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Object Extraction using Segmentation

Object Extraction using Segmentation. ECE 847 Final Project Bryan Willimon. Overview. Background of Project Main Idea and Focus Drawbacks and Obstacles Results Conclusion/Future Work. Background. Current Research Project

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Object Extraction using Segmentation

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  1. Object Extraction using Segmentation ECE 847 Final Project Bryan Willimon

  2. Overview • Background of Project • Main Idea and Focus • Drawbacks and Obstacles • Results • Conclusion/Future Work

  3. Background • Current Research Project • Using computer vision to grab an unknown object from within a pile given a target item • Unknown = no previous knowledge of object • Working with PUMA arm in EIB • Previous projects have achieved similar results but with known surroundings and known objects (STAIR)

  4. Main Idea • Extract an unknown object using various types of segmentation working together • Graph Segmentation and Lucas-Kanade • Graph Segmentation breaks image into many regions • Largest region (not touching a border) is determined to be object on top

  5. Main Idea (cont.) • Determine centroid of the object to be the point of grasping (for 2D image) • Use Lucas-Kanade to give feature points in the whole image • Only track the feature points on current object • Continue tracking until all points are gone and repeat process until target is found

  6. Drawbacks and Obstacles • Drawbacks • Graph Segmentation is slow • Only using 2D image for grasping • Obstacles • Find and use a faster algorithm • Grasping a 3D object requires 3D modeling and/or motion • Also check if any other objects will be damaged in any way once current item is being moved

  7. Results

  8. Results (cont.)

  9. Conclusion/Future Work • Results provide a way to extract an unknown object from a pile • Explore other segmentation algorithms and find something faster • Using color and/or clustering • Work on grasping 3D objects

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