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Puzzle Solver. Sravan Bhagavatula EE 638 Project Stanford ECE. Overview. Purpose of Project High Level Implementation Scale Invariant Feature Transform Explanation of Algorithm Results Future Work. Purpose of Project. Solving a jigsaw Finding placements

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puzzle solver

Puzzle Solver

SravanBhagavatula

EE 638 Project

Stanford ECE

overview
Overview
  • Purpose of Project
  • High Level Implementation
  • Scale Invariant Feature Transform
  • Explanation of Algorithm
  • Results
  • Future Work
purpose of project
Purpose of Project
  • Solving a jigsaw
  • Finding placements
  • Based on locations in original picture
high level implementation
High-level Implementation
  • Needs two inputs
    • Pieces
    • Original Image
  • Outputs
    • Numbered pieces
    • Original with placements
scale invariant feature transform
Scale Invariant Feature Transform
  • Object Recognition technique (David Lowe)
  • Rotation / orientation change was a problem
  • Features obtained similar to neuron responses in inferior temporal cortex (for primate vision)

Object Recognition from Local Scale-Invariant Features, D. G. Lowe, International Conference on Computer Vision, Corfu, Greece, Sept. 1999.

scale invariant feature transform1
Scale Invariant Feature Transform
  • KeypointLocations
    • Defined as extrema of a difference-of-Gaussian function applied in scale space
  • Local Image Description
    • Robust descriptor to local affine distortion
scale invariant feature transform2
Scale Invariant Feature Transform
  • Computationally efficient – one second/image order of 1000 features
  • Occlusions
  • Tested very well for rotation / scale changes
  • Chosen for invariance
explanation of algorithm
Explanation of Algorithm

P – Image of pieces

S – Image of complete picture

  • Find the keypoints in P and S with vl_sift
  • Output a modified P, with piece labels
    • Use kmeans() to cluster the keypoints in each piece
  • Take a small number of points per cluster
    • Around 20 – 30.
  • Compare these keypoints with ones in S
    • 2-norm comparison of the SIFT keypoint descriptors
explanation of algorithm cont
Explanation of Algorithm – Cont.
  • Find locations in S of matches
    • These basically count as the location of each piece
  • Classify each region of matches into clusters
    • I.E., choose a “central point” to designate as the label of the region
  • Output a modified version of S using these cluster labels
    • One that has the same labels as the one in P, such that similar pieces are in the right locations
future work
Future Work
  • Background of pieces needs to be uniform
    • Additional step to make the background uniform?
  • Try out orientation, lighting changes
  • Clustering without numPieces
  • Test it on much larger puzzles (~1000 piece, perhaps)
    • Computation time
  • Solve without the solution image
    • Much harder, more than just feature matching