Multiple resolution imagery and texture analysis
Pattern Recognition
The factor of scale in remote sensing
Remote Sensing of Environment
Pattern Recognition Letters
Practical image processing in C: acquisition, manipulation and storage: hardware, software, images and text
Handbook of pattern recognition and image processing (vol. 2): computer vision
Handbook of pattern recognition and image processing (vol. 2): computer vision
Advanced computational methods for spatial information extraction
Computers & Geosciences
Digital Image Processing; A Practical Primer
Digital Image Processing; A Practical Primer
Digital Picture Processing
Automated Smoothing of Image and Other Regularly Spaced Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The standard method of programming kernel-based image processing tasks requires an exponential increase in processing time as kernel size increases. A new approach to programming kernel-based image processing tasks is presented that requires near constant time to process a given image at any given kernel size. A comparison is made using a standard method of average filtering and a new fast algorithm. Each algorithm was tested five times and processing times were averaged for kernel sizes ranging from 3 × 3 to 101 × 101. Results show the new algorithm is faster at all kernel sizes and orders of magnitude faster at larger kernel sizes (e.g., nearly a thousand times faster when using a 101 × 101 kernel size). A discussion is provided on other types of kernel-based image processing tasks that can also use this same programming approach.