## Auto binary signals review 2018

17 comments### Make money with binary trading

Since edge detection is susceptible to noise in the image, first step is to remove the noise in the image with a 5x5 Gaussian filter. We have already seen this in previous chapters. From these two images, we can find edge gradient and direction for each pixel as follows:.

Gradient direction is always perpendicular to edges. It is rounded to one of four angles representing vertical, horizontal and two diagonal directions. After getting gradient magnitude and direction, a full scan of image is done to remove any unwanted pixels which may not constitute the edge.

For this, at every pixel, pixel is checked if it is a local maximum in its neighborhood in the direction of gradient. Check the image below:. Point A is on the edge in vertical direction. Gradient direction is normal to the edge.

Point B and C are in gradient directions. So point A is checked with point B and C to see if it forms a local maximum.

If so, it is considered for next stage, otherwise, it is suppressed put to zero. This stage decides which are all edges are really edges and which are not. For this, we need two threshold values, minVal and maxVal. Any edges with intensity gradient more than maxVal are sure to be edges and those below minVal are sure to be non-edges, so discarded. Those who lie between these two thresholds are classified edges or non-edges based on their connectivity.

If they are connected to "sure-edge" pixels, they are considered to be part of edges. Otherwise, they are also discarded. See the image below:. The edge A is above the maxVal, so considered as "sure-edge". Although edge C is below maxVal, it is connected to edge A, so that also considered as valid edge and we get that full curve.

But edge B, although it is above minVal and is in same region as that of edge C, it is not connected to any "sure-edge", so that is discarded. So it is very important that we have to select minVal and maxVal accordingly to get the correct result. This stage also removes small pixels noises on the assumption that edges are long lines.

OpenCV puts all the above in single function, cv. We will see how to use it. First argument is our input image. Second and third arguments are our minVal and maxVal respectively. It is the size of Sobel kernel used for find image gradients. By default it is 3. Last argument is L2gradient which specifies the equation for finding gradient magnitude. If it is True, it uses the equation mentioned above which is more accurate, otherwise it uses this function: By default, it is False.

Canny Theory Canny Edge Detection is a popular edge detection algorithm. It was developed by John F. Canny in It is a multi-stage algorithm and we will go through each stages. Noise Reduction Since edge detection is susceptible to noise in the image, first step is to remove the noise in the image with a 5x5 Gaussian filter. From these two images, we can find edge gradient and direction for each pixel as follows: Non-maximum Suppression After getting gradient magnitude and direction, a full scan of image is done to remove any unwanted pixels which may not constitute the edge.

Check the image below: