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

[@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 :))

[@ЭйвейлАлександр]

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

[@Ravi-ks7ev]

The concept i was looking for .Thanks

[@eyithings]

Thank you for the explicit explanation.

[@musicgotmelike9668]

great video. Exactly what I was looking for!

[@ClashRoyaleLord]

thank you great vid love from Nigeria

[@amirsadrpour3456]

thank you for the video !

[@ucleminh1616]

Thank you so much for the lecture.

[@kommolafe]

Thank you very much for the video!

[@zhongjianluo3394]

good explaination. Thanks

[@muhammadwaseem_]

That was awesome

[@tanieadas]

255 x 0.9098 = 231.99 = 232

[@samanthamccormick7865]

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

[@andymoral]

God bless you!

[@tess2859]

This sounds like Sal Khan from Khan Academy

[@ransarawijitharathna7566]

thank you

[@qzorn4440]

interesting. thanks.

[@MohamedIbrahim-sj1td]

where is the next lecture?

[@ahmedtabti2264]

thank you