[Python] Morophological 구현 (모폴로지 연산 그림으로 설명)

오늘은 영상처리에서 자주 사용되는 모폴리지 처리에 대해 포스팅하겠다. 구현은 scipy 라이브러리의 ndimage 함수를 사용했다.

 

Morphological Operations

• An important part of image analysis involves understanding the shape of the objects in that image through morphological operations

 

• Morphology means form or structure

 

• The goal is to transform the structure or form of the objects using a structuring element

 

• Morphological operations can be applied on binary, grayscale and color images

 

- Dilation, Erosion, Opening, and Closing

 

 

Dilation

•The dilation of the image I with a structuring element S is denoted as (I + S)

 

1. Foreground pixels in an image grow or expand

 

2. Fill small holes in an object

 

3. Combine objects that are close enough to each other but are not connected

 

Dilation

 

[Dilation 구현]

Dilation 구현

 

Erosion

• The erosion of the image I and with a structuring element S is denoted as (I - S)

 

- Shrink objects in an image by removing pixels

 

- Erosion is opposite of dilation.

 

Erosion

 

Erosion 구현

 

Opening and Closing

• Opening and closing operations are complex morphological operations

 

• They are obtained by combining dilation and erosion

 

• Opening and closing can be performed on binary, grayscale and color images.

 

위: Opening, 아래: Closing

 

Opening

Opening = Erosion 후 Dilation 예시

 

• Opening generally smooths a contour in an image, breaking narrow isthmuses and eliminating thin protrusions

 

Opening 예시

 

Opening 구현

 

Closing

Opening = Dilation 후 Erosion 예시

 

• Closing tends to narrow smooth sections of contours, fusing narrow breaks and long thin gulfs, eliminating small holes, and filling gaps in contours

 

Closing 예시

 

Closing 구현