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Published on: September 28, 2018
The converging squares algorithm: an efficient method for locating peaks in multidimensions
1Department of Electrical Engineering and the Robotics Institute, Carnegie-Mellon University, Pittsburgh, PA 15213; Bell Laboratories, Murray Hill, NJ 07974.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
The converging squares algorithm objectively and efficiently finds peaks in complex data. This noise-robust method enhances computational speed for applications in image analysis and industrial inspection.
Area of Science:
- Data analysis
- Computational geometry
- Image processing
Background:
- Conventional peak picking methods can be sensitive to noise and require parameter tuning.
- Objective and efficient algorithms are needed for analyzing multi-dimensional sampled data.
Purpose of the Study:
- To introduce and detail the converging squares algorithm for peak detection.
- To highlight the algorithm's robustness, objectivity, and computational efficiency.
Main Methods:
- The converging squares algorithm utilizes a resolution pyramid structure.
- The method is detailed for 2D and 3D data.
- Quantitative comparisons with conventional peak picking techniques were performed.
Main Results:
- The algorithm demonstrates robustness against noise and data type variations.
- It offers objective results without empirical parameter adjustments.
- Significant computational efficiency was observed compared to traditional methods.
Conclusions:
- The converging squares algorithm provides a superior alternative for peak detection in multi-dimensional data.
- Its efficiency and noise immunity make it suitable for biomedical image analysis and industrial inspection.
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