Abstract :
This short survey of moment invariants points out interesting clues. Moments offer a sound theoretical framework for solving the generic problems encountered in many imaging applications. The diverse families of orthogonal moments provide the flexibility that may be required to face a particular target. However, they have to satisfy the time computation constraints that are inherent in many applications. Suk and Flusser proposed a solution of simultaneously dealing with affine transformation and blur (with centrosymmetric PSF) for pattern recognition, template matching, and image registration. Flusser and Zivota suggested a set of combined moments that are invariant to both rotation and blurring. Based on complex moments, Liu and Zhang derived a subset of moment features that are not affected by image blurring and geometric transformation such as translation, scale, and rotation. All these works, however, point out the problems related to the number of invariants to be selected, the choice of the size of the region of interest where moments are computed, and the dependence with object features (i.e., symmetry).
Keywords :
computer vision; image matching; image registration; image restoration; medical image processing; computer vision; generic problems; geometric transformation; image blurring; image registration; medical imaging; moment invariants; moment-based approach; object features; orthogonal moments; pattern recognition; template matching; time computation constraints; Algebra; Application software; Biomedical imaging; Computer vision; Feature extraction; Geometry; Medical treatment; Noise robustness; Polynomials; Tomography; Algorithms; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Reproducibility of Results; Sensitivity and Specificity;
