvolume":"        99","volume":99,"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","date_updated":"2021-01-12T07:41:30Z","citation":{"ista":"Blaschko M, Lampert C. 2012. Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision. 99(3), 257–258.","short":"M. Blaschko, C. Lampert, International Journal of Computer Vision 99 (2012) 257–258.","mla":"Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue on Structured Prediction and Inference.” <i>International Journal of Computer Vision</i>, vol. 99, no. 3, Springer, 2012, pp. 257–58, doi:<a href=\"https://doi.org/10.1007/s11263-012-0530-y\">10.1007/s11263-012-0530-y</a>.","ieee":"M. Blaschko and C. Lampert, “Guest editorial: Special issue on structured prediction and inference,” <i>International Journal of Computer Vision</i>, vol. 99, no. 3. Springer, pp. 257–258, 2012.","chicago":"Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue on Structured Prediction and Inference.” <i>International Journal of Computer Vision</i>. Springer, 2012. <a href=\"https://doi.org/10.1007/s11263-012-0530-y\">https://doi.org/10.1007/s11263-012-0530-y</a>.","apa":"Blaschko, M., &#38; Lampert, C. (2012). Guest editorial: Special issue on structured prediction and inference. <i>International Journal of Computer Vision</i>. Springer. <a href=\"https://doi.org/10.1007/s11263-012-0530-y\">https://doi.org/10.1007/s11263-012-0530-y</a>","ama":"Blaschko M, Lampert C. Guest editorial: Special issue on structured prediction and inference. <i>International Journal of Computer Vision</i>. 2012;99(3):257-258. doi:<a href=\"https://doi.org/10.1007/s11263-012-0530-y\">10.1007/s11263-012-0530-y</a>"},"year":"2012","date_published":"2012-09-01T00:00:00Z","type":"journal_article","doi":"10.1007/s11263-012-0530-y","day":"01","abstract":[{"text":"Overview of the Special Issue on structured prediction and inference.","lang":"eng"}],"publist_id":"3521"},{"title":"Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components","intvolume":"         7","publication_status":"published","date_created":"2018-12-11T12:02:15Z","department":[{"_id":"ChLa"}],"article_processing_charge":"No","author":[{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert"},{"first_name":"Jan","last_name":"Peters","full_name":"Peters, Jan"}],"issue":"1","_id":"3248","scopus_import":"1","article_type":"original","publisher":"Springer","file_date_updated":"2020-07-14T12:46:04Z","page":"31 - 41","quality_controlled":"1","abstract":[{"lang":"eng","text":"We describe RTblob, a high speed vision system that detects objects in cluttered scenes based on their color and shape at a speed of over 800 frames/s. Because the system is available as open-source software and relies only on off-the-shelf PC hardware components, it can provide the basis for multiple application scenarios. As an illustrative example, we show how RTblob can be used in a robotic table tennis scenario to estimate ball trajectories through 3D space simultaneously from four cameras images at a speed of 200 Hz."}],"doi":"10.1007/s11554-010-0168-3","day":"01","date_updated":"2022-05-24T08:05:40Z","citation":{"ista":"Lampert C, Peters J. 2012. Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing. 7(1), 31–41.","short":"C. Lampert, J. Peters, Journal of Real-Time Image Processing 7 (2012) 31–41.","mla":"Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects in Multiple Camera Streams with off-the-Shelf Hardware Components.” <i>Journal of Real-Time Image Processing</i>, vol. 7, no. 1, Springer, 2012, pp. 31–41, doi:<a href=\"https://doi.org/10.1007/s11554-010-0168-3\">10.1007/s11554-010-0168-3</a>.","chicago":"Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects in Multiple Camera Streams with off-the-Shelf Hardware Components.” <i>Journal of Real-Time Image Processing</i>. Springer, 2012. <a href=\"https://doi.org/10.1007/s11554-010-0168-3\">https://doi.org/10.1007/s11554-010-0168-3</a>.","ieee":"C. Lampert and J. Peters, “Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components,” <i>Journal of Real-Time Image Processing</i>, vol. 7, no. 1. Springer, pp. 31–41, 2012.","ama":"Lampert C, Peters J. Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. <i>Journal of Real-Time Image Processing</i>. 2012;7(1):31-41. doi:<a href=\"https://doi.org/10.1007/s11554-010-0168-3\">10.1007/s11554-010-0168-3</a>","apa":"Lampert, C., &#38; Peters, J. (2012). Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. <i>Journal of Real-Time Image Processing</i>. Springer. <a href=\"https://doi.org/10.1007/s11554-010-0168-3\">https://doi.org/10.1007/s11554-010-0168-3</a>"},"year":"2012","ddc":["000"],"volume":7,"month":"03","oa_version":"Submitted Version","publication":"Journal of Real-Time Image Processing","has_accepted_license":"1","language":[{"iso":"eng"}],"oa":1,"publist_id":"3417","publication_identifier":{"issn":["1861-8200"],"eissn":["1861-8219"]},"date_published":"2012-03-01T00:00:00Z","type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","file":[{"creator":"kschuh","file_id":"5958","access_level":"open_access","relation":"main_file","file_name":"2012_Springer_Lampert.