TQWT has been usen on images for filtering and Feature extraction. Q factor is chosen according to the oscillatory behaviour of the signal. This can be understood in case of 1D signal. But how can the Q factor be understood when applied to images? How do we determine what is the value of Q to be chosen?
ECG steganography based on tunable Q‐factor wavelet transform and singular value decomposition
Yes, the TQWTs are used in many filtering and feature extraction applications in image processing. Face recognition and verification is one of the well known application. The Q factor is able to differentiate features. As the Q factor increased, the thick features can be better detected and localized then thin features which may not be detectable by low values of Q. It also dependents on type of wavelet and its direction ability.
Thank you sir. What do thick and thin features mean sir?
Can you please explain..
It is related to feature thickness or feature's maximum axis length or maximum distance of its boundary pixel.
Thank you Gupta sir!!
I think I dont clearly understand feature thickness. Can you please direct me so that I can understand it or suggest some references sir..
Reference paper -
Selesnick I W, "Wavelet transform with tunable Q-Factor," IEEE Trans. Sig. Process., vol.59, no.8, Aug.2011.
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