{"publication_identifier":{"isbn":["978-331958770-7"]},"doi":"10.1007/978-3-319-58771-4_19","page":"235 - 246","author":[{"full_name":"Kuske, Jan","last_name":"Kuske","first_name":"Jan"},{"full_name":"Swoboda, Paul","id":"446560C6-F248-11E8-B48F-1D18A9856A87","last_name":"Swoboda","first_name":"Paul"},{"full_name":"Petra, Stefanie","last_name":"Petra","first_name":"Stefanie"}],"language":[{"iso":"eng"}],"editor":[{"first_name":"François","last_name":"Lauze","full_name":"Lauze, François"},{"last_name":"Dong","first_name":"Yiqiu","full_name":"Dong, Yiqiu"},{"full_name":"Bjorholm Dahl, Anders","first_name":"Anders","last_name":"Bjorholm Dahl"}],"_id":"646","ec_funded":1,"conference":{"name":"SSVM: Scale Space and Variational Methods in Computer Vision","start_date":"2017-06-04","location":"Kolding, Denmark","end_date":"2017-06-08"},"citation":{"ieee":"J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.","mla":"Kuske, Jan, et al. <i>A Novel Convex Relaxation for Non Binary Discrete Tomography</i>. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 235–46, doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">10.1007/978-3-319-58771-4_19</a>.","short":"J. Kuske, P. Swoboda, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 235–246.","ista":"Kuske J, Swoboda P, Petra S. 2017. A novel convex relaxation for non binary discrete tomography. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 235–246.","apa":"Kuske, J., Swoboda, P., &#38; Petra, S. (2017). A novel convex relaxation for non binary discrete tomography. In F. Lauze, Y. Dong, &#38; A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">https://doi.org/10.1007/978-3-319-58771-4_19</a>","chicago":"Kuske, Jan, Paul Swoboda, and Stefanie Petra. “A Novel Convex Relaxation for Non Binary Discrete Tomography.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:235–46. Springer, 2017. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">https://doi.org/10.1007/978-3-319-58771-4_19</a>.","ama":"Kuske J, Swoboda P, Petra S. A novel convex relaxation for non binary discrete tomography. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:235-246. doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">10.1007/978-3-319-58771-4_19</a>"},"year":"2017","intvolume":"     10302","publist_id":"7132","volume":10302,"project":[{"grant_number":"616160","_id":"25FBA906-B435-11E9-9278-68D0E5697425","name":"Discrete Optimization in Computer Vision: Theory and Practice","call_identifier":"FP7"}],"month":"06","date_updated":"2021-01-12T08:07:34Z","type":"conference","day":"01","department":[{"_id":"VlKo"}],"abstract":[{"text":"We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations.","lang":"eng"}],"main_file_link":[{"url":"https://arxiv.org/abs/1703.03769","open_access":"1"}],"publication_status":"published","date_published":"2017-06-01T00:00:00Z","publisher":"Springer","scopus_import":1,"oa":1,"oa_version":"Submitted Version","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T11:47:41Z","alternative_title":["LNCS"],"quality_controlled":"1","title":"A novel convex relaxation for non binary discrete tomography","status":"public"}