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:46:04Z","file_size":2933187,"checksum":"241be47ea50e81a283bcf4c45b07e8cc","date_created":"2019-02-12T10:52:25Z"}]},{"alternative_title":["IST Austria Technical Report"],"month":"07","title":"Approximating marginals using discrete energy minimization","pubrep_id":"36","date_created":"2018-12-12T11:39:06Z","department":[{"_id":"VlKo"},{"_id":"ChLa"}],"publication_status":"published","oa_version":"Published Version","author":[{"id":"476A2FD6-F248-11E8-B48F-1D18A9856A87","last_name":"Korc","first_name":"Filip","full_name":"Korc, Filip"},{"full_name":"Kolmogorov, Vladimir","last_name":"Kolmogorov","first_name":"Vladimir","id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph"}],"has_accepted_license":"1","_id":"5396","publisher":"IST Austria","language":[{"iso":"eng"}],"file_date_updated":"2020-07-14T12:46:44Z","page":"13","oa":1,"abstract":[{"lang":"eng","text":"We consider the problem of inference in agraphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete  Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pair-wise terms over a discretized domain. This allows the use of techniques originally devel-oped for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it  from training data. Experimental results show that for certain types of graphs a learned function can out-perform the  Bethe approximation. We also establish a link between the Bethe free energy and submodular functions."}],"publication_identifier":{"issn":["2664-1690"]},"day":"23","doi":"10.15479/AT:IST-2012-0003","type":"technical_report","date_published":"2012-07-23T00:00:00Z","citation":{"ista":"Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete energy minimization, IST Austria, 13p.","mla":"Korc, Filip, et al. <i>Approximating Marginals Using Discrete Energy Minimization</i>. IST Austria, 2012, doi:<a href=\"https://doi.org/10.15479/AT:IST-2012-0003\">10.15479/AT:IST-2012-0003</a>.","short":"F. Korc, V. Kolmogorov, C. Lampert, Approximating Marginals Using Discrete Energy Minimization, IST Austria, 2012.","chicago":"Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. <i>Approximating Marginals Using Discrete Energy Minimization</i>. IST Austria, 2012. <a href=\"https://doi.org/10.15479/AT:IST-2012-0003\">https://doi.org/10.15479/AT:IST-2012-0003</a>.","ieee":"F. Korc, V. Kolmogorov, and C. Lampert, <i>Approximating marginals using discrete energy minimization</i>. IST Austria, 2012.","ama":"Korc F, Kolmogorov V, Lampert C. <i>Approximating Marginals Using Discrete Energy Minimization</i>. IST Austria; 2012. doi:<a href=\"https://doi.org/10.15479/AT:IST-2012-0003\">10.15479/AT:IST-2012-0003</a>","apa":"Korc, F., Kolmogorov, V., &#38; Lampert, C. (2012). <i>Approximating marginals using discrete energy minimization</i>. IST Austria. <a href=\"https://doi.org/10.15479/AT:IST-2012-0003\">https://doi.org/10.15479/AT:IST-2012-0003</a>"},"year":"2012","date_updated":"2023-02-23T11:13:22Z","related_material":{"record":[{"relation":"earlier_version","id":"3124","status":"public"}]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","ddc":["000"],"file":[{"creator":"system","file_id":"5490","access_level":"open_access","relation":"main_file","file_name":"IST-2012-0003_IST-2012-0003.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:46:44Z","file_size":618744,"checksum":"7e0ba85ad123b13223aaf6cdde2d288c","date_created":"2018-12-12T11:53:29Z"}]},{"conference":{"end_date":"2011-12-14","location":"Granada, Spain","name":"NIPS: Neural Information Processing Systems","start_date":"2011-12-12"},"publisher":"Neural Information Processing Systems","language":[{"iso":"eng"}],"quality_controlled":"1","title":"Maximum margin multi-label structured prediction","month":"12","publication_status":"published","oa_version":"None","department":[{"_id":"ChLa"}],"date_created":"2018-12-11T12:01:45Z","author":[{"first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"_id":"3163","scopus_import":1,"related_material":{"record":[{"relation":"later_version","id":"3322","status":"public"}]},"status":"public","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"We study multi-label prediction for structured output sets, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multilabel classification techniques are typically not applicable in this situation, because they require explicit enumeration of the label set, which is infeasible in case of structured outputs. Relying on techniques originally designed for single-label structured prediction, in particular structured support vector machines, results in reduced prediction accuracy, or leads to infeasible optimization problems. In this work we derive a maximum-margin training formulation for multi-label structured prediction that remains computationally tractable while achieving high prediction accuracy. It also shares most beneficial properties with single-label maximum-margin approaches, in particular formulation as a convex optimization problem, efficient working set training, and PAC-Bayesian generalization bounds."}],"publist_id":"3522","day":"01","date_published":"2011-12-01T00:00:00Z","type":"conference","date_updated":"2023-10-17T11:47:35Z","year":"2011","citation":{"ieee":"C. Lampert, “Maximum margin multi-label structured prediction,” presented at the NIPS: Neural Information Processing Systems, Granada, Spain, 2011.","chicago":"Lampert, Christoph. “Maximum Margin Multi-Label Structured Prediction.” Neural Information Processing Systems, 2011.","apa":"Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information Processing Systems.","ama":"Lampert C. Maximum margin multi-label structured prediction. In: Neural Information Processing Systems; 2011.","ista":"Lampert C. 2011. Maximum margin multi-label structured prediction. NIPS: Neural Information Processing Systems.","mla":"Lampert, Christoph. <i>Maximum Margin Multi-Label Structured Prediction</i>. Neural Information Processing Systems, 2011.","short":"C. Lampert, in:, Neural Information Processing Systems, 2011."