Target recognition method on retina-like laser detection and ranging images

A target recognition method on retina-like laser detection and ranging images is proposed in this study. The method does not require complicated image preprocessing due to speeded-up robust features (SURF) combined with retina-like sampling as feature match descriptors. Subpixel resampling achieves optimization and avoids affecting the accuracy and precision of target recognition. Several experiments are conducted to analyze the validity of SURF directly. The separate exploration of SURF with Cartesian, log-polar (LP), and inverse LP images are discussed. Furthermore, examples are used to demonstrate the capability of the proposed method. Finally, important conclusions are drawn as follows. (I) SURF extraction becomes difficult when it is directly used in LP images as expected. (II) Applying SURF with an inverse LP process is valid. (III) SURF key match points in inverse LP images are less than those in Cartesian images. (IV) The accuracy of the proposed solution agrees well with that of the Cartesian solution when angle and scale variants are used. The present recognition solution may be used in various applications involving space-variant image processing.