Category: Theory
9. Constraints: Visual Object Recognition
Borders and faces orientation Discerning borders of objects and face orientation with vectors, an initial computer vision theory seemed plausible but too difficult to implement. Orthographic projection In orthographic projection, the correspondence of a system of points of three known objects and one unknown object creates a system of equations with a unique solution of…
8. Constraints: Search, Domain Reduction
https://www.youtube.com/watch?v=d1KyYyLmGpA Domain Reduction Algorithm Vocabulary Variable v: something that can have an assignment Value x: something that can be assigned Domain d: set of all different values Constraint c: limit on variable values With a depth-first search, the domain reduction algorithm goes back up one node when it is unable to comply with a constraint. It…
7. Constraints: Interpreting Line Drawings
Computer vision Empirical approach Using lines on pictures of real-world objects, the edges between shapes could serve to identify the number of objects in it. The different intersections possible generally form two types of trihedral vertexes to identify shapes: arrow vertexes fork vertexes Theoretical approach A second approach uses convex and concave lines and boundaries…
6. Search: Games, Minimax, and Alpha-Beta
How a computer can play games (like Chess) How human play chess: mixing analysis, strategy, tactics to choose a move If-Then rules Look ahead and evaluate British museum algorithm To evaluate the potential of the next situation of the board, (methods 2 and 3) we can use a linear scoring polynomial to give number to…
5. Search: Optimal, Branch and Bound, A*
Optimal search trees Finding the best possible sequence of choices. Getting closer to the goal is generally considered good, but it may lead to dead ends or non-optimal choices. Oracle Knowing the minimum path length to the goal, the search algorithm records the length of path already extended and always extends the shortest path first until…