}},{"date_published":"2011-01-01T00:00:00Z","type":"conference","date_updated":"2023-10-17T11:59:50Z","citation":{"apa":"Quadrianto, N., &#38; Lampert, C. (2011). Learning multi-view neighborhood preserving projections (pp. 425–432). Presented at the ICML: International Conference on Machine Learning, Bellevue, United States: ML Research Press.","ama":"Quadrianto N, Lampert C. Learning multi-view neighborhood preserving projections. In: ML Research Press; 2011:425-432.","chicago":"Quadrianto, Novi, and Christoph Lampert. “Learning Multi-View Neighborhood Preserving Projections,” 425–32. ML Research Press, 2011.","ieee":"N. Quadrianto and C. Lampert, “Learning multi-view neighborhood preserving projections,” presented at the ICML: International Conference on Machine Learning, Bellevue, United States, 2011, pp. 425–432.","short":"N. Quadrianto, C. Lampert, in:, ML Research Press, 2011, pp. 425–432.","mla":"Quadrianto, Novi, and Christoph Lampert. <i>Learning Multi-View Neighborhood Preserving Projections</i>. ML Research Press, 2011, pp. 425–32.","ista":"Quadrianto N, Lampert C. 2011. Learning multi-view neighborhood preserving projections. ICML: International Conference on Machine Learning, 425–432."},"year":"2011","abstract":[{"text":"We address the problem of metric learning for multi-view data, namely the construction of embedding projections from data in different representations into a shared feature space, such that the Euclidean distance in this space provides a meaningful within-view as well as between-view similarity. Our motivation stems from the problem of cross-media retrieval tasks, where the availability of a joint Euclidean distance function is a pre-requisite to allow fast, in particular hashing-based, nearest neighbor queries. We formulate an objective function that expresses the intuitive concept that matching samples are mapped closely together in the output space, whereas non-matching samples are pushed apart, no matter in which view they are available. The resulting optimization problem is not convex, but it can be decomposed explicitly into a convex and a concave part, thereby allowing efficient optimization using the convex-concave procedure. Experiments on an image retrieval task show that nearest-neighbor based cross-view retrieval is indeed possible, and the proposed technique improves the retrieval accuracy over baseline techniques.","lang":"eng"}],"publist_id":"3316","day":"01","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Novi","last_name":"Quadrianto","full_name":"Quadrianto, Novi"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887"}],"_id":"3319","scopus_import":"1","month":"01","title":"Learning multi-view neighborhood preserving projections","publication_status":"published","oa_version":"None","department":[{"_id":"ChLa"}],"article_processing_charge":"No","date_created":"2018-12-11T12:02:39Z","language":[{"iso":"eng"}],"page":"425 - 432","conference":{"name":"ICML: International Conference on Machine Learning","start_date":"2011-06-28","end_date":"2011-07-02","location":"Bellevue, United States"},"publisher":"ML Research Press"},{"day":"23","doi":"10.1561/0600000033","abstract":[{"lang":"eng","text":"Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. This monograph introduces the reader to the most popular classes of structured models in computer vision. Our focus is discrete undirected graphical models which we cover in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. We discuss separately recently successful techniques for prediction in general structured models. In the second part of this monograph we describe methods for parameter learning where we distinguish the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. We highlight developments to enhance current models and discuss kernelized models and latent variable models. To make the monograph more practical and to provide links to further study we provide examples of successful application of many methods in the computer vision literature."}],"year":"2011","citation":{"ama":"Nowozin S, Lampert C. Structured learning and prediction in computer vision. <i>Foundations and Trends in Computer Graphics and Vision</i>. 2011;6(3-4):185-365. doi:<a href=\"https://doi.org/10.1561/0600000033\">10.1561/0600000033</a>","apa":"Nowozin, S., &#38; Lampert, C. (2011). Structured learning and prediction in computer vision. <i>Foundations and Trends in Computer Graphics and Vision</i>. Now Publishers. <a href=\"https://doi.org/10.1561/0600000033\">https://doi.org/10.1561/0600000033</a>","ieee":"S. Nowozin and C. Lampert, “Structured learning and prediction in computer vision,” <i>Foundations and Trends in Computer Graphics and Vision</i>, vol. 6, no. 3–4. Now Publishers, pp. 185–365, 2011.","chicago":"Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction in Computer Vision.” <i>Foundations and Trends in Computer Graphics and Vision</i>. Now Publishers, 2011. <a href=\"https://doi.org/10.1561/0600000033\">https://doi.org/10.1561/0600000033</a>.","short":"S. Nowozin, C. Lampert, Foundations and Trends in Computer Graphics and Vision 6 (2011) 185–365.","mla":"Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction in Computer Vision.” <i>Foundations and Trends in Computer Graphics and Vision</i>, vol. 6, no. 3–4, Now Publishers, 2011, pp. 185–365, doi:<a href=\"https://doi.org/10.1561/0600000033\">10.1561/0600000033</a>.","ista":"Nowozin S, Lampert C. 2011. Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision. 6(3–4), 185–365."