Instructor 
Facundo Mémoli, m e m o l i @ m a t h . s t a n f o r d . e d u 

Meetings: 
Mondays 2:154:05 PM, in 240101 

Prerequisites:  I assume mathematical sophistication and familiarity with programming. However, the course is designed for a novice in the area. The grade will be based on class participation and a final project, which can be either an implementation or a survey paper.  
Grading:  The course will not have any homework or tests. The grade will be based on class participation and a final project, which can be either an implementation or a survey paper. I will provide a list of possible projects early in the quarter.  
Literature:  I will be providing slides/notes for the class, as well as supplementary readings, when appropriate. 
This seminar will touch upon the use of ideas from metric geometry for tackling the problem of Matching/Comparison of Shapes under invariances.
We will cover different ideas of Mikhail Gromov that have proved extremely useful in the context of this applied problem.
The central idea is to consider shapes as metric spaces (or measure metric spaces) and, then, use one of various notions of distance between metric spaces to obtain a measure of dissimilarity between shapes. The choice of the metric with which one augments the shapes encodes the degree of invariance one obtains from the dissimilarity measure. The main example in this family of notions of dissimilarity between shapes is the GromovHausdorff distance.
We will start by reviewing the main approaches in the literature. Then we will discuss the requisite theoretical background from Metric Geometry and cover details about the numerical computation of GromovHausdorff distances.
Connections with several standard approaches to the problem of shape comparison will be discussed.
Syllabus is [here]
"Shape Matching using GromovHausdorff distances." Introductory presentation.
[BBI] "A Course in Metric Geometry", by BuragoBuragoIvanov, AMS Graduate Studies in Mathematics Volume 33
[Villani] "Topics in Optimal Transportation", by Cedric Villani, AMS Graduate Studies in Mathematics Volume 58
[M07] "On the use of GromovHausdorff for Shape Matching and Comparison", by F.Memoli. PBG 2007.
[MS05] "A theoretical and computational framework for the isometry invariant recognition of point cloud data", by F.Memoli and G. Sapiro. Foundations of Computational Mathematics, 2005.
[Gromov] Metric structures for Riemannian and nonRiemmanian spaces, Birkhauser.
[M08euclidean] "Gromovhausdorff distance in Euclidean spaces", by F.Memoli. CVPR 2008.
[BBK06] "Efficient Computation of Isometry Invariant Distances between Surfaces", by Bronstein, Bronstein and Kimmel. SIAM 2006.
[BM] "Shape Matching and Object Recognition Using Shape Contexts", by Belongie et al, 2001.
[BM1] "Matching Shapes", by Belongie et al, 2002.
[Report] + [Presentation]
[osada] "Shape Distributions." Osada et al., 2002
[BK] "Which point configurations are determined by their distribution of pairwise distances ", M. Boutin and G. Kemper, 2006.
[BK1] "On reconstructing npoint configurations based on distrbutions of distances or areas ", M. Boutin and G. Kemper, 2003.
[BKgraphs] "Lossless representations of graphs using distrbutions", M. Boutin and G. Kemper, 2008.
[Report] + [Presentation]
[Frosini] "Using Matching distance in Size Theory, a survey", by d'Amico, Frosni and Landi, 2006.
[Frosinimultidim] "Stability in Multidimensional size theory", by Frosini et al., 2006.
Frosini's webpage. See the articles in wikipedia, start here.
Systematically Accelerating the Fracture Simulation Workflow using Shape Matching Techniques [Presentation]
Lecture 3 notes and auxiliary slides
Reading assignment:
Reading assignment:
Suggested reading: