Opening and closing

Opening and closing morphological operations are fundamental techniques used in image processing, primarily for manipulating binary or grayscale images. These operations involve applying a structuring element (or kernel) to an image, and they are typically used for noise removal, shape extraction, and image enhancement.

Opening Operation

Definition: Opening is the process of eroding an image first and then dilating the eroded image. It’s denoted as A ∘ B, where A is the image and B is the structuring element (kernel).

Purpose

Removes small objects or noise from the foreground (white regions in a binary image). Helps in separating connected objects in an image.

How it Works

Erosion: Shrinks the foreground objects by eroding the boundaries. Dilation: Expands the eroded objects, restoring the size of the remaining objects after erosion. Result: Small white regions (noise) are removed, and the shape of the larger objects is preserved.

Use Case: Cleaning up noise in binary images.

Opening with OpenCV

This morphological operation require a kernel (or structuring elements) to process.

The morphologyEx function performs different kinds of morphological operations on the given image using a specified kernel. The cv2.MORPH_OPEN option processes an opening effect on the image.

image_opening = cv2.morphologyEx(grayscale_image, cv2.MORPH_OPEN, cross_kernel_3)

This function returns an array with the same shape as the initial image. You can then display the image with the standard imshow function of OpenCV.

Results

../_images/images_opening_cross_3.png — Fig. 18 Example of opening (morphological) operation on an image (Cross kernel of size 3).

Closing Operation

Definition: Closing is the reverse of opening. It first dilates the image and then erodes it. It’s denoted as A • B, where A is the image and B is the structuring element (kernel).

Purpose

Closes small holes or gaps in the foreground (white regions in a binary image). Connects or “fills” small breaks in the objects.

How it Works

Dilation: Expands the foreground objects by enlarging the boundaries. Erosion: Shrinks the dilated objects back to their original size, but with small holes or gaps filled. Result: Small black regions (holes) within the objects are removed, and small gaps between objects are closed.

Use Case: Filling small holes and connecting close objects.

Closing with OpenCV

This morphological operation require a kernel (or structuring elements) to process.

The morphologyEx function performs different kinds of morphological operations on the given image using a specified kernel. The cv2.MORPH_CLOSE option processes a closing effect on the image.

image_opening = cv2.morphologyEx(grayscale_image, cv2.MORPH_CLOSE, cross_kernel_3)

This function returns an array with the same shape as the initial image. You can then display the image with the standard imshow function of OpenCV.

Results

../_images/images_closing_cross_3.png — Fig. 19 Example of closing (morphological) operation on an image (Cross kernel of size 3).