},"date_updated":"2023-10-17T11:52:46Z","volume":6,"ddc":["000"],"department":[{"_id":"ChLa"}],"date_created":"2018-12-11T12:02:39Z","article_processing_charge":"No","publication_status":"published","intvolume":"         6","title":"Structured learning and prediction in computer vision","scopus_import":"1","_id":"3320","issue":"3-4","author":[{"full_name":"Nowozin, Sebastian","last_name":"Nowozin","first_name":"Sebastian"},{"first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"publisher":"Now Publishers","article_type":"original","quality_controlled":"1","page":"185 - 365","file_date_updated":"2020-07-14T12:46:07Z","oa":1,"publist_id":"3315","type":"journal_article","date_published":"2011-05-23T00:00:00Z","file":[{"date_updated":"2020-07-14T12:46:07Z","content_type":"application/pdf","file_name":"2011_CompGraphicsVision_Nowozin.pdf","date_created":"2020-05-14T14:34:47Z","file_size":3745064,"checksum":"f1043ef389f1558e2a226bb51568511f","file_id":"7837","creator":"dernst","relation":"main_file","access_level":"open_access"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","month":"05","has_accepted_license":"1","publication":"Foundations and Trends in Computer Graphics and Vision","language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"publisher":"Neural Information Processing Systems Foundation","_id":"3322","publication":"NIPS: Neural Information Processing Systems","author":[{"orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"article_processing_charge":"No","department":[{"_id":"ChLa"}],"date_created":"2018-12-11T12:02:40Z","oa_version":"None","publication_status":"published","title":"Maximum margin multi label structured prediction","month":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","related_material":{"record":[{"id":"3163","relation":"earlier_version","status":"public"}]},"year":"2011","citation":{"ama":"Lampert C. <i>Maximum Margin Multi Label Structured Prediction</i>. Neural Information Processing Systems Foundation; 2011.","apa":"Lampert, C. (2011). <i>Maximum margin multi label structured prediction</i>. <i>NIPS: Neural Information Processing Systems</i>. Neural Information Processing Systems Foundation.","chicago":"Lampert, Christoph. <i>Maximum Margin Multi Label Structured Prediction</i>. <i>NIPS: Neural Information Processing Systems</i>. Neural Information Processing Systems Foundation, 2011.","ieee":"C. Lampert, <i>Maximum margin multi label structured prediction</i>. Neural Information Processing Systems Foundation, 2011.","mla":"Lampert, Christoph. “Maximum Margin Multi Label Structured Prediction.” <i>NIPS: Neural Information Processing Systems</i>, Neural Information Processing Systems Foundation, 2011.","short":"C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information Processing Systems Foundation, 2011.","ista":"Lampert C. 2011. Maximum margin multi label structured prediction, Neural Information Processing Systems Foundation,p."},"date_updated":"2023-10-17T11:47:36Z","type":"conference_poster","date_published":"2011-12-13T00:00:00Z","day":"13","publist_id":"3313","abstract":[{"text":"We study multi-label prediction for structured output spaces, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multi-label classification techniques are typically not applicable in this situation, because they require explicit enumeration of the label space, which is infeasible in case of structured outputs. Relying on techniques originally designed for single- label structured prediction, in particular structured support vector machines, results in reduced prediction accuracy, or leads to infeasible optimization problems. In this work we derive a maximum-margin training formulation for multi-label structured prediction that remains computationally tractable while achieving high prediction accuracy. It also shares most beneficial properties with single-label maximum-margin approaches, in particular a formulation as a convex optimization problem, efficient working set training, and PAC-Bayesian generalization bounds.","lang":"eng"}]},{"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"id":"5386","relation":"earlier_version","status":"public"}]},"acknowledgement":"The first author is supported by the Austrian Science Fund (FWF) grant No. P20134-N13. The authors would like to thank Sebastian Nowozin for helpful discussions.","abstract":[{"text":"We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks.","lang":"eng"}],"publist_id":"3294","doi":"10.1109/CVPR.2011.5995503","day":"22","publication_identifier":{"eisbn":["978-1-4577-0395-9"],"isbn":["978-1-4577-0394-2"]},"date_published":"2011-07-22T00:00:00Z","type":"conference","date_updated":"2023-02-23T12:23:56Z","citation":{"ieee":"C. Chen, D. Freedman, and C. Lampert, “Enforcing topological constraints in random field image segmentation,” in <i>CVPR: Computer Vision and Pattern Recognition</i>, Colorado Springs, CO, United States, 2011, pp. 2089–2096.","chicago":"Chen, Chao, Daniel Freedman, and Christoph Lampert. “Enforcing Topological Constraints in Random Field Image Segmentation.” In <i>CVPR: Computer Vision and Pattern Recognition</i>, 2089–96. IEEE, 2011. <a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">https://doi.org/10.1109/CVPR.2011.5995503</a>.","apa":"Chen, C., Freedman, D., &#38; Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. In <i>CVPR: Computer Vision and Pattern Recognition</i> (pp. 2089–2096). Colorado Springs, CO, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">https://doi.org/10.1109/CVPR.2011.5995503</a>","ama":"Chen C, Freedman D, Lampert C. Enforcing topological constraints in random field image segmentation. In: <i>CVPR: Computer Vision and Pattern Recognition</i>. IEEE; 2011:2089-2096. doi:<a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">10.1109/CVPR.2011.5995503</a>","ista":"Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in random field image segmentation. CVPR: Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 2089–2096.","short":"C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–2096.","mla":"Chen, Chao, et al. “Enforcing Topological Constraints in Random Field Image Segmentation.” <i>CVPR: Computer Vision and Pattern Recognition</i>, IEEE, 2011, pp. 2089–96, doi:<a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">10.1109/CVPR.2011.5995503</a>."