?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Probabilistic+Search+for+Object+Segmentation+and+Recognition&rft.creator=Hillenbrand%2C+Dr.+Ulrich&rft.creator=Hirzinger%2C+Prof.+Dr.+Gerd&rft.subject=Machine+Vision&rft.subject=Machine+Learning&rft.subject=Robotics&rft.description=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%2C+the+truncated+object+probability%2C+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%2C+geometric+hashing%2C+alignment)+from+the+probabilistic+perspective+and+adds+insight+into+the+optimal+ordering+of+object+hypotheses+for+evaluation.+Moreover%2C+it+introduces+point-relation+densities%2C+a+key+component+of+the+truncated+probability%2C+as+statistical+models+of+local+surface+shape.&rft.date=2002&rft.type=Conference+Paper&rft.type=PeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F2393%2F1%2FHillenbrand_Hirzinger_02.pdf&rft.identifier=++Hillenbrand%2C+Dr.+Ulrich+and+Hirzinger%2C+Prof.+Dr.+Gerd++(2002)+Probabilistic+Search+for+Object+Segmentation+and+Recognition.++%5BConference+Paper%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F2393%2F