IR and Visible image fusion


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IR and Visible image fusion


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Color fusion based on EM algorithm for IR and visible image :

Color fusion based on EM algorithm for IR and visible image SUBMITTED BY CHETANKUMAR .P. PATIL IV SEM MTech(DEC) 1MJ09LEC02 MVJCE,BANGALORE-67 UNDER THE GUIDANCE OF Mrs R.Arul vadivu , Dept. E & C MVJCE,BANGALORE-67


CONTENTS History Problem definition Objective Introduction fusion based on EM algorithm Advantages Conclusion reference

History :

History The workload of a human operator human observers is often unreliable information can be obtained more accurately less time and at a lower cost Less costly information may be obtained

Problem definition :

Problem definition Inappropriate color mapping . Observers need specific training.

Objective of the project:

Objective of the project The main objective of this project is Image fusion is presented using EM algorithm False coloring of this fused image


Introduction The IR image records thermal radiations The visible image has much more high-frequency information To make the coloration of false-color images appear more natural, Reinhard et al. introduced

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The false colour fusion in YUV space is carried out as: Where Vis and IR stand for the visible image and the IR image, respectively. Y = EM(Vis,IR) U = Vis - IR V = IR - Vis

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Local analysis of window h h Coefficient j


Advantages Color image fusion is becoming a more and more important research field and lots of novel false color fusion approaches have been developed to fuse IR and visible images. Appropriately false-colored night-time imagery is often helpful for human observers

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Ideally, a color rendering method will produce night-vision imagery with a ‘naturalistic.

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EM algorithm matlab coding

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GUI result of EM algorithm fused result


Conclusion color image fusion is presented using EM algorithm color transfer algorithm for IR and visible image. our proposed method have more “naturistic” than that of others our method has very abundant detail information .

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We propose a approach to the fusion of spatially registered images and image sequences. The advantage of the proposed method is the improved image

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REFERENCES [1] Multi-sensor image fusion and its applications. By Rick S Blum and Zheng Liu [2] The EM algorithm and extensions. By GEOFFREYJ.McLACHLAN,THRIYAMBAKAM [3] J. Yang and R. S. Blum, “A statistical signal processing approach to image fusion for concealed weapon detection,” IEEE Intl. Conf. on Image Processing, pp. I-513–I-516, Rochester, NY, September 2002. [4] Colorizing grayscale intensified nightvision images. By Alexander Toet [5] D. L. Hall and J. Llinas, “An introduction to multisensor data fusion,” Proc. IEEE, vol.85, no. 1, pp. 6-23, Jan. 1997.

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