},"year":"2011","conference":{"location":"Colorado Springs, CO, United States","end_date":"2011-06-25","start_date":"2011-06-20","name":"CVPR: Conference on Computer Vision and Pattern Recognition"},"publisher":"IEEE","language":[{"iso":"eng"}],"page":"2089 - 2096","quality_controlled":"1","month":"07","title":"Enforcing topological constraints in random field image segmentation","publication_status":"published","oa_version":"None","article_processing_charge":"No","department":[{"_id":"HeEd"},{"_id":"ChLa"}],"date_created":"2018-12-11T12:02:45Z","author":[{"full_name":"Chen, Chao","last_name":"Chen","first_name":"Chao","id":"3E92416E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Freedman","first_name":"Daniel","full_name":"Freedman, Daniel"},{"orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"publication":"CVPR: Computer Vision and Pattern Recognition","_id":"3336","scopus_import":"1"},{"citation":{"mla":"Wang, Zhikun, et al. <i>Learning Anticipation Policies for Robot Table Tennis</i>. IEEE, 2011, pp. 332–37, doi:<a href=\"https://doi.org/10.1109/IROS.2011.6094892\">10.1109/IROS.2011.6094892</a>.","short":"Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, J. Peters, in:, IEEE, 2011, pp. 332–337.","ista":"Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. 2011. Learning anticipation policies for robot table tennis. IROS: RSJ International Conference on Intelligent Robots and Systems, 332–337.","apa":"Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., &#38; Peters, J. (2011). Learning anticipation policies for robot table tennis (pp. 332–337). Presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA: IEEE. <a href=\"https://doi.org/10.1109/IROS.2011.6094892\">https://doi.org/10.1109/IROS.2011.6094892</a>","ama":"Wang Z, Lampert C, Mülling K, Schölkopf B, Peters J. Learning anticipation policies for robot table tennis. In: IEEE; 2011:332-337. doi:<a href=\"https://doi.org/10.1109/IROS.2011.6094892\">10.1109/IROS.2011.6094892</a>","ieee":"Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, and J. Peters, “Learning anticipation policies for robot table tennis,” presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA, 2011, pp. 332–337.","chicago":"Wang, Zhikun, Christoph Lampert, Katharina Mülling, Bernhard Schölkopf, and Jan Peters. “Learning Anticipation Policies for Robot Table Tennis,” 332–37. IEEE, 2011. <a href=\"https://doi.org/10.1109/IROS.2011.6094892\">https://doi.org/10.1109/IROS.2011.6094892</a>."},"year":"2011","date_updated":"2021-01-12T07:42:45Z","type":"conference","date_published":"2011-01-01T00:00:00Z","day":"01","doi":"10.1109/IROS.2011.6094892","publist_id":"3293","abstract":[{"lang":"eng","text":"Playing table tennis is a difficult task for robots, especially due to their limitations of acceleration. A key bottleneck is the amount of time needed to reach the desired hitting position and velocity of the racket for returning the incoming ball. Here, it often does not suffice to simply extrapolate the ball's trajectory after the opponent returns it but more information is needed. Humans are able to predict the ball's trajectory based on the opponent's moves and, thus, have a considerable advantage. Hence, we propose to incorporate an anticipation system into robot table tennis players, which enables the robot to react earlier while the opponent is performing the striking movement. Based on visual observation of the opponent's racket movement, the robot can predict the aim of the opponent and adjust its movement generation accordingly. The policies for deciding how and when to react are obtained by reinforcement learning. We conduct experiments with an existing robot player to show that the learned reaction policy can significantly improve the performance of the overall system."}],"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","status":"public","scopus_import":1,"_id":"3337","author":[{"full_name":"Wang, Zhikun","last_name":"Wang","first_name":"Zhikun"},{"orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Mülling, Katharina","last_name":"Mülling","first_name":"Katharina"},{"last_name":"Schölkopf","first_name":"Bernhard","full_name":"Schölkopf, Bernhard"},{"last_name":"Peters","first_name":"Jan","full_name":"Peters, Jan"}],"date_created":"2018-12-11T12:02:45Z","department":[{"_id":"ChLa"}],"oa_version":"None","publication_status":"published","title":"Learning anticipation policies for robot table tennis","month":"01","quality_controlled":"1","page":"332 - 337","language":[{"iso":"eng"}],"publisher":"IEEE","conference":{"end_date":"2011-09-30","location":"San Francisco, USA","start_date":"2011-09-25","name":"IROS: RSJ International Conference on Intelligent Robots and Systems"}},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"relation":"later_version","id":"3336","status":"public"}]},"status":"public","ddc":["000"],"file":[{"file_size":26390601,"checksum":"ad64c2add5fe2ad10e9d5c669f3f9526","date_created":"2018-12-12T11:53:34Z","file_name":"IST-2011-0002_IST-2011-0002.