Assessment of Region-Based Moment Invariants for Object Recognition

Abstract

Geometric region-based moments as features for invariant object recognition are studied. Theoretically rotation, translation, and scale invariant Hu, Zernike, and Krawtchouk moments are used as features for region description. Accuracy of such description and efficiency is tested by recognition of letters and digits from extended Slovenian alphabet. Ten testing samples in six different image resolutions are constructed for each character from learning set. Testing set consists of 390 samples per resolution (altogether 2340 samples). Recognition accuracy obtained by using Hu moments is 95.6%, 87.4% with Zernike moments, and with Krawtchouk moments 64.1%. Object recognition by using Krawtchouk moments is the most sensitive to object rotation and scaling, which is confirmed with the description error of 9.28%. All moment invariants can be reliable used for object recognition in images with up to four times lower resolution as in original image