The Detection and Segmentation of Blobs in Infrared Images

Abstract

A computer procedure for detecting and finding the boundaries of blobs in noisy infrared images is described. Our evaluation of this procedure on a data base of 81 targets, 34 for design and 47 for test, resulted in only two false negatives (missed targets) and no false detections. Our procedure consists of an intensity normalizer, a dc notch filter, an edge detector, a spoke filter, a gradient-guided segmenter, an extractor of the standard deviation of the gray level in each blob, an extractor of the fraction of intense edge elements along the boundary of each blob, and a three-nearest-neighbor classifier. Among these processes, the spoke filter and the gradient-guided segmenter are new. Both of them contribute strongly to the effectiveness of our procedure. The spoke filter is sensitive to a wide variety of shapes of blobs within a specified range of sizes. The gradient-guided segmenter exploits the noise immunity of the direction of the digital gradient to find a best threshold for segmenting each detected blob.