Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object.This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.
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