hand-drawings as model

hand-drawings are simpler, less informative 

class variability: variations among instances within an object class

Chamfer matching methods can detect shapes in cluttered images, but they need a large number of templates to handle shape variations, e.g. 1000, and are prone to produce rather high false-positive rates

a powerful point-matching method based on Integer Quadratic Programming: computational complexity

Besides, [1] uses real images as models, so it is unclear how it would perform when given simpler, less informative hand-drawings.

[2]  based on edge patches

Contour Segment Network

  • dealing with highly cluttered images,
  • allowing intra-class shape variations and large scale changes,
  • working from a single example,
  • being robust to broken edges, and
  • being computationally efficient

brittleness of edge detection – contour is often broken into several edgel-chains

segment the contour chains of the model, giving a set of contour segment chains along the outlines of the object

functionality of pure shape matchers takes a clean shape as input, support matching to cluttered test images

Simple decomposes the hand-drawing into PAS, then uses these PAS for the Hough voting stage, and the hand-drawing itself for the shape matching stage.

representative shape context

  • shape context of which point should represent the image?
  • pixel density based sampling – promote point with higher or lower density?
  • uniformly 

    sampled shape context of the shape may contain redundant information

  • Mori et al. [3] tested the representative shape context method on the Snodgrass and Vanderwart line drawings.

    • Queries were distorted versions the original
    • Embed objects into some clutter:

      • find the outline of the object, construct a binary mask for it, and using logical operations (AND à OR) to copy the clutter around the object.

      • Finding the outline of objects is done using a method similar to flood-fill.
    • Pseudocode for original Representative Shape Context
    • PRE-PROCESSING:
      % Compute shape contexts for known shapes
      PRUNING:
      SCquery = shape contexts for r random pointsforeach known shape Si
      for j = 1 : r
      dist(Squery; Si)+ = minu(2(SCj query; SCui ))
      % Sort dist and truncate to return a
      % shortlist.

A query of a hand-drawn shape is successful if the corresponding known shape is included in the set of retrieved candidate shapes.

 

‘Shape Context and Chamfer Matching in Cluttered Scenes’ – only a single template shape

reference

paper From Images to Shape Models for Object Detection [pdf]

Hierarchical Matching of Deformable Shapes[pdf]

[3] Mori, G., Belongie, S., & Malik, J., (2001) Shape Contexts Enable Efficient Retrieval of Similar Shapes, CVPR.[pdf]

[4] Recognizing hand-drawn images using shape context [pdf]

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