Two new algorithms for fast computation of Legendre moments
Lei Qin, Huazhong Shu, Fenghua Jin, Christine Toumoulin, Limin Luo
Abstract
Lei Qin, Huazhong Shu, Fenghua Jin, Christine Toumoulin, Limin Luo
Abstract
Orthogonal moments have been successfully used in the field of pattern recognition and image analysis. However, due to the complexity in their calculation, the problem of fast computation of orthogonal moments has not till now been well solved. This paper presents two fast and efficient algorithms for the two dimensional (2D) Legendre moment computation. They are based on a block representation of the image and respectively use cumulative and integral methods. Results on 2D binary images show that these algorithms can decrease the computational complexity in a very important way.
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Orthogonal moments have been successfully used in the field of pattern recognition and image analysis. However, due to the complexity in their calculation, the problem of fast computation of orthogonal moments has not till now been well solved. This paper presents two fast and efficient algorithms for the two dimensional (2D) Legendre moment computation. They are based on a block representation of the image and respectively use cumulative and integral methods. Results on 2D binary images show that these algorithms can decrease the computational complexity in a very important way.
Key concepts: Legendre polynomials, Computation, Algorithm, Computational complexity theory, Block (permutation group theory), Moment (physics), Method of moments (probability theory), Representation (politics)