In the previous article, the Opening operator was specified which was applying the erosion operation after dilation. It helps in removing the internal noise in the image. Closing is similar to the opening operation. In closing operation, the basic premise is that the closing is opening performed in reverse. It is defined simply as a dilation followed by an erosion using the same structuring element used in the opening operation.
Syntax: cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
-> image: Input Image array.
-> cv2.MORPH_CLOSE: Applying the Morphological Closing operation.
-> kernel: Structuring element.
Below is the Python code explaining Closing Morphological Operation –
- Python | Morphological Operations in Image Processing (Opening) | Set-1
- Python | Morphological Operations in Image Processing (Gradient) | Set-3
- Image segmentation using Morphological operations in Python
- Difference between Opening and Closing in Digital Image Processing
- Morphological operations in MATLAB
- Point Processing in Image Processing using Python-OpenCV
- Image Processing in Java | Set 3 (Colored image to greyscale image conversion)
- Image Processing in Java | Set 4 (Colored image to Negative image conversion)
- Image Processing in Java | Set 6 (Colored image to Sepia image conversion)
- Getting started with Scikit-image: image processing in Python
- Mahotas - Closing Process on Image
- Mahotas – Closing Holes in Image
- Image Processing in MATLAB | Fundamental Operations
- Image Processing in Java | Set 5 (Colored to Red Green Blue Image Conversion)
- Image Processing in Java | Set 7 (Creating a random pixel image)
- Image Processing in Java | Set 8 (Creating mirror image)
- Image Processing in Java | Set 11 (Changing orientation of image)
- Image Processing in Java | Set 10 ( Watermarking an image )
- Image Edge Detection Operators in Digital Image Processing
- Image Processing in Python (Scaling, Rotating, Shifting and Edge Detection)
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