lecture 14: shape google: rigid shape statistics · lecture 14: shape google: rigid shape...

Lecture 14: Shape Google: Rigid ShapeStatistics

COMPSCI/MATH 290-04

Chris Tralie, Duke University

3/1/2016

COMPSCI/MATH 290-04 Lecture 14: Shape Google: Rigid Shape Statistics

Announcements

B Group Assignment 1 Full Submission Due Tomorrow(Wednesday) 11:55 PM

B Rank Top 3 Final Project Choices By Friday 3/5 (Groups of3-4)

B Attendance Policy ClarificationB Midterm Next Thursday 3/10

Table of Contents

I Shape Statistics / Algorithms

B Comparing Shape Statistics

B Classification / Performance Evaluation

Random Sampling By Area

Goal: Given a shape, return similar shapes in a database

Tralie 2010

B Focus on point clouds

B Focus on shapes similar under rigid motion

Random Sampling By Area

Centroid Centering / RMS Scaling

For a point cloud {~xi}Ni=1

B Subtract off centroidB Root mean square scale. Want√√√√ 1

N∑i=1

||~xi ||2 = 1

Shape Matching Criteria

B Concise To StoreB Quick to computeB Efficient to matchB DiscerningB Noise tolerantB Rotation Invariant

Shape Histogram: Shells

B Quick To Compute

B Concise To StoreB Rotation InvariantB Discerning

B Quick To Compute

B Concise To StoreB Rotation InvariantB Discerning

B Quick To ComputeB Concise To Store

B Rotation InvariantB Discerning

B Quick To ComputeB Concise To StoreB Rotation Invariant

B Discerning

B Quick To ComputeB Concise To StoreB Rotation Invariant

B Discerning

B Quick To ComputeB Concise To StoreB Rotation InvariantB Discerning

What can’t it tell apart?SHOW VIDEO

Shape Histogram: Shells And Sectors

SHOW VIDEO

Still Rotation Invariant?

B Sort sectors within each shellB Record PCA eigenvalues within each shell

Spin Images

Johnson/Herbert 1999, Figure Huang 2010COMPSCI/MATH 290-04 Lecture 14: Shape Google: Rigid Shape Statistics

Spin Images: Rubber Duck

Johnson/Herbert 1999

Spin Images

B Quick To Compute

B Concise To Store

(Can compress images)

B Rotation Invariant (Careful with principal axis stability)B Discerning

Spin Images

B Quick To Compute

B Concise To Store

(Can compress images)

Spin Images

(Can compress images)B Rotation Invariant (Careful with principal axis stability)B Discerning

Spin Images

B Quick To ComputeB Concise To Store (Can compress images)

Spin Images

B Quick To ComputeB Concise To Store (Can compress images)B Rotation Invariant

(Careful with principal axis stability)B Discerning

Spin Images

B Quick To ComputeB Concise To Store (Can compress images)B Rotation Invariant (Careful with principal axis stability)

B Discerning

Spin Images

B Quick To ComputeB Concise To Store (Can compress images)B Rotation Invariant (Careful with principal axis stability)B Discerning

Spin Images

B Quick To ComputeB Concise To Store (Can compress images)B Rotation Invariant (Careful with principal axis stability)B Discerning

D2: Distance Histograms

Osada 2003, Figure from Huang 2010

D2: Primitive Examples

Osada 2003

D2: Real Examples

Osada 2003COMPSCI/MATH 290-04 Lecture 14: Shape Google: Rigid Shape Statistics

D1: Randomly Sample Points

D3: Randomly Sample Areas

D4: Randomly Sample Volumes

Osada 2003

A3: Randomly Sample Angles

Osada 2003

Extended Gaussian Image

Funkhouser 2004

B Efficient To Compute / Concise To Store

Funkhouser 2004

B Discerning

(Only fully describes convex objects)

Funkhouser 2004

B Discerning (Only fully describes convex objects)

Funkhouser 2004

B Rotation Invariant

(Rotate To Align With PCA Axes)

Funkhouser 2004

B Rotation Invariant (Rotate To Align With PCA Axes)

Funkhouser 2004

B Robust To Noise?

Funkhouser 2004

B Not Robust To Noise!

Funkhouser 2004

Table of Contents

B Shape Statistics / Algorithms

I Comparing Shape Statistics

B Classification / Performance Evaluation

Normalize Histograms By Mass

h′[i] =h[i]∑N

k=1 h[k ]

In other words, all bins should sum to 1

Histogram Euclidean Distance

For histograms h1 and h2

dE(h1,h2) =

√√√√ N∑i=1

(h1[i]− h2[i])2

Just thinking of h1 and h2 as high dimensional Euclideanvectors! Each histogram bin is a dimension

Histogram Cosine Distance

dC(h1,h2) = cos−1

(~h1 · ~h2

|| ~h1|||| ~h2||

Euclidean Distance Shortcomings

0 5 10 15 20 250

0.7Euclidean Distance Fail

Histogram 1Histogram 2Histogram 3

They all have the same distance!

Euclidean Distance Shortcomings

0 5 10 15 20 250

0.7Earth Mover's Distance Illustration

Histogram 1Histogram 2

Move earth from blue to red

Earth Mover’s Distance

Chi Squared Distance

dχ(h1,h2) =12

N∑i=1

(h1[i]− h2[i])2

h1[i] + h2[i]

Exclude values for which h1[i] = h2[i] = 0

Table of Contents

B Shape Statistics / Algorithms

B Comparing Shape Statistics

I Classification / Performance Evaluation

Evaluation Strategy

Do leave one out technique

Use each item as test item in turn, compare to database

I Summarize evaluation statistics over entire database byaveraging them

Precision / Recall

Rusinkiewiz/Funkhouser 2009COMPSCI/MATH 290-04 Lecture 14: Shape Google: Rigid Shape Statistics

Other Evaluation Metrics

B Average Precision (Area Under Precision/Recall Curve)B Mean Reciprocal Rank (1/rank of first correct item)B Median Reciprocal Rank

1 is perfect score

lecture 14: shape google: rigid shape statistics · lecture 14: shape google: rigid shape...

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