Survey on Different Applications of Image Processing
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Abstract
In Imaging Science, image processing is the processing of images using mathematical operations by employing any form of signal processing, with the input being an image, a series of images, or a video, such as a photograph or video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. These image processing techniques can be used to perform applications in the real world. This image processing technique helps to improve various aspects related to the real world. Some of these applications are in the field of health science, security assurance, and augmented reality. Additionally, this technique can be applied in real-time applications. All these applications are performed by using image processing as its basic platform.
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References
Dalton, J. (1798). Extraordinary Facts Relating to the Vision of Colour. London: Cadel and Davins.
Machado, G. M. (2009). A Physiologically-based Model for Simulation of Color Vision Deficiency. IEEE Transactions on Visual and Computer Graphics, 15(6), 1291-1298.
Machado, G. M., & Oliveira, M. M. (2010). A Model for Simulation of Color Vision Deficiency and A Color Contrast Enhancement Technique for Dichromats, 74.
Kanhangad, V., Kumar, A., & Zhang, D. (2011). Contactless and pose invariant biometric identification using hand surface. IEEE Transactions on Image Processing, 6(3), 1415-1424.
The Hong Kong Polytechnic University. (2015). Implementation Codes for 3D Palmprint Matching.
Li, W. L., Zhang, L., & Zhang, D. (2009). Three-dimensional palmprint recognition. In Proc. IEEE Int. Conf. Syst., Main Cybern., Oct 2009, pp. 4847-4852.
Rajavel, P. (2010). Image Dependent Brightness Preserving Histogram Equalization. IEEE Transactions on Consumer Electronics, 56(2).
Kim, Y.-T. (1997). Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics, 43(1), 1-8.
Chen, S.-D., & Ramli, A. R. (2003). Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Transactions on Consumer Electronics, 49(4), 1301-1309.
Azuma, R., Baillot, Y., & Behringer, R. (2001). Recent Advances in Augmented Reality. Computers & Graphics, November 2001.
Avery, B., Thomas, B., & Piekarski, W. (2008). User Evaluation of See-Through Vision for Mobile Outdoor Augmented Reality. In 7th Int'l Symposium on Mixed and Augmented Reality, 69-72. Cambridge, UK.
Zhou, F., Duh, H. B.-L., & Billinghurst, M. (2008). Trends in augmented reality tracking, interaction, and display: A review of ten years of ISMAR. Mixed and Augmented Reality 2008. ISMAR 2008. 7th IEEE/ACM International.