Fast and Stable Representation for Both Gray and Color 1-D or 2-D Objects Using Different Sets of Discrete Orthogonal Moments | ||||
| Obour Institute Journal for Engineering and Technology | ||||
| Volume 1, Issue 1, 2023, Page 128-139 PDF (951.17 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/oijet.2023.446886 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Gaber Hassan  ; Mohamed Farag
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| Department of Computer Science, Obour High Institute for Management & Informatics, Obour, Egypt | ||||
| Abstract | ||||
| Representation, analysis and interpretation of an image acquired by a real (i.e. non ideal) imaging system is the key problem in many application areas such as robot vision, remote sensing, astronomy and medicine, to name but a few. Images may be gray or color. One of the most commonly used to represent gray images in the last period is the moments. Moments are considered as statistical quantities that describe the pixels distribution inside an image's space. Image reconstruction method is the best way to check the capability of the moment to represent the image efficiently. In this paper we introduced efficient methods to reconstruct gray scale images based on various sets of discrete orthogonal moments: generalized laguerre moments (GLMs), Chebychev moments (CMs) and Krawtchouk moments (KMs). Assisted by quaternion algebra, representation of color images become smoothly, hence, we extended both GLMs and CMs by using quaternion algebra and derived various sets of quaternion moments: quaternion generalized laguerre moments (Q_GLMs) and quaternion Chebychev moments (Q_CMs). The experimental results show the capacity of the proposed approaches for image reconstruction against different the noise attack. We used the normalized image reconstruction error (NIRE) as a measure to the image reconstruction capability. | ||||
| Keywords | ||||
| Discrete orthogonal moments; quaternion moments; image reconstruction | ||||
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