What is segmentation techniques?

What is segmentation techniques?

In image segmentation, you divide an image into various parts that have similar attributes. By using image segmentation techniques, you can divide and group-specific pixels from an image, assign them labels and classify further pixels according to these labels.

How do I segment an image in OpenCV?

Image Segmentation using OpenCV – Extracting specific Areas of an…

1. Segmentation and contours.
2. Hierarchy and retrieval mode.
3. Approximating contours and finding their convex hull.
4. Conex Hull.
5. Matching Contour.
6. Identifying Shapes (circle, rectangle, triangle, square, star)
7. Line detection.
8. Blob detection.

How do I change the color of an image in OpenCV?

1. We can Convert BGR and RGB with OpenCV function cvtColor().
2. im_rgb = cv2.cvtColor(im_cv, cv2.COLOR_BGR2RGB)
3. Image.fromarray(im_rgb).save(‘path of your image’)
4. Another format: RGB to BGR.
5. im_pillow = np.array(Image.open(‘path of your image’))
6. im_bgr = cv2.cvtColor(im_pillow, cv2.COLOR_RGB2BGR)

How are blobs used to segment an image?

To convert the image, we use the Otsu’s Threshold method (https://en.wikipedia.org/wiki/Otsu%27s_method). This method is used because it’s a parametric method to select a threshold value that basically maximizes the variance between the light and dark pixels in the resulting image. The regions in the resulting binary image are called blobs.

Which is the best technique for blob detection?

One of the most promising techniques is called Blob Detection. A Blob, in a sense, is anything that is considered a large object or anything bright in a dark background, in images, we can generalize it as a group of pixel values that forms a somewhat colony or a large object that is distinguishable from its background.

When to binarize image before or after blob detection?

Now, let us binarize the image: For binarizing, we used a threshold on the pixel value of 0.6 since we can see on the sample plotline that there is a separation within this value. We usually binarize our image before blob detection since it is easier to work around the smaller dimensions and normalized values.

What is the purpose of blob detection in OpenCV?

A Blob is a group of connected pixels in an image that share some common property ( E.g grayscale value ). In the image above, the dark connected regions are blobs, and the goal of blob detection is to identify and mark these regions. OpenCV provides a convenient way to detect blobs and filter them based on different characteristics.