## Image Processing CS 6640 Project -5 (Feature & Object Detection)

5 stars based on 60 reviews

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 matlab edge detection binary image processing using canny method small pixels noises on the assumption matlab edge detection binary image processing using canny method 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 matlab edge detection binary image processing using canny method 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:

## Online share trading brokerage comparison india

### Binary options stock picks weekends

Edge detection is the operation of finding the boundaries of objects present in an image. Classical methods use the image gradient or approximations of the image gradient to detect edge location. If you have a noisy image it is a good practice to reduce the noise before detecting the edges.

This is because noise might lead to false steps during edge detection, specially when gradient based methods are employed. Noise may produce unreliable oscillating derivative values across short distances. Let's investigate the profile of rows of our HELA image its red channel: Experiment the edge detection methods provided by the edge function in the smoothed image G.

Which one s works best? Try also after a Gaussian filter. Use the returned value for the gradient threshold to help you calibrate your edge detection. Kmeans is an iterative clustering technique that separates a data set into K mutually exclusive clusters, such that members within a cluster are closer to each other and to the cluster centroid its mean than to members and centroid of any other cluster.

When applied to perform image segmentation, Kmeans partitions the image into regions of similar intensities. It works very well for images with close to homogeneous regions. In MATLAB, use the function kmeans note that kmeans is not part of the image processing toolbox as it can be used for general data sets; it is a function of the statistics toolbox: We will need to reshape the image matrix to a format acceptable by kmeans a flat array: As it is, the kmeans segmentation seems to be a bit inferior when compared to the threshold segmentation we achieved in the previous lecture for the HELA nuclei image.

But we can easily adjust the kmeans result using morphological operations try also with imfill to fill holes:. Let's use a Gaussian filter: Edge contour detection Edge detection is the operation of finding the boundaries of objects present in an image. K-Means Kmeans is an iterative clustering technique that separates a data set into K mutually exclusive clusters, such that members within a cluster are closer to each other and to the cluster centroid its mean than to members and centroid of any other cluster.

But we can easily adjust the kmeans result using morphological operations try also with imfill to fill holes: Practice 1 - Experiment with the Kmeans demo for color image segmentation available in the image processing toolbox, "Color-Based Segmentation Using K-Means Clustering". Use the HELA image and try clustering in 2 regions, red and green only, since the blue channel is not very expressive; 2 - Write a script to do segmentation using the k-means procedure above.

Use as parameters things like filter type and its kernel size, what to use for 'Replicates' and 'start', and other values that might influence the k-means results.

Repeat the kmeans segmentation above for distinct values for 'Replicates' and see if you notice any difference.