Abhishek Mishra and Bharti Chaurasia
Scope college of Engineering, Bhopal, India
(1) e-mail: firstname.lastname@example.org
(2) e-mail: email@example.com@gmail.com
This paper compared the work done on the contour model based shadow detection algorithm with the random field model based shadow detection method. the contour model based shadow detection algorithm tailoring the traditional model of the geometric active contours such that the new model of the contours is systematically biased toward segmenting the shadow and the dark regions in the image. After detecting and segmenting the shadow and the dark regions in the image the post processing is calculation of optimal threshold and a boundary complexity metric to distinguish the true shadows. Whereas the random field model based shadow detection method uses a surface descriptor, termed colour-shade, to capture gradual colour surface variations. The colour-shade descriptor incorporated into the condition random field model to find same illumination pairs and to obtain coherent shadow regions. The shadow removal method with local colour constancy computation uses less anisotropic diffusion to estimate the image pixel illuminant locally in shadow region.
Abhishek Mishra and Bharti Chaurasia, “Single Image Shadow Detection and Removal in Aerial Images: A Review,” International Journal of Advanced Engineering and Management, Vol. 2, No. 2, press, 2017.
- I. Elbakary and K. M. Iftekharuddin, “Shadow detection of man-made buildings in high-resolution panchromatic satellite images,” IEEE Trans. on Geoscience and Remote Sensing, vol. 52, no. 9, Sept. 2014, pp.5374-5386.
- Xingsheng Yuan1, Marc Ebner2, Zhengzhi Wang, x “Single-image shadow detection and removal using local colour constancy computation,” IET Image Process., 2015, Vol. 9, Iss. 2, pp. 118–126, doi: 10.1049/iet-ipr.2014.0242.
- Zhu, K.G.G. Samuel, S. Masood, and M.F. Tappen, “Learning to Recognize Shadows in Monochromatic Natural Images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
- F. Lalonde, A.A. Efros, and S.G. Narasimhan, “Estimating Natural Illumination from a Single Outdoor Image,” Proc. 12th IEEE Int’l Conf. Computer Vision, 2009.
- Karsch, V. Hedau, D. Forsyth, and D. Hoiem, “Rendering Synthetic Objects Into Legacy Photographs,” Proc. ACM Siggraph, 2011.
- L. Waltz, “Generating Semantic Descriptions from Drawings of Scenes with Shadows,” technical report, 1972.
- Barrow and J. Tenenbaum, “Recovering Intrinsic Scene Characteristics from Images,” Computer Vision Systems, pp. 3-26, 1978.
- Sharma, Y. Kurmi, and V. Chaurasia, “Formation of super- resolution image: a review,” Int. Jour. of Emerging Tech. and Adv. Engg., Apr. 2014, vol. 4, no. 4, pp. 218-221.
- Kurmi and V. Chaurasia, “An image fusion approach based on adaptive fuzzy logic model with local level processing,” Int. Jour. of Comp. Appl., Aug. 2015, vol. 124, no.1, pp. 39-42.
- Tiwari, K. Chauhan, and Y. Kurmi “Shadow detection and compensation in aerial images using MATLAB,” Int. Jour. of Comp. Appl., June 2015, vol. 119, no.20, pp. 5-9.
- Kurmi and V. Chaurasia, “Performance of haze removal filter for hazy and noisy images,” Int. Jour. of Sci. Engg. and Tech., Apr. 2014, vol. 3 no. 4, pp. 437-439.
- K. Patle, B. Chourasia, and Y. Kurmi, “High Dynamic Range Image Analysis through Various Tone Mapping Techniques,” Int. Jour. of Comp. Appl., vol.153, no. 11, Nov. 2016 pp. 14-17.
- Kumar, B. Chourasia, and Y. Kurmi, “Image defogging by multiscale depth fusion and hybrid scattering model,” International Journal of Computer Applications (0975 – 8887), vol. 155, no 11, Dec. 2016, pp. 34-38.