<p>Computer vision research often involves high-dimensional analysis of feature space (e.g. pairwise distances, range queries, nearest neighbors, etc). Classical computational geometry data structures, such as KD trees appear a lot in the CV literature.</p>

<p>I would start into looking space-partitioning data structures and their usage in clustering, vector quantization, and density estimation. H. Samet is an expert in the field: <a href="http://store.elsevier.com/product.jsp?isbn=9780123694461" rel="nofollow">Foundations of Multidimensional and Metric Data Structures</a></p>

CC BY-SA 3.0
2012-04-16T17:47:30.630Z
2012-04-16T17:47:30.630Z
killogre
<p>Probably the most useful application of computational geometry within computer vision is in the realm of shape analysis/matching and image registration. There's a lot to google here, but you can start with Kendall's book on shape space, and also look at problems of measuring the distance between shapes.</p>

CC BY-SA 3.0
2011-10-08T04:18:42.050Z
2011-10-08T04:18:42.050Z
Suresh Venkat
<p>I'm interested in both of these topics independently, and wondering some good applications of comp geom within the domain of vision.  What are some nice applications and potential projects of comp geom applied to vision?</p>

<soft-question><cg.comp-geom><cv.computer-vision>
What are some applications of computational geometry towards computer vision?
feet
# What are some applications of computational geometry towards computer vision?

(Asked by: username_0 on 2011-10-08)

I'm interested in both of these topics independently, and wondering some good applications of comp geom within the domain of vision. What are some nice applications and potential projects of comp geom applied to vision?

**Tags:** soft-question, cg.comp-geom, cv.computer-vision

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## 2 answers

### Answer by username_1 on 2012-04-16. Score: 2

Computer vision research often involves high-dimensional analysis of feature space (e.g. pairwise distances, range queries, nearest neighbors, etc). Classical computational geometry data structures, such as KD trees appear a lot in the CV literature.

I would start into looking space-partitioning data structures and their usage in clustering, vector quantization, and density estimation. H. Samet is an expert in the field: [Foundations of Multidimensional and Metric Data Structures](http://store.elsevier.com/product.jsp?isbn=9780123694461)


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### Answer by username_2 on 2011-10-08. Score: 2

Probably the most useful application of computational geometry within computer vision is in the realm of shape analysis/matching and image registration. There's a lot to google here, but you can start with Kendall's book on shape space, and also look at problems of measuring the distance between shapes.


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