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:46:41Z","relation":"main_file","access_level":"open_access","creator":"system","file_id":"5495"}],"date_published":"2011-03-28T00:00:00Z","type":"technical_report","date_updated":"2023-02-23T11:22:48Z","citation":{"ama":"Chen C, Freedman D, Lampert C. <i>Enforcing Topological Constraints in Random Field Image Segmentation</i>. IST Austria; 2011. doi:<a href=\"https://doi.org/10.15479/AT:IST-2011-0002\">10.15479/AT:IST-2011-0002</a>","apa":"Chen, C., Freedman, D., &#38; Lampert, C. (2011). <i>Enforcing topological constraints in random field image segmentation</i>. IST Austria. <a href=\"https://doi.org/10.15479/AT:IST-2011-0002\">https://doi.org/10.15479/AT:IST-2011-0002</a>","chicago":"Chen, Chao, Daniel Freedman, and Christoph Lampert. <i>Enforcing Topological Constraints in Random Field Image Segmentation</i>. IST Austria, 2011. <a href=\"https://doi.org/10.15479/AT:IST-2011-0002\">https://doi.org/10.15479/AT:IST-2011-0002</a>.","ieee":"C. Chen, D. Freedman, and C. Lampert, <i>Enforcing topological constraints in random field image segmentation</i>. IST Austria, 2011.","short":"C. Chen, D. Freedman, C. Lampert, Enforcing Topological Constraints in Random Field Image Segmentation, IST Austria, 2011.","mla":"Chen, Chao, et al. <i>Enforcing Topological Constraints in Random Field Image Segmentation</i>. IST Austria, 2011, doi:<a href=\"https://doi.org/10.15479/AT:IST-2011-0002\">10.15479/AT:IST-2011-0002</a>.","ista":"Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in random field image segmentation, IST Austria, 69p."},"year":"2011","abstract":[{"lang":"eng","text":"We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks."}],"oa":1,"doi":"10.15479/AT:IST-2011-0002","day":"28","publication_identifier":{"issn":["2664-1690"]},"file_date_updated":"2020-07-14T12:46:41Z","language":[{"iso":"eng"}],"page":"69","publisher":"IST Austria","author":[{"full_name":"Chen, Chao","first_name":"Chao","last_name":"Chen","id":"3E92416E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Freedman","first_name":"Daniel","full_name":"Freedman, Daniel"},{"full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"_id":"5386","has_accepted_license":"1","title":"Enforcing topological constraints in random field image segmentation","pubrep_id":"22","alternative_title":["IST Austria Technical Report"],"month":"03","oa_version":"Published Version","publication_status":"published","department":[{"_id":"ChLa"}],"date_created":"2018-12-12T11:39:02Z"},{"day":"21","doi":"10.1109/TRO.2011.2121130","publist_id":"3225","abstract":[{"text":"Dynamic tactile sensing is a fundamental ability to recognize materials and objects. However, while humans are born with partially developed dynamic tactile sensing and quickly master this skill, today's robots remain in their infancy. The development of such a sense requires not only better sensors but the right algorithms to deal with these sensors' data as well. For example, when classifying a material based on touch, the data are noisy, high-dimensional, and contain irrelevant signals as well as essential ones. Few classification methods from machine learning can deal with such problems. In this paper, we propose an efficient approach to infer suitable lower dimensional representations of the tactile data. In order to classify materials based on only the sense of touch, these representations are autonomously discovered using visual information of the surfaces during training. However, accurately pairing vision and tactile samples in real-robot applications is a difficult problem. The proposed approach, therefore, works with weak pairings between the modalities. Experiments show that the resulting approach is very robust and yields significantly higher classification performance based on only dynamic tactile sensing.","lang":"eng"}],"year":"2011","citation":{"mla":"Kroemer, Oliver, et al. “Learning Dynamic Tactile Sensing with Robust Vision Based Training.” <i>IEEE Transactions on Robotics</i>, vol. 27, no. 3, IEEE, 2011, pp. 545–57, doi:<a href=\"https://doi.org/10.1109/TRO.2011.2121130\">10.1109/TRO.2011.2121130</a>.","short":"O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011) 545–557.","ista":"Kroemer O, Lampert C, Peters J. 2011. Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics. 27(3), 545–557.","ama":"Kroemer O, Lampert C, Peters J. Learning dynamic tactile sensing with robust vision based training. <i>IEEE Transactions on Robotics</i>. 2011;27(3):545-557. doi:<a href=\"https://doi.org/10.1109/TRO.2011.2121130\">10.1109/TRO.2011.2121130</a>","apa":"Kroemer, O., Lampert, C., &#38; Peters, J. (2011). Learning dynamic tactile sensing with robust vision based training. <i>IEEE Transactions on Robotics</i>. IEEE. <a href=\"https://doi.org/10.1109/TRO.2011.2121130\">https://doi.org/10.1109/TRO.2011.2121130</a>","chicago":"Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Learning Dynamic Tactile Sensing with Robust Vision Based Training.” <i>IEEE Transactions on Robotics</i>. IEEE, 2011. <a href=\"https://doi.org/10.1109/TRO.2011.2121130\">https://doi.org/10.1109/TRO.2011.2121130</a>.","ieee":"O. Kroemer, C. Lampert, and J. Peters, “Learning dynamic tactile sensing with robust vision based training,” <i>IEEE Transactions on Robotics</i>, vol. 27, no. 3. IEEE, pp. 545–557, 2011."