Image Interpolation and Object Recognition Approach based on SAI Algorithm with Normalized Cross Correlation

Authors

  • Aruna Kumari Palisetty Department of CSE, University College of Engineering, JNTUK, Vizianagaram, India
  • R V Naga Suneetha Avvaru Department of CSE, University College of Engineering, JNTUK, Vizianagaram, India

DOI:

https://doi.org/10.37591/josettt.v1i1.86

Abstract

This paper mainly deals with producing quality of an image using soft-decision interpolation technique and also identifying the particular object in that image with the help of Normalized Cross Correlation. the use of image interpolation, to reserve spatial details. Identifying the missing pixels in an image one by one for many times, we are proposing the new technique, identifying missing pixels in a group. This new technique can be applied to different screen structures by the use of 2-D piecewise autoregressive model. The result of this approach is similar to the 2-D interpolation filter. This approach is better than the existing approaches and it produces best results with visual quality. Some images may contain blurring, ridges, ringing are fully reduced in these
images. Now we have quality image, on this image we are recognizing a particular object in that image. To identify the object Normalized Cross Correlation is used. The aim of
object recognition is to identify object and estimate their location and orientation.

Keywords: Autoregressive process, Image Interpolation, Soft-Decision Estimation, Object Recognition, Normalized Cross Correlation.

References

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Published

2020-02-29

Issue

Vol. 1 No. 1 (2014)

Section

Research Articles

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