Lapped Block Image Analysis via the Method of Legendre Moments

Abstract

Research investigating the use of Legendre moments for pattern recognition has been performed in recent years. This field of research remains quite open. This paper proposes a new technique based on block-based reconstruction method (BBRM) using Legendre moments compared with the global reconstruction method (GRM). For alleviating the blocking artifact involved in the processing, we propose a new approach using lapped block-based reconstruction method (LBBRM). For the problem of selecting the optimal number of moment used to represent a given image, we propose the maximum entropy principle (MEP) method. The main motivation of the proposed approaches is to allow fast and efficient reconstruction algorithm, with improvement of the reconstructed images quality. A binary handwritten musical character and multi-gray-level Lena image are used to demonstrate the performance of our algorithm

Comparative Study of Exact Continuous Orthogonal Moments Applications : Local Feature Extraction and Data Compression

“…Our main objective is to reach high reconstruction quality using only a small finite number of moments, by splitting the original image into blocks, and using fast and exact approximation. This strategy relies in the utilization of loworder polynomials on small intervals instead of high orders on a single one [13]. This aims to introduce these continues moments in the data compression field, which can be achieved by transforming the data, projecting them on the basis of functions, and then encoding the resulted coefficients [1617].…”

“…The approach by first calculating the geometric moments accurately, then employing the relationship in (13)of Zernike moments and the geometric moments. …”

“…Nevertheless, if we consider images with greater sizes, higher order moments are involved and the handling of greater quantity of information is necessary [13]. Hence the moments computation becomes a time consuming procedure.…”

Blocking artifact removal using partial overlapping based on exact Legendre moments computation

“…The first one is the need of computing a set of complicated quantities for each moment order [3]. The second is the necessity to manipulate greater quantity of information due to a reconstruction of large image space [2,17]. In this case, high moment orders must be used to reconstruct the fine details of the image [17].…”

“…The second is the necessity to manipulate greater quantity of information due to a reconstruction of large image space [2,17]. In this case, high moment orders must be used to reconstruct the fine details of the image [17]. Moreover, those high-order moments are more sensitive to noise than lower-order ones [1,18].…”