Histogram-based morphological edge detector

We present a new edge detector for automatic extraction of oceanographic (mesoscale) features present in infrared (IR) images obtained from the Advanced Very High Resolution Radiometer (AVHRR). Conventional edge detectors are very sensitive to edge fine structure, which makes it difficult to distinguish the weak gradients that are useful in this application from noise. Mathematical morphology has been used in the past to develop efficient and statistically robust edge detectors. Image analysis techniques use the histogram for operations such as thresholding and edge extraction in a local neighborhood in the image. An efficient computational framework is discussed for extraction of mesoscale features present in IR images. The technique presented here, the Histogram-Based Morphological Edge detector (HMED), extracts all the weak gradients, yet retains theedgesharpnessin theimage. Wealsopresent newmorphological operations defined in the domain of the histogram of an image. We provide interesting experimental results from applying the HMED technique to oceanographic data in which certain features are known to have edge gradients of varying strength.