},"date_updated":"2021-01-12T07:43:06Z","type":"journal_article","date_published":"2011-05-21T00:00:00Z","volume":27,"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","status":"public","date_created":"2018-12-11T12:03:01Z","department":[{"_id":"ChLa"}],"oa_version":"None","publication_status":"published","intvolume":"        27","title":"Learning dynamic tactile sensing with robust vision based training","month":"05","scopus_import":1,"publication":"IEEE Transactions on Robotics","_id":"3382","issue":"3","author":[{"full_name":"Kroemer, Oliver","last_name":"Kroemer","first_name":"Oliver"},{"last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Peters, Jan","first_name":"Jan","last_name":"Peters"}],"publisher":"IEEE","quality_controlled":"1","page":"545 - 557","language":[{"iso":"eng"}]},{"volume":32,"acknowledgement":"The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 228180. This work was funded in part by the EC project CLASS, IST 027978, and the PASCAL2 network of excellence, IST 2002-506778.","status":"public","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","year":"2011","citation":{"apa":"Blaschko, M., Shelton, J., Bartels, A., Lampert, C., &#38; Gretton, A. (2011). Semi supervised kernel canonical correlation analysis with application to human fMRI. <i>Pattern Recognition Letters</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.patrec.2011.02.011\">https://doi.org/10.1016/j.patrec.2011.02.011</a>","ama":"Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. Semi supervised kernel canonical correlation analysis with application to human fMRI. <i>Pattern Recognition Letters</i>. 2011;32(11):1572-1583. doi:<a href=\"https://doi.org/10.1016/j.patrec.2011.02.011\">10.1016/j.patrec.2011.02.011</a>","chicago":"Blaschko, Matthew, Jacquelyn Shelton, Andreas Bartels, Christoph Lampert, and Arthur Gretton. “Semi Supervised Kernel Canonical Correlation Analysis with Application to Human FMRI.” <i>Pattern Recognition Letters</i>. Elsevier, 2011. <a href=\"https://doi.org/10.1016/j.patrec.2011.02.011\">https://doi.org/10.1016/j.patrec.2011.02.011</a>.","ieee":"M. Blaschko, J. Shelton, A. Bartels, C. Lampert, and A. Gretton, “Semi supervised kernel canonical correlation analysis with application to human fMRI,” <i>Pattern Recognition Letters</i>, vol. 32, no. 11. Elsevier, pp. 1572–1583, 2011.","mla":"Blaschko, Matthew, et al. “Semi Supervised Kernel Canonical Correlation Analysis with Application to Human FMRI.” <i>Pattern Recognition Letters</i>, vol. 32, no. 11, Elsevier, 2011, pp. 1572–83, doi:<a href=\"https://doi.org/10.1016/j.patrec.2011.02.011\">10.1016/j.patrec.2011.02.011</a>.","short":"M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition Letters 32 (2011) 1572–1583.","ista":"Blaschko M, Shelton J, Bartels A, Lampert C, Gretton A. 2011. Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters. 32(11), 1572–1583."},"date_updated":"2021-01-12T07:43:09Z","type":"journal_article","date_published":"2011-08-01T00:00:00Z","day":"01","doi":"10.1016/j.patrec.2011.02.011","publist_id":"3218","abstract":[{"text":"Kernel canonical correlation analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying process that generates the data and can ignore high-variance noise directions. However, for data where acquisition in one or more modalities is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multi-variate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of KCCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.","lang":"eng"}],"quality_controlled":"1","page":"1572 - 1583","language":[{"iso":"eng"}],"publisher":"Elsevier","scopus_import":1,"publication":"Pattern Recognition Letters","_id":"3389","issue":"11","author":[{"full_name":"Blaschko, Matthew","first_name":"Matthew","last_name":"Blaschko"},{"full_name":"Shelton, Jacquelyn","last_name":"Shelton","first_name":"Jacquelyn"},{"full_name":"Bartels, Andreas","last_name":"Bartels","first_name":"Andreas"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph"},{"full_name":"Gretton, Arthur","last_name":"Gretton","first_name":"Arthur"}],"department":[{"_id":"ChLa"}],"date_created":"2018-12-11T12:03:03Z","publication_status":"published","oa_version":"None","intvolume":"        32","month":"08","title":"Semi supervised kernel canonical correlation analysis with application to human fMRI"},{"oa":1,"publist_id":"2431","date_published":"2010-11-04T00:00:00Z","type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","file":[{"content_type":"application/pdf","file_name":"2010_ECCV_Nowozin.pdf","date_updated":"2020-07-14T12:46:16Z","file_size":4087332,"checksum":"3716e10e161f7c714fd17ec193a223c3","date_created":"2020-05-19T16:27:34Z","creator":"dernst","file_id":"7871","access_level":"open_access","relation":"main_file"}],"month":"11","oa_version":"Submitted Version","has_accepted_license":"1","conference":{"start_date":"2010-09-05","name":"ECCV: European Conference on Computer Vision","end_date":"2010-09-11","location":"Heraklion, Crete, Greece"},"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Recent progress in per-pixel object class labeling of natural images can be attributed to the use of multiple types of image features and sound statistical learning approaches. Within the latter, Conditional Random Fields (CRF) are prominently used for their ability to represent interactions between random variables. Despite their popularity in computer vision, parameter learning for CRFs has remained difficult, popular approaches being cross-validation and piecewise training.