Models and Matching

1. Models and Matching Methods of modeling objects and their environments; Methods of matching models to sensed data for recogniton Stockman CSE/MSU Fall 2008

2. Some methods to study • Mesh models (surface) • Vertex-edge-face models (surface) • Functional forms: superquadrics (surface) • Generalized cylinders (volume) • Voxel sets and octrees (volume) • View class models (image-based) • Recognition by appearance (image-based) • Functional models and the Theory of affordances (object-oriented) Stockman CSE/MSU Fall 2008

3. Models are what models do Stockman CSE/MSU Fall 2008

4. What do models do? Stockman CSE/MSU Fall 2008

5. Vertex-edge-face models Polyhedra and extensions; Model the surface of objects Stockman CSE/MSU Fall 2008

6. Vertex-Edge-Face model Stockman CSE/MSU Fall 2008

7. Sample object All surfaces are planar or cylindrical Stockman CSE/MSU Fall 2008

8. Matching methods • Hypothesize point correspondences • Filter on distances • Compute 3D alignment of model to data • Verify positions of other model points, edges, or faces. You can now do this! • LOTS of work in the literature on this! Can work for many industrial objects (and human faces perhaps!) Stockman CSE/MSU Fall 2008

9. Triangular meshes Very general and used by most CAD systems. Stockman CSE/MSU Fall 2008

10. Texture-mapped mesh dog SURFACE PLUS TEXTURE 3D SURFACE MODEL With each triangle is a mapping of its vertices into pixels [r, c] of a color image. Thus any point of any triangle can be assigned a color [R, G, B]. There may be several images available to create these mappings. Courtesy of Kari Puli Stockman CSE/MSU Fall 2008

11. Meshes are very general They are usually verbose and often are too detailed for many operations, but are often used in CAD. (Volumetric cube models are actually displayed here: made from many views by Kari Pulli.) Stockman CSE/MSU Fall 2008

12. Modeling the human body for clothing industry and … Multiple Structured light scanners used: could this be a service industry such as Kinkos? Actually cross sections of a generalized cylinder model. Stockman CSE/MSU Fall 2008

13. Mesh characteristics + can be easy to generate from scanned data Stockman CSE/MSU Fall 2008

14. Making mesh models Stockman CSE/MSU Fall 2008

15. Marching cubeshttp://www.exaflop.org/docs/marchcubes/ (James Sharman) Raster scan through image F(r, c). Look for adjacent pixels, one above threshold and one below threshold. Interpolate real coordinates for f(x, y) = t in between "Marching Cubes: A High Resolution 3D Surface Construction Algorithm",William E. Lorensen and Harvey E. Cline,Computer Graphics (Proceedings of SIGGRAPH '87), Vol. 21, No. 4, pp. 163-169. Stockman CSE/MSU Fall 2008

16. Marching in 3D space F(s, r, c) Some voxel corners are above threshold t and some are below. Stockman CSE/MSU Fall 2008

17. PhD work by Paul Albee 2004 • Used Argonne National Labs scanner • High energy, high resolution planar Xrays penetrate object rotating on a turntable • Computer aided tomography synthesizes a 3D volume of densities with voxel size of about 5 microns Stockman CSE/MSU Fall 2008

18. Segmentation of Scutigera a tiny crablike organism Slice j of material density F( sj, r, c ) “thresholded” volume Stockman CSE/MSU Fall 2008

19. Some common 3D problems • analyze blood vessel structure in head • capture structure and motion of vertebrae of spine • analyze porosity and structure of soil • analyze structure of materials • automatic segmentation into regions • automatic correspondence of 3D points at two instants of of time • 3D volume visualization and virtual tours Stockman CSE/MSU Fall 2008

20. Scanning technique abstraction CCD camera (row) material sample X-ray planes scintillator Pin head X-rays partly absorbed by sample; excite scintillator producing one row in the camera image; rotate sample a few degrees and produce another row; 3D reconstruction using CT rotate Stockman CSE/MSU Fall 2008

21. Scutigera: a tiny crustacean • organism is smaller than 1 mm • scanned at Argonne • volume segmented and • meshed by Paul Albee • roughly ten million triangles • to represent the surface • anaglyph created for 3D • visualization • (view with stereo glasses) Stockman CSE/MSU Fall 2008

