Image Processing Algorithms Implementation Using Quantum Cellular Automata - Cellular Automata in Image Processing and Geometry - page 74

i1

-1

-1

i2

-1 out

-1

i3

i4 i5

Fig. 4.9 The QCA circuit for morphological erosion

218 nm 2 m 2 . It is covering an orthogonal re-

198 × that is approximately 0 . 043 µ

gion of 10 11 grids and the ratio of the area covered by QCA cells to the overall

orthogonal region area of the layout is 0

×

318. Fig. 4.10 presents the simulation re-
sults of the QCA morphological erosion operation, which confirms the correctness
of the design. As it is shown all possible value combinations of 5 inputs are applied
to the circuit. The output is set to logic “1” only when all inputs stay at logic “1”
otherwise is set to logic “0” indicating the AND gate behaviour.

.

4.5.2 QCA Implementation of Morphological Dilation

The QCA implementation of morphological dilation operation that will be described
in the following uses a cross 3 3 pixels structuring element. The procedure that

applies this dilation can be simplified as follows. First, superimpose the structur-
ing element over every 3

×

3 region of the image. Then, if the black pixels of the
structuring element coincides with one or more black pixels on the input image,
paint black the pixel of the output image that corresponds to the structuring element
origin. In other cases the pixels of the output image are left white.

×