User:Cristinac/Day4

Day four

Big Data and Discrimination

Deconstructing Harry

Guttorm Guttormsgaard

Asger Jorn

metadata of information; data gallery; average colour of the image, timestamp, face recognition, average colour data, what could a photo gallery mean?

Gaussian blur (many image treatments begin with)

derivates

http://programmingcomputervision.com/

a gradient has a magnitude and a direction (like a vector)

Search By Image - Sebastian Schmieg

Control detection

contours that are detected are not continuous, but they are fragments and there is an extra step that determines what kind of fragments got together and create an extra step

Volterra Kernel Training/Identification System

http://www.sciencedirect.com/science/article/pii/S0952197612002461

statistical, not logical model of the face

behind the algorithm is a manual work that is done by people repeatedly

every detail of the face is annotated

labour conditions

Training data to feed the classifier : no image exists in isolation

False positive : images that have been selected as containing a face when they don’t

https://en.wikipedia.org/wiki/Ghostwriter

cvdazzle.com - techniques to avoid face detection

the same algorithm can be fed with any kind of statistical data; ex: banana recognition

sort by face

http://www.cise.ufl.edu/~arunava/papers/cvpr09.pdf

https://en.wikipedia.org/wiki/Volterra_series

CSV-no space for metadata, no authorship information

https://okfn.org/

frictionless data http://centraldedados.pt/

adding “I think” at the end of every paragraph

iPython