This document discusses using graph theory and depth images to recognize objects. It proposes constructing a mesh of the world from depth camera data, transforming depth images into normal maps, and representing each region or plane as a different color "billboard". Objects in images can be matched by growing regions into nodes connected by edges between neighboring regions. Graphs of images could then be compared to find the largest isomorphic subgraphs and determine if the images match.

1. Depth image recognition
using isomorphic graph
theory
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2. Our project...
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We try to construct a
mesh of the world using
the data from a depth
sensing camera.
Having the depth images
Transforming to normal
images
Apply billboard concept (
each region / plane in
different color)
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3. Image == sub-graph?
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Lets assume that we
can perfectly
recognize objects in
an image
The image graph is a
sub-graph of the real
world
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4. Image analysis
RGB Images
Normal Maps
Region Growing
Images
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5. Graph-based object recognition
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Objects can be matched
using region growing
Each object in image →
Node
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RGB histogram is the
“color” of the node
Average color pixel of
each Node(region)
Edges are assigned to
neighbouring regions
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6. Graph-based object recognition (2)
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In image sequences,
nodes represent objects.
The nodes of the graph
can be used for pose
estimation
Saved they can be used
for global positioning
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7. One approach
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Recursive color mapping
to detect each region
One random pixel is
given, a contour is
obtained by color flooding
and a new node is created
The process is repeated
for every border pixel
An edge is created when
two regions are neighbors
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8. The sequence
Nodes
Delaunay
Voronoi
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9. Finding the isomorphisms
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10. Finding the isomorphisms
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11. In the end
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When the largest
isomorphic sub-graph
is found we can see if
the images have
matched.
Then its just looking
at the sizes..
?
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12. Demo-time
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