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Week 9 7/16/14

Week 9 7/16/14. Amari Lewis Aidean Sharghi. Light field dataset. Using the depth estimate provided by the L ytro compatible viewer software. See if we can use this information to increase object recognition.

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Week 9 7/16/14

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  1. Week 97/16/14 Amari Lewis AideanSharghi

  2. Light field dataset • Using the depth estimate provided by the Lytro compatible viewer software. • See if we can use this information to increase object recognition.

  3. The white pixels do not have depth value- due to occlusion, distance, surface angle or material. • The darker the color (black or grey), the more accurate depth perception)

  4. bike

  5. vehicle

  6. building

  7. RGB-D cameras • Sensing systems that capture RGB images along with per-pixel depth information. • The white pixels do not have depth value- due to occlusion, distance, surface angle or material. • The darker the color (black), the more accurate depth perception)

  8. Studying the depth information… • RGBD – OBJ CALSSIFIATION • The process(RGB-D object recognition and detection): utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. • Our ultimate goal is to find out a way that we can incorporate the use of the depth estimation from light field images for object recognition.

  9. References • Holistic Scene Understanding for 3D Object Detection with RGBD cameraauthors: Dahua Lin Sanja Fidler Raquel Urtasun • RGB-D Object Recognition and Detection– Artificial intelligence University of Washington • Depth from Combining Defocus and Correspondence Using Light-Field Cameras-University of California, Berkeley Authors: Michael Tao1, Sunil Hadap2, JitendraMalik1, and Ravi Ramamoorthi • RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments authors: Peter Henry, Michael Krainin, Evan Herbst, Xiaofeng Ren, Dieter Fox

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