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Data Processing – CH 11 – Rees
transmission and storage of data image processing
preprocessing radiometric correction geometric correction image enhancement contrast modification spatial filtering band transformation vegetation index principal component analysis
image classification
© Cambridge University Press 2011
satellite receiving station mask
LAB 5
LAB 5
© Cambridge University Press 2011
contrast modification
© Cambridge University Press 2011
linear contrast stretch
© Cambridge University Press 2011
linear contrast stretch
© Cambridge University Press 2011
histogram equalization
© Cambridge University Press 2011
histogram equalization
© Cambridge University Press 2011
smoothing or low-pass filter (boxcar)
© Cambridge University Press 2011
sharpening or high-pass filter
© Cambridge University Press 2011
Laplacian filter
© Cambridge University Press 2011
Sobel or derivative filter
convolution theorem
matlab demo
© Cambridge University Press 2011
filtering in fourier transform domain
Facial recognition breakthrough: 'Deep Dense' software spots faces in images even if they're partially hidden or UPSIDE DOWN • Algorithm was built by Yahoo Labs in California and Stanford University
• It built on Viola-Jones algorithm which spots front-facing images of people
• Researchers used a form of machine learning known as a deep convolutional neural network
• This involves training a computer to spot features in a database of images
• The algorithm can identify faces from various angles, when part of the face is hidden and even upside down
• It doesn't recognise who a face belongs to, but could be trained to do so
• Facebook and Google use similar networks to improve image recognition http://www.dailymail.co.uk/
The Deep Dense Face Detector algorithm was built by Yahoo Labs in California and Stanford University. The researchers used a form of machine learning known as a deep convolutional neural network to train a computer to spot facial features (pictured) in a database of images.
© Cambridge University Press 2011
median filter
© Cambridge University Press 2011
normalized difference vegetation index (NDVI)
– red .64 µm – near IR .8 µm
NDVI = ri − rrri + rr
ri
rr
Reflectance of green-leafed vegetation is low in the red part of the spectrum because of absorption by chlorophyll and high in the near infrared.
principal component analysis matlab demo
principal component analysis
matlab demo
Data Processing – CH 11 – Rees
transmission and storage of data image processing
preprocessing radiometric correction geometric correction image enhancement contrast modification spatial filtering band transformation vegetation index principal component analysis
image classification
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