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Efficiently locating objects using the Hausdorff distance
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DOI：10.1023/A:1007975324482.png)
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摘要
En 中文
The Hausdorff distance is a measure defined between two point sets, here representing a model and an image. The Hausdorff distance is reliable even when the image contains multiple objects, noise, spurious features, and occlusions. In the past, it has been used to search images for instances of a model that has been translated, or translated and scaled, by finding transformations that bring a large number of model features close to image features, and vice versa. In this paper, we apply it to the task of locating an affine transformation of a model in an image; this corresponds to determining the pose of a planar object that has undergone weak-perspective projection. We develop a rasterised approach to the search and a number of techniques that allow us io locate quickly all transformations of the model that satisfy two quality criteria; we can also efficiently locate only the best transformation. We discuss an implementation of this approach, and present some examples of its use.
Keyword:
Hausdorff distance
object recognition
feature-based matching
efficient search
model-based recognition
affine transformation
distance transform
guaranteed search
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