Name
Abstract | ||
|---|---|---|
We introduce a technique that can automatically tune the parameters of a rule-based computer vision system comprised of thresholds, combinational logic, and time constants. This lets us retain the flexibility and perspicacity of a conventionally structured system while allowing us to perform approximate gradient descent using labeled data. While this is only a heuristic procedure, as far as we are aware there is no other efficient technique for tuning such systems. We describe the components of the system and the associated supervised learning mechanism. We also demonstrate the utility of the algorithm by comparing its performance versus hand tuning for an automotive headlight controller. Despite having over 100 parameters, the method is able to profitably adjust the system values given just the desired output for a number of videos. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Control theory,Gradient descent,Computer science,Combinational logic,Supervised learning,Artificial intelligence,Labeled data,Perspicacity,Machine learning,Heuristic procedure,Automotive industry |
DocType | Volume | Citations |
Journal | abs/1808.00361 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan H. Connell | 1 | 712 | 60.10 |
| Benjamin Herta | 2 | 109 | 8.06 |