Extraction of Normalized Central Moments - Excellent Invariance to Translation and Scaling
- Login to Download
- 1 Credits
Resource Overview
Extracting normalized central moments demonstrates superior invariance to translation and scaling transformations. This method appears simpler and more reliable compared to Hu moments for computer vision applications. Please correct me if any technical inaccuracies exist. Contact qq254730570 for further discussion.
Detailed Documentation
When extracting image features, normalized central moments present an excellent choice for pattern recognition tasks. This approach maintains robust invariance to both translation and scaling transformations, ensuring consistent feature extraction results across various imaging conditions. Unlike Hu moments which involve complex polynomial combinations and sometimes demonstrate reliability issues, normalized central moments offer a more straightforward computational approach based on standardized moment calculations relative to the centroid. While these observations reflect my personal experience in computer vision implementation, I welcome technical corrections from the community. For additional information regarding image feature extraction methodologies or implementation details involving OpenCV's moments() function and central moment normalization techniques, please contact qq254730570.
- Login to Download
- 1 Credits