Abstract
Automatic target recognition (ATR) involves processing images for detecting, classifying, and tracking targets embedded in a background scene. This paper presents an algorithm for detecting a specified set of target objects embedded in visual images for an ATR application. The developed algorithm employs a novel technique for automatically detecting man-made and non-man-made single, two, and multitargets from nontarget objects, located within a cluttered environment by evaluating nonoverlapping image blocks, where block-by-block comparison of wavelet cooccurrence feature is done. The results of the proposed algorithm are found to be satisfactory.
Highlights
The last three decades have seen rapid development in electronic automation, though mechanical automation was there for the past 200 years
The steps required for successful implementation of an automatic target recognition (ATR) task involves automatic detection, classification, and tracking of a target located in an image scene
The discrete wavelet transform (DWT) has properties that make it an ideal transform for the processing of images encountered in target recognition applications, including rapid processing, a natural ability to adapt to changing local image statistics, efficient representation of abrupt changes and precise position information, ability to adapt to high background noise and uncertainty about target properties, and a relative independence to target-to-sensor distance
Summary
The last three decades have seen rapid development in electronic automation, though mechanical automation was there for the past 200 years. The wavelet transform is a multiresolution technique, which can be implemented as a pyramid or tree structure and is similar to subband decomposition. Target detection is achieved by calculating cooccurrence matrix features from detail subbands of discrete wavelet transformed, nonoverlapping but adjacent subblocks of different sizes, depending upon the target image. From these calculations, the subblock with the maximum of combined wavelet cooccurrence feature values (WCFs) is identified as a seed window.
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