After smoothing the next step is the calculation of the gradient of the image. The gradient calculation leads to the detection of the possible edge strength and direction. This is executed by another convolution with a gradient operator. The most commonly used gradient operators are the Prewitt and the Sobel operators. Both operators perform a 2-D spatial gradient measurement on an image. The Sobel edge detector uses a pair of 3x3 convolution masks, one estimating the gradient in the x- direction (columns) and the other estimating the gradient in the y-direction (rows). The Sobel operators are presented in Fig. 2. Gradient magnitude and orientation is defined by the following equations: Our Canny implementation will be used by a lab on chip system on a Spartan 6 FPGA. Therefore we use the results produced by the Spartan 6 synthesis for our simulation. We simulate the design by using 3 different image file sources which are 8bit grayscale files of varying sizes. In Fig. 6 the input and the output files of the Canny implementations are demonstrated. Fig. 6.c and 6.d is an example frame of a video for a lab on chip experiment. The timing results are presented in Table II. TABLE II. CANNY TIME RESULTS As can be seen for an image of 1.2Mpixel we have achieved a computation time of 3.09ms. Thus, for an image of 1 Mpixel a rate of 396 frames per second has been achieved, which is far beyond our set specifications for a real-time design. On the Virtex-5 the throughput reaches the number of 580 frames per second for 1Mpixel images. Even in the low-end Spartan-3E Figure 3. 4-pixel parallel Gaussian smoothing calculation and addition/subtraction to increase speed. The results are stored in a cache used as an input for the next stage. (a) lena input, (b) lena output, (c) HCLAchip input, (d) HCLAchip output, (e) disc-brake input, (f) disc-brake output