22. Presentation of Results to User • Can explore the 3D data using rotation/translation • Can create stereo images from 3D data Stockman CSE/MSU Fall 2008

23. Physics-based models Can be used to make meshes; Meshes retain perfect topology; Can span spots of bad or no data Stockman CSE/MSU Fall 2008

24. Physics-based modeling Stockman CSE/MSU Fall 2008

25. Forces move points on the model; halt at scanned data Stockman CSE/MSU Fall 2008

26. Fitting an active contour to image data Stockman CSE/MSU Fall 2008

27. Balloon model for closed object surface Courtesy of Chen and Medioni Stockman CSE/MSU Fall 2008

28. Balloon evolution • balloon stops at data points • mesh forces constrain neighbors • large triangles split into 4 triangles • resulting mesh has correct topology • hard CS part is detecting when balloon should be stopped by data point Stockman CSE/MSU Fall 2008

29. Physics-based models Can also model dynamic behavior of solids (Finite Element Methods) Stockman CSE/MSU Fall 2008

30. Tagged MRI: 3D interest points can be written to body! The MRI sensor tags living tissue and can sense its movement. Motion of a 3D tetrahedral finite elements model can then be analyzed. FMA model attempts to model the real physics of the heart. Work by Jinah Park and Dimitry Metaxes. Stockman CSE/MSU Fall 2008

31. Algorithms from computer graphics make mesh models from blobs • Marching squares applied to some connected image region (blob) • Marching cubes applied to some connected set of voxels (blob) • See a CG text for algorithms: see the visualization toolkit for software Stockman CSE/MSU Fall 2008

32. The octree for compression Stockman CSE/MSU Fall 2008

33. Generalized cylinders Stockman CSE/MSU Fall 2008

34. Generalized cylinders • component parts have axis • cross section function describes variation along axis • good for articulated objects, such as animals, tools • can be extracted from intensity images with difficulty Stockman CSE/MSU Fall 2008

35. Extracting a model from a segmented image region Courtesy of Chen and Medioni Stockman CSE/MSU Fall 2008

36. Interpreting frames from video • Can we match a frame region to a model? • What about a sequence of frames? • Can we determine what actions the body is doing? Stockman CSE/MSU Fall 2008

37. Generalized cylinders Stockman CSE/MSU Fall 2008

38. View class models Objects modeled by the distinct views that they can produce Stockman CSE/MSU Fall 2008

39. “aspect model” of a cube Stockman CSE/MSU Fall 2008

40. Recognition using an aspect model Stockman CSE/MSU Fall 2008

41. View class model of chair 2D Graph-matching (as in Ch 11) used to evaluate match. Stockman CSE/MSU Fall 2008

42. Side view classes of Ford Taurus (Chen and Stockman) These were made in the PRIP Lab from a scale model. Viewpoints in between can be generated from x and y curvature stored on boundary. Viewpoints matched to real image boundaries via optimization. Stockman CSE/MSU Fall 2008

43. Matching image edges to model limbs Could recognize car model at stoplight or gate or in car wash. Stockman CSE/MSU Fall 2008

44. Appearance-based models Using a basis of sub images; Using PCA to compress bases; Eigenfaces (see older .pdf slides 14C) Stockman CSE/MSU Fall 2008

45. Function-based modeling Object-oriented; What parts does the object have; What behaviors does it have; What can be done with it? (See plastic slides of Louise Starks’s work.) Stockman CSE/MSU Fall 2008

46. Louise Stark: chair model • Dozens of CAD models of chairs • Program analyzes model for * stable pose * seat of right size * height off ground right size * no obstruction to body on seat * program would accept a trash can (which could also pass as a container) Stockman CSE/MSU Fall 2008

47. Theory of affordances: J.J. Gibson • An object can be “sittable”: a large number of chair types, a box of certain size, a trash can turned over, … • An object can be “walkable”: the floor, ground, thick ice, bridge, ... • An object can be a “container”: a cup, a hat, a barrel, a box, … • An object can be “throwable”: a ball, a book, a coin, an apple, a small chair, … Stockman CSE/MSU Fall 2008

48. Minski’s theory of frames(Schank’s theory of scripts) • Frames are learned expectations – frame for a room, a car, a party, an argument, … • Frame is evoked by current situation – how? (hard) • Human “fills in” the details of the current frame (easier) Stockman CSE/MSU Fall 2008