'Quad trees' presentation slideshows

Computer Graphics

Computer Graphics. Modeling. Modeling. Modeling is simply the process of creating 3D objects Many different processes to create models Many different representations of model data

735 views • 73 slides

Collisions and Intersections

Collisions and Intersections. When objects move, test for collision. When projecting surfaces, check for intersections. ( Many slides adapted from Amitabh Varshney). Collision Strategy. Brute force test. Fine if few shapes. Test by bounding with simpler shape.

552 views • 41 slides

Quad Trees

27. dest. 24. 15. 8. source. Quad Trees. Region data vs. point data. Roads and rivers in a country/state. Which rivers flow through Florida? Which roads cross a river? Network firewalls. (source prefix, destination prefix, action) (01*, 110*, drop packet). Quad Trees. Binary images.

410 views • 33 slides

Using interval analysis to generate quad-trees of piecewise constraints

376 views • 22 slides

Ch 10 Image Segmentation

Ch 10 Image Segmentation. Ideally, partition an image into regions corresponding to real world objects. Goals of segmentation. Segments formed by K-means. Segmentation attempted via contour/boundary detection. Clustering versus region-growing. Clustering versus region-growing.

511 views • 21 slides

More Specialized Data Structures

More Specialized Data Structures. String data structures Spatial data structures. String Data Structures. String Operations. String indexing Pattern matching Find pattern P in text T Find common substrings among a set of a strings Application Domains Bioinformatics Google search!.

326 views • 14 slides

More Specialized Data Structures

More Specialized Data Structures. String data structures Spatial data structures. String Data Structures. String Operations. String indexing Pattern matching Find pattern P in text T Find common substrings among a set of a strings Application Domains Bioinformatics Google search!.

229 views • 14 slides

R-Trees

R-Trees. Presented By N Jaggan 2001A4A7223. Spatial Data Objects. Cover Multi-Dimensional Spaces Not Represented well by Points Examples include Maps objects like countries, Data elements like gears etc in a machine drawing etc. Why indexing?. Queries should take least possible time

327 views • 11 slides

Barnes-Hut Algorithm CS498lvk

248 views • 8 slides