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--- abstract: 'The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape.' altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: 'European Conference on Computer Vision -- ECCV 2002' confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Hillenbrand given: Ulrich honourific: Dr. lineage: '' - family: Hirzinger given: Gerd honourific: Prof. Dr. lineage: '' date: 2002 date_type: published datestamp: 2002-08-09 department: ~ dir: disk0/00/00/23/93 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 2393 fileinfo: /style/images/fileicons/application_pdf.png;/2393/1/Hillenbrand_Hirzinger_02.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'scene analysis, object recognition, grouping, segmentation, surface geometry, statistical modeling, range-data processing, searching, optimization' lastmod: 2011-03-11 08:54:58 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 791-806 pubdom: FALSE publication: ~ publisher: ~ refereed: TRUE referencetext: |- \bibitem{Ballard81} {\sc Ballard, D.~H.} \newblock Generalizing the {Hough} transform to detect arbitrary shapes. \newblock {\em Pattern Recognit. 13\/} (1981), 111--122. \bibitem{Besl&Jain85a} {\sc Besl, P., and Jain, R.} \newblock Intrinsic and extrinsic surface characteristics. \newblock In {\em Proc.\ IEEE Conf.\ Comp.\ Vision Patt.\ Recogn.\/} (1985), pp.~226--233. \bibitem{Besl&Jain85b} {\sc Besl, P., and Jain, R.} \newblock Three-dimensional object recognition. \newblock {\em ACM Comput.\ Surveys 17\/} (1985), 75--145. \bibitem{Faugeras&Hebert87} {\sc Faugeras, O.~D., and Hebert, M.} \newblock The representation, recognition, and positioning of {3-D} shapes from range data. \newblock In {\em Three-Dimensional Machine Vision}, T.~Kanade, Ed. 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