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Classification and Pose Recovery in Image Sequences (CAPRI)

The CAPRI project, led by Michael Hödlmoser and supervised by Martin Kampel, Branislav Micusik, and Marc Pollefeys, addresses the time-consuming challenges of Chamfer matching in image sequences. As we await results from CVPR, our next steps involve utilizing multiple projections for each frame to identify the optimal sequence over time. By employing random forests to sort projections trained on 3D models, we aim to distinguish vehicle types and poses effectively, focusing on identifying adequate and discriminative features for improved outcomes.

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Classification and Pose Recovery in Image Sequences (CAPRI)

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  1. Classification and Pose Recovery in Image Sequences (CAPRI) Michael Hödlmoser Supervisors: Martin Kampel Branislav Micusik Marc Pollefeys

  2. What‘snew • Waiting for CVPR results • Problem: Chamfermatchingis time consuming

  3. Next Steps • New idea: • Having in mind: Multiple projectionsforeachframe, find bestsequenceover time • Usingrandomforestsforsortingtheprojections

  4. Next steps • Random forestistrainedusingprojectionsof 3D models • A vehicle type andposegivesoneclassofthetree • Find adequateanddiscriminativefeatures

  5. Time Table

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