Object Detection Using Marked Point Process

CMPUT 615

Nilanjan Ray

Object Detection

• Often we are asked to detect objects in an image, where the number of objects is not known a priori

• We may have knowledge about object likelihood, i.e., a good sense of what is a good measurement, what is not

• We may also have some knowledge about spatial distribution of the objects

• Can we put together all the pieces of information in a nice computational framework for object detection?

Yes! Marked point process framework can be utilized here

Object Detection: Point Process

• A point process (aka spatial point process) can attach a probability to a configuration of points on a space

• A point can have its marks. For example, an ellipse center is the point and its marks are the orientation and two radii

• Thus, a point together with its marks can represent an object that we want to detect from an image

Point Process Prior

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a ~ U(amin, amax), b ~ U(bmin, bmax),

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A point consists of a center and its marks (mi)

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Interaction function:

marks

Simulations From Marked PP Prior

Four realizations

Metropolis-Hastings Algorithm

• Has 3 move types– Birth of a new point– Death of an existing point– Altering marks of an existing point

• Each such move type is accepted or rejected via a ratio (a dimensionless number) called MH ratio

• This process simulation is run a long time– until the configuration converges

Detection Result

Road Network Extraction

Building Extraction

Hydrographic Network Extraction

Summary

• Spatial point process is excellent in modeling object level information

• Can deal with variable number of objects in an image

• The downside is long computations: sampling based techniques take a long time