Introduction to Histogram Equalization

In this lecture we show how an intensity transformation whose functional shape is derived from an image's cumulative frequency plot can be used to distribute the image's intensity values more uniformly and nearly equalize an image's histogram

[@ma007mun]

At last, somebody understood what I was looking for. Kudos

[@ЭйвейлАлександр]

Attention! 1:04 255*0.6493 = 165.5715 1:10 255*0.9098 = 231.999

[@birgitchen]

very good explained, also the "introduction to image histograms"... finally i understand what we're doing in class :D thanks a lot!! an hope for more :))

[@aryamehta]

Holy shit this is so cool. Who tf came up with this, a wizard

[@eyithings]

Thank you for the explicit explanation.

[@Ravi-ks7ev]

The concept i was looking for .Thanks

[@musicgotmelike9668]

great video. Exactly what I was looking for!

[@huub_al]

thank you great vid love from Nigeria

[@ucleminh1616]

Thank you so much for the lecture.

[@samanthamccormick7865]

I've fallen asleep every time I try to watch this video. You should do sleep podcasts

[@amirsadrpour3456]

thank you for the video !

[@zhongjianluo3394]

good explaination. Thanks

[@muhammadwaseem_]

That was awesome

[@kommolafe]

Thank you very much for the video!

[@tanieadas]

255 x 0.9098 = 231.99 = 232

[@tess2859]

This sounds like Sal Khan from Khan Academy

[@andymoral]

God bless you!

[@MohamedIbrahim-sj1td]

where is the next lecture?

[@qzorn4440]

interesting. thanks.

[@ahmedtabti2264]

thank you

[@ransarawijitharathna7566]

thank you