1. Stockman CSE/MSU Fall 20081 Models and Matching Methods of modeling objects and their environments; Methods of matching models to sensed data for recogniton

2. Stockman CSE/MSU Fall 20082 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)

3. Stockman CSE/MSU Fall 20083 Models are what models do

4. Stockman CSE/MSU Fall 20084 What do models do?

5. Stockman CSE/MSU Fall 20085 Vertex-edge-face models Polyhedra and extensions; Model the surface of objects

6. Stockman CSE/MSU Fall 20086 Vertex-Edge-Face model

7. Stockman CSE/MSU Fall 20087 Sample object All surfaces are planar or cylindrical

8. Stockman CSE/MSU Fall 20088 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!)

9. Stockman CSE/MSU Fall 20089 Triangular meshes Very general and used by most CAD systems.

10. Stockman CSE/MSU Fall 200810 Texture-mapped mesh dog Courtesy of Kari Puli 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. 3D SURFACE MODEL SURFACE PLUS TEXTURE

11. Stockman CSE/MSU Fall 200811 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.)

12. Stockman CSE/MSU Fall 200812 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.

13. Stockman CSE/MSU Fall 200813 Mesh characteristics + can be easy to generate from scanned data

14. Stockman CSE/MSU Fall 200814 Making mesh models

15. Stockman CSE/MSU Fall 200815 Marching cubes http://www.exaflop.org/docs/marchcubes/ (James Sharman) http://www.exaflop.org/docs/marchcubes/ "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. 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

16. Stockman CSE/MSU Fall 200816 Marching in 3D space F(s, r, c) Some voxel corners are above threshold t and some are below.

17. Stockman CSE/MSU Fall 200817 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

18. Stockman CSE/MSU Fall 200818 Segmentation of Scutigera a tiny crablike organism Slice j of material density F( sj, r, c ) “thresholded” volume

19. Stockman CSE/MSU Fall 200819 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

20. Stockman CSE/MSU Fall 200820 Scanning technique abstraction CCD camera (row) material sample X-ray planes scintillator Pin head rotate 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

21. Stockman CSE/MSU Fall 200821 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)

22. Stockman CSE/MSU Fall 200822 Presentation of Results to User Can explore the 3D data using rotation/translation Can create stereo images from 3D data

23. Stockman CSE/MSU Fall 200823 Physics-based models Can be used to make meshes; Meshes retain perfect topology; Can span spots of bad or no data

24. Stockman CSE/MSU Fall 200824 Physics-based modeling

25. Stockman CSE/MSU Fall 200825 Forces move points on the model; halt at scanned data

26. Stockman CSE/MSU Fall 200826 Fitting an active contour to image data

27. Stockman CSE/MSU Fall 200827 Balloon model for closed object surface Courtesy of Chen and Medioni

28. Stockman CSE/MSU Fall 200828 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

29. Stockman CSE/MSU Fall 200829 Physics-based models Can also model dynamic behavior of solids (Finite Element Methods)

30. Stockman CSE/MSU Fall 200830 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.

31. Stockman CSE/MSU Fall 200831 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

32. Stockman CSE/MSU Fall 200832 The octree for compression

33. Stockman CSE/MSU Fall 200833 Generalized cylinders

34. Stockman CSE/MSU Fall 200834 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

35. Stockman CSE/MSU Fall 200835 Extracting a model from a segmented image region Courtesy of Chen and Medioni

36. Stockman CSE/MSU Fall 200836 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?

37. Stockman CSE/MSU Fall 200837 Generalized cylinders

38. Stockman CSE/MSU Fall 200838 View class models Objects modeled by the distinct views that they can produce

39. Stockman CSE/MSU Fall 200839 “aspect model” of a cube

40. Stockman CSE/MSU Fall 200840 Recognition using an aspect model

41. Stockman CSE/MSU Fall 200841 View class model of chair 2D Graph-matching (as in Ch 11) used to evaluate match.

42. Stockman CSE/MSU Fall 200842 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.

43. Stockman CSE/MSU Fall 200843 Matching image edges to model limbs Could recognize car model at stoplight or gate or in car wash.

44. Stockman CSE/MSU Fall 200844 Appearance-based models Using a basis of sub images; Using PCA to compress bases; Eigenfaces (see older.pdf slides 14C)

45. Stockman CSE/MSU Fall 200845 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.)

46. Stockman CSE/MSU Fall 200846 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)

47. Stockman CSE/MSU Fall 200847 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, …

48. Stockman CSE/MSU Fall 200848 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)

49. Stockman CSE/MSU Fall 200849 Make a frame for my house Item 1 Item 2 Item 3 Item 4 Item 5 Item 6