이 번역 페이지는 최신 내용을 담고 있지 않습니다. 최신 내용을 영문으로 보려면 여기를 클릭하십시오.
이웃 및 블록 처리
일부 영상 처리 연산에서는 전체 영상을 한 번에 처리하지 않고 영상을 섹션으로 나눠 처리합니다. 슬라이딩 이웃 연산(Sliding Neighborhood Operation)은 각 픽셀 이웃에 알고리즘을 적용하여 영상을 한 번에 한 픽셀씩 처리합니다. 개별 블록 처리(Distinct Block Processing)에서는 영상을 겹치지 않는 동일한 크기의 블록으로 나누고 각 개별 블록에 알고리즘을 적용합니다. 그런 다음 이웃과 블록을 다시 조합하여 출력 영상을 구성합니다.
함수
클래스
ImageAdapter | Interface for image I/O |
도움말 항목
- Neighborhood or Block Processing: An Overview
Divide an image into sections, called blocks or neighborhoods, to reduce the memory needed to process the image.
- Sliding Neighborhood Operations
A sliding neighborhood operation is performed one pixel at a time using information about the pixel’s neighborhood.
- Distinct Block Processing
Distinct block processing divides an image into nonoverlapping rectangular sections that can be processed individually.
- Block Size and Performance
Using larger block sizes reduces overall computation time but requires more memory to process each block.
- Use Column-wise Processing to Speed Up Sliding Neighborhood or Distinct Block Operations
Reshape sliding neighborhoods and distinct blocks to reduce the execution time of processing an image.
- Perform Block Processing on Image Files in Unsupported Formats
To work with image data in file formats not supported by block processing functions, construct a class that manages files based on region.
- Parallel Block Processing on Large Image Files
If you have a Parallel Computing Toolbox™ license, you can take advantage of multiple processor cores on your machine to improve the performance of
blockproc
.