This paper focuses in explaining a Fourier based affine estimator which is applied to the task of Image Synthesis. An affine transformation is an important class of linear 2-D geometric transformations which maps variables into new by applying a linear combination of translation, rotation, scaling and/or shearing operations. | ISSN:2249-5789 S Vandana et al , International Journal of Computer Science & Communication Networks,Vol 2(3), 407-415 Synthesis and Image Matching On Structural Patterns Using Affine Transformation in EIE Department, Sri Devi Women’s Engineering College, Hyderabad, Andhra Pradesh, India; (E-mail: vandana_slv@). & Head of EIE Department, Sri Devi Women’s Engineering College, Hyderabad, Andhra Pradesh, India; (E-mail: ramukorrapati@). Principal, Sri Devi Women’s Engineering College, Hyderabad, Andhra Pradesh, India; (E-mail: yrrao315@) Sheela in ECE Department, JNTU College of Engineering, Hyderabad, Andhra Pradesh, India; (E-mail: kanithasheela@). Abstract This paper focuses in explaining a Fourier based affine estimator which is applied to the task of Image Synthesis. An affine transformation is an important class of linear 2-D geometric transformations which maps variables into new by applying a linear combination of translation, rotation, scaling and/or shearing operations. Conventional retrieval systems are very effective when knowledge information and query information are in a uniform orientation but fails in recognition when effects such as scaling, orientation exist. But as this technique is based on texture analysis, which is termed the affine estimator, it will even match the images with non-uniform orientation. Keywords: Affine Estimator, Image Synthesis. . Image Matching is an important problem in computer vision and pattern analysis. In this paper, recognition of objects from their boundaries that are subject to affine transformations is considered. The affine transformation includes rotation, scaling, skewing, and translation. It preserves parallel lines and equispaced points along a line. In some cases, the affine transformation can also be used to approximate the perspective transformation. The existing .
