A novel automatic thresholding segmentation method with local adaptive thresholds
This addresses the challenge of accurate image segmentation for applications like medical imaging or object detection, though it appears incremental as it builds on existing threshold-based approaches.
The paper tackles the problem of segmenting bright objects from dark backgrounds in grayscale images by proposing a novel automatic thresholding method with local adaptive thresholds, which mimics human expectations even with fuzzy boundaries.
A novel method for segmenting bright objects from dark background for grayscale image is proposed. The concept of this method can be stated simply as: to pick out the local-thinnest bands on the grayscale grade-map. It turns out to be a threshold-based method with local adaptive thresholds, where each local threshold is determined by requiring the average normal-direction gradient on the object boundary to be local minimal. The method is highly automatic and the segmentation mimics a man's natural expectation even the object boundaries are fuzzy.