Computer Manual in MATLAB to Accompany Pattern by David G. Stork

By David G. Stork

The e-book is just too skinny and covers the matlab fundamentals in virtually 1/2 its pages! certain, there are attention-grabbing fabrics and software program on its website yet i would not spend the cash for it if knew that!

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Additional info for Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition

Example text

5. This figure shows that the difference operator identifies the edges of the image more clearly and with more responses per edge, which corroborates the conclusions drawn in Canny’s work (Canny, 1986) on the proper gradient operator under his constraints of SNR (signal to noise ratio) and simple response. 32 Pattern Recognition Techniques, Technology and Applications 350 Gradient 280 210 140 (a) 70 0 1 2379 4758 7136 9515 11893 Meter (m) 350 Gradient 280 210 140 (b) 70 0 1 2379 4758 7136 9515 11893 Meter (m) Fig.

H. Hunt & D. N. Wagner, Coding of natural scenes in primary visual cortex. Neuron 37, 2003, 703-718. Witkowski M. & Randell D. (2006) Modes of Attention and Inattention for a Model of Robot Perception. Proc. Towards Autonomous Robotic Systems, 2006, 246-253. Wolfe J. M. (1994). 0 A Revised model of visual search. Psychonomic Bulletin, 1(2), 1994, 202-238. Wolfe J. M. & Horowitz T. S. (2004). What attributes guide the deployment of visual attention and how do they do it?. Nature Reviews. Neuroscience, 5(6), 2004, 495501.

Two different gradient operators, Gaussian and difference, are evaluated to test which one is best suited to solve the current problem. The next step is the projection of the gradient, which simplifies the thresholding that must be carried out to eliminate noise from the gradient. Once the projection of the gradient is available, it is thresholded. The objective of the thresholding is to differentiate noise from real edges. An edge is found when there is data in the projection over the 26 Pattern Recognition Techniques, Technology and Applications threshold.

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