모폴로지 연산
팽창, 침식, 재구성 및 기타 모폴로지 연산 수행
모폴로지는 형태에 따라 영상을 처리하는 광범위한 영상 처리 연산 집합입니다. 모폴로지 연산에서는 영상의 각 픽셀이 이웃의 다른 픽셀 값에 따라 조정됩니다. 이웃의 크기와 형태를 선택하여 입력 영상의 특정 형태에 민감한 모폴로지 연산을 생성할 수 있습니다.
함수
도움말 항목
모폴로지의 요소
- 모폴로지 연산의 유형
가장 기본적인 모폴로지 연산은 팽창과 침식입니다. 팽창과 침식을 결합하여 더욱 특화된 연산을 수행할 수도 있습니다. - Morphological Reconstruction
Morphological reconstruction is used to extract marked objects from an image without changing the object size or shape. - Structuring Elements
A structuring element defines the neighborhood used to process each pixel. A structuring element influences the size and shape of objects to process in the image. - Border Padding for Morphology
Morphological dilation and erosion pad the image border in different ways to avoid border effects. - Pixel Connectivity
Connectivity determines whether a center pixel and adjacent pixels belong to the same object. - Lookup Table Operations
A lookup table is a vector in which each element represents the different permutations of pixels in a neighborhood. Lookup tables are useful for custom erosion and dilation operations.
모폴로지의 응용
- Dilate an Image to Enlarge a Shape
Dilation adds pixels to boundary of an object. Dilation makes objects more visible and fills in small holes in the object. - Remove Thin Lines Using Erosion
Erosion removes pixels from the boundary of an object. Erosion removes islands and small objects so that only substantive objects remain. - Use Morphological Opening to Extract Large Image Features
You can use morphological opening to remove small objects from an image while preserving the shape and size of larger objects in the image. - Flood-Fill Operations
A flood fill operation assigns a uniform pixel value to connected pixels, stopping at object boundaries. - Find Image Peaks and Valleys
You can use neighborhood processing to find global and regional minima and maxima in images.