shape matching and metric geometry...jan 13, 2014 · 1 shape matching and metric geometry facundo...
TRANSCRIPT
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1
Shape Matching and Metric
Geometry
Facundo Memoli.
CS 5339, The Ohio State University, Spring
2014.
Monday, January 13, 2014
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https://people.math.osu.edu/memoli.2/courses/cse-topics-2014/
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General stuff:
• class attendance is mandatory
• will post possible papers soon, after we discuss some more.
• a couple of days prior to each class i will update the webpage and postmaterials you should read on your own, before the class.
• you should read the papers so as to gain basic understanding of the ideaproposed by the authors
• in my slides i will use the following tags for the materials listed underthe”resources” section of the class webpage:
– [BBI] will refer to the AMS book by Burago, Burago and Ivanov.
– [Villani] AMS book by Cedric Villani.
– [dGW] my 2011 FoCM paper.
– [M07] my PBG07 paper.
– etc
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office hours etc
send me an email and we’ll set up a meeting.
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a couple of reference books:
Note: these are not textbooks
[BBI] [Villani]
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introduction/motivation: shape matching, shape comparison, shape analysis
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The Problem of Shape/Object Matching
• databases of objects
• objects can be many things:
– proteins– molecules– 2D objects (imaging)– 3D shapes: as obtained via a 3D scanner– 3D shapes: modeled with CAD software– 3D shapes: coming from design of bone protheses– text documents– more complicated structures present
in datasets (things you can’t visualize)Monday, January 13, 2014
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3D objects: examples
• cultural heritage (Michelangelo project:http://www-graphics.stanford.edu/projects/mich/)
• search of parts in a factory of, say, cars
• face recognition: the face of an individual is a 3D shape...
• proteins: the shape of a protein reflects its function..protein data bank: http://www.rcsb.org
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• 3D scanners are becoming increasingly cheaper.
• consequence: lots of 3D data are being generated and stored.
• 3D printing!
• How do we organize those datasets?
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http://sketchup.google.com/3dwarehouse/
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MichelangeloSculptorMichelangelo di Lodovico Buonarroti Simoni, commonly known as Michelangelo, was an Italian sculptor, painter, architect, poet, and engineer of the High Renaissance who exerted an unparalleled influence on the development of Western art. Wikipedia
Born: March 6, 1475, Caprese Michelangelo, ItalyDied: February 18, 1564, Rome, ItalyBuried: Basilica of Santa Croce, Florence, ItalyStructures: St. Peter's Basilica, Laurentian Library, MorePeriods: Italian Renaissance, High Renaissance, Renaissance
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https://graphics.stanford.edu/projects/mich/
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3D objects: examples
• cultural heritage (Michelangelo project:http://www-graphics.stanford.edu/projects/mich/)
• search of parts in a factory of, say, cars
• face recognition: the face of an individual is a 3D shape...
• proteins: the shape of a protein reflects its function..protein data bank: http://www.rcsb.org
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3D objects: examples
• cultural heritage (Michelangelo project:http://www-graphics.stanford.edu/projects/mich/)
• search of parts in a factory of, say, cars
• face recognition: the face of an individual is a 3D shape...
• proteins: the shape of a protein reflects its function..protein data bank: http://www.rcsb.org
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similarity principle: geometrically similar molecules are likely to have
similar functions
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about 100,000 3D structures of proteins
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similarity ppl: also in chemoinformatics
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similarity ppl: also in chemoinformatics
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similarity ppl: also in chemoinformatics
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2d shapes...
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2d shapes...
MNIST db: handwritten text recognition -- think USPS
segmented real objects -- MPEG 7 db
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?Monday, January 13, 2014
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• We intuitively know when two shapes are similar, and how similar they
are...
• This intrinsic understanding permits grouping/classifying shapes..
• if confronted with zillions of shapes: how do we do that? need a com-
puter...
• How can we extract/define a notion of similarity between shapes that can
be used by a computer?
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typical scenario
Text
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typical scenario
Text
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typical scenario
Text?Monday, January 13, 2014
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Typical situation: classification
• assume you have database D of objects.
• assume D is composed by several objects, and that each
of these objects belongs to one of n classes C1, . . . , Cn.
• imagine you are given a new object o, not in your
database, and you are asked to determine whether o belongs
to one of the classes. If yes, you also need to point to the class.
• One simple procedure is to say that you will assign object
o the class of the closest object in D:
class(o) = class(z)
where z ∈ D minimizes dist(o, z)
• in order to do this, one first needs to define a notion
dist of distance or dis-similarity between objects.Monday, January 13, 2014
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Typical situation: classification
• assume you have database D of objects.
• assume D is composed by several objects, and that each
of these objects belongs to one of n classes C1, . . . , Cn.
• imagine you are given a new object o, not in your
database, and you are asked to determine whether o belongs
to one of the classes. If yes, you also need to point to the class.
• One simple procedure is to say that you will assign object
o the class of the closest object in D:
class(o) = class(z)
where z ∈ D minimizes dist(o, z)
• in order to do this, one first needs to define a notion
dist of distance or dis-similarity between objects.Monday, January 13, 2014
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So.. first thing we need: a distance between shapes!
• what is, mathematically, a suitable distance between shapes?
• what properties are important?
• we probably want to be able to compute this distance easily .. computa-tional cost..
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X Y
Another important point: invariances
Are these two objects the same?
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XY
this is called invariance to rigid transformations
Another important point: invariances
Are these two objects the same?
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Another important points: invariances
what about these two?
roughly speaking, this corresponds to invariance to bending transformations..Monday, January 13, 2014
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Bending transformations
the distance, as measured by an ant, does not change
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Bending transformations
the distance, as measured by an ant, does not change
Important: this distance is different from the Euclidean distance!!Monday, January 13, 2014
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Geodesic distance vs Euclidean distance
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invariances...
The measure of dis-similarity dist must capture thetype of invariance you want to encode in your classification system.
= ?dist( , ) = 0 ?
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A Metric Space is a pair (X, d) whereX is a set and d : X ×X → R+, called the metric, s.t.
1. For all x, y, z ∈ X, d(x, y) ≤ d(x, z) + d(z, y).
2. For all x, y ∈ X, d(x, y) = d(y, x).
3. d(x, y) = 0 if and only if x = y.
Remark 1. One example is Rd with the Euclidean metric. Spheres Sn endowed
with the spherical metric provide another example.
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Metric Geometry in Shape Matching..
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What is Metric Geometry?
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What is Metric Geometry?
MG is the world of metric spaces..
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What is Metric Geometry?
MG is the world of metric spaces..
what can I say about the geometry of a shape/space using only measurements of distance?
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Example/Exercise
You land in a new planet (perfect sphere). You find no-one around.. how do you estimate the (size) radius of the
planet?
Say you only have a (short) measuring tape..
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Summary
• Shapes are being produced at a fast pace. We need methods for organizingcollection of shapes.
• When dealing with databases of objects, one needs a notion of dis-similaritybetween objects.
• This notion must take into account desired invariance (we saw two kinds,bendings and rigid isometries).
• We will model this dis-similarity as a metric or distance between objects– metric space structure!
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