\r\nIn this work, we propose a simple yet expressive tree-structured CRF based on a recent hierarchical image segmentation method. Our model combines and weights multiple image features within a hierarchical representation and allows simple and efficient globally-optimal learning of ≈ 105 parameters. The tractability of our model allows us to pose and answer some of the open questions regarding parameter learning applying to CRF-based approaches. The key findings for learning CRF models are, from the obvious to the surprising, i) multiple image features always help, ii) the limiting dimension with respect to current models is the amount of training data, iii) piecewise training is competitive, iv) current methods for max-margin training fail for models with many parameters.\r\n"}],"doi":"10.1007/978-3-642-15567-3_8","day":"04","date_updated":"2021-01-12T07:52:14Z","year":"2010","citation":{"ista":"Nowozin S, Gehler P, Lampert C. 2010. On parameter learning in CRF-based approaches to object class image segmentation. ECCV: European Conference on Computer Vision, LNCS, vol. 6316, 98–111.","short":"S. Nowozin, P. Gehler, C. Lampert, in:, Springer, 2010, pp. 98–111.","mla":"Nowozin, Sebastian, et al. <i>On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation</i>. Vol. 6316, Springer, 2010, pp. 98–111, doi:<a href=\"https://doi.org/10.1007/978-3-642-15567-3_8\">10.1007/978-3-642-15567-3_8</a>.","ieee":"S. Nowozin, P. Gehler, and C. Lampert, “On parameter learning in CRF-based approaches to object class image segmentation,” presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6316, pp. 98–111.","chicago":"Nowozin, Sebastian, Peter Gehler, and Christoph Lampert. “On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation,” 6316:98–111. Springer, 2010. <a href=\"https://doi.org/10.1007/978-3-642-15567-3_8\">https://doi.org/10.1007/978-3-642-15567-3_8</a>.","ama":"Nowozin S, Gehler P, Lampert C. On parameter learning in CRF-based approaches to object class image segmentation. In: Vol 6316. Springer; 2010:98-111. doi:<a href=\"https://doi.org/10.1007/978-3-642-15567-3_8\">10.1007/978-3-642-15567-3_8</a>","apa":"Nowozin, S., Gehler, P., &#38; Lampert, C. (2010). On parameter learning in CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. <a href=\"https://doi.org/10.1007/978-3-642-15567-3_8\">https://doi.org/10.1007/978-3-642-15567-3_8</a>"},"ddc":["000"],"volume":6316,"alternative_title":["LNCS"],"title":"On parameter learning in CRF-based approaches to object class image segmentation","intvolume":"      6316","publication_status":"published","department":[{"_id":"ChLa"}],"article_processing_charge":"No","date_created":"2018-12-11T12:05:12Z","author":[{"last_name":"Nowozin","first_name":"Sebastian","full_name":"Nowozin, Sebastian"},{"full_name":"Gehler, Peter","first_name":"Peter","last_name":"Gehler"},{"orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"_id":"3793","scopus_import":1,"publisher":"Springer","file_date_updated":"2020-07-14T12:46:16Z","page":"98 - 111","quality_controlled":"1"},{"title":"Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning","month":"11","alternative_title":["LNCS"],"intvolume":"      6312","oa_version":"None","publication_status":"published","department":[{"_id":"ChLa"}],"date_created":"2018-12-11T12:05:12Z","author":[{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert"},{"last_name":"Krömer","first_name":"Oliver","full_name":"Krömer, Oliver"}],"_id":"3794","scopus_import":1,"conference":{"name":"ECCV: European Conference on Computer Vision","start_date":"2010-09-05","end_date":"2010-09-11","location":"Heraklion, Crete, Greece"},"publisher":"Springer","language":[{"iso":"eng"}],"page":"566 - 579","quality_controlled":"1","abstract":[{"text":"We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.","lang":"eng"}],"publist_id":"2433","doi":"10.1007/978-3-642-15552-9_41","day":"10","date_published":"2010-11-10T00:00:00Z","type":"conference","date_updated":"2021-01-12T07:52:14Z","citation":{"ista":"Lampert C, Krömer O. 2010. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. ECCV: European Conference on Computer Vision, LNCS, vol. 6312, 566–579.","mla":"Lampert, Christoph, and Oliver Krömer. <i>Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning</i>. Vol. 6312, Springer, 2010, pp. 566–79, doi:<a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">10.1007/978-3-642-15552-9_41</a>.","short":"C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579.","chicago":"Lampert, Christoph, and Oliver Krömer. “Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning,” 6312:566–79. Springer, 2010. <a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">https://doi.org/10.1007/978-3-642-15552-9_41</a>.","ieee":"C. Lampert and O. Krömer, “Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning,” presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece, 2010, vol. 6312, pp. 566–579.","apa":"Lampert, C., &#38; Krömer, O. (2010). Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp. 566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. <a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">https://doi.org/10.1007/978-3-642-15552-9_41</a>","ama":"Lampert C, Krömer O. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning. In: Vol 6312. Springer; 2010:566-579. doi:<a href=\"https://doi.org/10.1007/978-3-642-15552-9_41\">10.1007/978-3-642-15552-9_41</a>"},"year":"2010","status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","volume":6312,"main_file_link":[{"url":"http://www.ics.forth.gr/eccv2010/intro.php"}]}]