Fusing stereo images into its equivalent cyclopean view
- Fusing stereo images,
- Principal Component Analysis,
- Discrete Wavelet Transform
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Image fusion is a technique of intertwining at least two pictures of same scene to shape single melded picture which shows indispensable data in the melded picture. Picture combination system is utilized for expelling clamor from the pictures. Commotion is an undesirable material which crumbles the nature of a picture influencing the lucidity of a picture. Clamor can be of different kinds, for example, Gaussian commotion, motivation clamor, uniform commotion and so forth. Pictures degenerate some of the time amid securing or transmission or because of blame memory areas in the equipment. Picture combination should be possible at three dimensions, for example, pixel level combination, highlight level combination and choice dimension combination. There are essentially two kinds of picture combination methods which are spatial area combination systems and transient space combination procedures. (PCA) combination, Normal strategy, high pass sifting are spatial area techniques and strategies which incorporate change, for example, Discrete Cosine Transform, Discrete wavelet change are transient space combination strategies. There are different techniques for picture combination which have numerous favorable circumstances and detriments. Numerous procedures experience the ill effects of the issue of shading curios that comes in the intertwined picture shaped. Also, the Cyclopean One of the most astonishing properties of human stereo vision is the combination of the left and right perspectives of a scene into a solitary cyclopean one. Under typical survey conditions, the world shows up as observed from a virtual eye set halfway between the left and right eye positions. The apparent picture of the world is never recorded specifically by any tangible exhibit, however developed by our neural equipment. The term cyclopean alludes to a type of visual upgrades that is characterized by binocular dissimilarity alone. He suspected that stereo-psis may find concealed articles, this may be helpful to discover disguised items. The critical part of this examination when utilizing arbitrary dab stereo-grams was that uniqueness is adequate for stereo-psis, and where had just demonstrated that binocular difference was vital for stereo-psis.
 Sahu, Deepak Kumar, and M. P. Parsai. "Different image fusion techniques–a critical review." International Journal of Modern Engineering Research (IJMER) 2, no. 5 (2012): 4298-4301.
 Guðmundsson, Sigurjón Árni, Henrik Aanaes, and Rasmus Larsen. "Fusion of stereo vision and time-of-flight imaging for improved 3d estimation." International Journal on Intelligent Systems Technologies and Applications (IJISTA) 5, no. 3/4 (2008): 425-433.
 Kolmogorov, V., Criminisi, A., Blake, A., Cross, G., & Rother, C. (2006). Probabilistic fusion of stereo with color and contrast for bilayer segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9), 1480-1492.
 B. Jules, Foundations of Cyclopean Perception. Univ. Chicago Press, 1971.
 A. Maalouf and M.-C. Larabi, “CYCLOP: A stereo color image quality assessment metric,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP 2011), Prague, Czech Republic, May 2011, pp. 1161-1164.
 M.-J. Chen, C.-C. Su, D.-L. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereo pairs accounting for rivalry,” Signal Processing: Image Communication, Vol. 28, no. 9, pp. 1143- 1155, Oct. 2013.
 Harper, Bernard, and Richard Latto. "Cyclopean vision, size estimation, and presence in orthostereoscopic images." Presence: Teleoperators & virtual environments 10, no. 3 (2001): 312-330.
 Lu, Kaixuan, and Wei Zhu. "Stereoscopic Image Quality Assessment Based on Cyclopean Image." In Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/Datacom/CyberSciTech), 2016 IEEE 14th Intl C, pp. 420-423. IEEE, 2016.
 Mingjing Li and Yubing Dong. “Review on technology of pixel-level image fusion.” In International Conference on Measurement, Information and Control (ICMIC), vol. 1, IEEE, 2013.
 K Sharmila, S Rajkumar, V Vijayarajan. “Hybrid method for Multimodality Medical image fusion using Discrete Wavelet Transform and Entropy concepts with Quantitative Analysis.” In International conference on Communication and Signal Processing (ICCSP), IEEE, April 3-5, 2013.
 Huaxun Zhang and Xu Cao. “A way of image fusion based on wavelet transform” In 9th International Conference on Mobile Ad-hoc and Sensor Networks, IEEE, 2013.
 Jasmeet kaur and Er. Rajdavinder Singh Boparai. “An evaluation on different image fusion techniques” IPASJ International Journal of Computer Science (IIJCS) Volume 2, Issue 4, April 2014.
 Y. Asnath Victy Phamila and R. Amutha. “Discrete Cosine Transform based fusion of multi-focus images for visual sensor networks.” Signal Processing 95 (2014).
 Gazal Malhotra and Dr. Vinay Chopra. “Improved multi-focus image fusion using ac-dct, edge preserving smoothing & DRSHE” In Proceedings of International Conference on Computer Science, Cloud Computing and Applications July 24-25, 2014.