object detection using marked point process
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Object Detection Using Marked Point Process
CMPUT 615
Nilanjan Ray
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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
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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
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Point Process Prior
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A point consists of a center and its marks (mi)
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Interaction function:
marks
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Simulations From Marked PP Prior
Four realizations
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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
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Detection Result
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Road Network Extraction
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Building Extraction
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Hydrographic Network Extraction
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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