Title :
Residual analysis for feature detection
Author :
Chen, Ming-Hua ; Lee, David ; Pavlidis, Theo
Author_Institution :
Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA
fDate :
1/1/1991 12:00:00 AM
Abstract :
It is shown that in a very simple form residual analysis achieves results that are at least as good as if not better than those obtained by other techniques. There are many ways for extensions of the method. For example, moving average filters of regularization can be used to obtain the residual images. Also, the strength of the correlation, measured by Drr(O), can be used to eliminate noise, weak edges, etc. A more ambitious extension is by considering smoothing filters that leave invariant the function representing the reflectance from smooth surfaces
Keywords :
computerised pattern recognition; computerised picture processing; filtering and prediction theory; invariance; correlation; feature detection; moving average filters; reflectance invariant; regularization; residual analysis; residual images; smooth surfaces; smoothing filters; Autocorrelation; Computer vision; Filters; Helium; Image edge detection; Laboratories; Machine vision; Numerical analysis; Pattern recognition; Smoothing methods;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on