Things about pattern recognition OGD

Pattern recognition ● Simplify the input ● Extract features ● Process ● Learn? ● Output results

Binarization ● From 256^3 to 0..1 ● Thresholding ● Otsu ● Adaptive mean ● Many others

Simple features ● Runlength → fragile ● Projections → more robust ● Background vs foreground

Complex features ● Haar features → face detection in camera ● Wavelengths

Knowledge ● The program needs to “know” about things ● Learning ● Or just program it?

Machine learning ● Bayes ● SVM's ● Neural networks ● Loads of other things ● Your algorithm here?

Just program? ● Often best way to get started ● Understand the problem ● Get to know some of the features

The hard part ● Getting data ● Analyzing it ● Extracting features ● Lots of boring code ● Lots of manual labor

Wow I'm really smart ● Average brain better than supercomputer ● Massively parallel computer ● Still most of it not understood ● But it is simple for the smallest parts

Demo ● Modern dutch license plates ● How to detect and read

What do you see?

Dutch license plate ● Rectangular ● Mostly yellow ● Fixed ratio height-width ● Contains characters/numbbers ● Little blue thingy ● Usually a car around it

OpenCV

My IDE

Just yellowish pixels

Postprocessing Connected components ● Filter out components that are too small ● Calculate ratio of components ● Filter out anything that does not match ratio

No more garbage!

Working on the plate itself ● We now know where the plate is ● Extract it from original source ● Preprocessing ● Extract coco's ● Resize to 25x25 ● Store coco's

Manual processing ● Sort the stored images

Simple pixel comparison ● Works great ● Picture is the feature ● Simplest learning by example

Possible improvements ● Better plate detection ● Handling dirty plates ● Definitely some refactoring ;)

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