Home » Modified Hue over Intensity Ratio Based Method for Shadow Detection and Removal in Arial Images

Modified Hue over Intensity Ratio Based Method for Shadow Detection and Removal in Arial Images

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Abhishek Mishra

setu17687@gmail.com
Scope college of Engineering, Bhopal, India

Bharti Chaurasia

bharti.chourasia27@gmail.com
Scope college of Engineering, Bhopal, India

Abstract

In this paper, we represent an outline to automatically identify and remove shadows from the real single image. The most significant features can be manipulated by multiple convolution deep neural networks (ConvNets) process. The required features extraction methods are imposed at the dominant boundaries in each super-pixel level of the image. The shadow masks may be predicted random model to generate a smooth image based on Bayesian formulation. These works represent a chromaticity based process for removal of desire shadows in aerial images. Hue is considered as the base variables for shadow detection. We represent modified hue over intensity ratio method for the better result.

Keywords

Cast Shadowing;
Hue over Intensity Ratio;
Hue, saturation, and value;
Min Depth parameter;
Self-Shadowing.

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Cited as

Abhishek Mishra and Bharti Chaurasia, “Modified Hue over Intensity Ratio Based Method for Shadow Detection and Removal in Arial Images,” International Journal of Advanced Engineering and Management, Vol. 2, No. 5, pp. 101-105,  2017.                                      

 References

  1. Nadimi, S., & Bhanu, B. (2004). Physical models for moving shadow and object detection in video. IEEE transactions on pattern analysis and machine intelligence, 26(8), 1079-1087.

  2. Prati, A., Mikic, I., Trivedi, M. M., & Cucchiara, R. (2003). Detecting moving shadows: algorithms and evaluation. IEEE transactions on pattern analysis and machine intelligence, 25(7), 918-923.
  3. Katre, R., & Dodkey, N. (2017.) Rain Streaks Removal in Image via Decomposition and Visibility Feature Saturation. International Journal, 1(1), 82-85.
  4. Lee, J. S. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE transactions on pattern analysis and machine intelligence, (2), 165-168.
  5. Mishra, A., Chaurasia, B., & Kurmi, Y. (2017). Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images. International Journal of Advanced Engineering and Management, 2(4), 86-89.
  6. Hong, L., Wan, Y., & Jain, A. (1998). Fingerprint image enhancement: Algorithm and performance evaluation. IEEE transactions on pattern analysis and machine intelligence, 20(8), 777-789.
  7. Awad, M., Chehdi, K., & Nasri, A. (2007). Multicomponent image segmentation using a genetic algorithm and artificial neural network. IEEE Geoscience and remote sensing letters, 4(4), 571-575.
  8. Mostafa, Y., & Abdelhafiz, A. (2017). Shadow Identification in High Resolution Satellite Images in the Presence of Water Regions. Photogrammetric Engineering & Remote Sensing, 83(2), 87-94.
  9. Moon, Hankyu, Rama Chellappa, and Azriel Rosenfeld. “Performance analysis of a simple vehicle detection algorithm.” Image and Vision Computing 20.1 (2002): 1-13.
  10. Xu, H., Yang, Z., Chen, G., Liao, G., & Tian, M. (2016). A Ground Moving Target Detection Approach Based on Shadow Feature With Multichannel High-Resolution Synthetic Aperture Radar. IEEE Geoscience and Remote Sensing Letters, 13(10), 1572-1576.
  11. Ghanbari, M., Majdi, M., & Talouki, M. (2017). Video Inpainting Using a Contour-based Method in Presence of More than One Moving Objects. International Journal of Advanced Engineering and Management, 2(2), 37-44.
  12. Sharma, D., Kurmi, Y., & Chaurasia, V. (2014). Formation of Super-Resolution Image: A Review. Int. Jour. of Emerging Tech. and Adv. Engg, 4(4), 218-221.
  13. Kurmi, Y., & Chaurasia, V. (2015). An Image Fusion Approach based on Adaptive Fuzzy Logic Model with Local Level Processing. International Journal of Computer Applications, 124(1), 39-42
  14. Tiwari, S., Chauhan, K., & Kurmi, Y. (2015). Shadow detection and compensation in aerial images using MATLAB. International Journal of Computer Applications, 119(20),1-9.
  15. Kurmi, Y., & Chaurasia, V. (2014). Performance of Haze Removal Filter for Hazy and Noisy Images. Int. Jour. of Sci. Engg. and Tech, 3(4), 437-439.
  16. Patle, M. K., Chourasia, B. & Kurmi. Y. (2016). “High Dynamic Range Image Analysis Through Various Tone Mapping Techniques. Int. Jour. of Comp. Appl., vol.153( 11),14-17.
  17. Kumar, A., Chourasia, B., & Kurmi, Y.  (2017). Image Defogging by Multiscale Depth Fusion and Hybrid Scattering Model, International Journal of Computer Applications, 155(11).
  18. Chen, Y., Wang, J., Xu, M., He, X., & Lu, H. (2016). A unified model sharing framework for moving object detection. Signal Processing124, 72-80.
  19. Chen, Y., Wang, J., Xu, M., He, X., & Lu, H. (2016). A unified model sharing framework for moving object detection.Signal Processing124, 72-80.
  20. Mishra, P. K., & Saroha, G. P. (2016). A study on classification for static and moving object in video surveillance system. International Journal of Image, Graphics and Signal Processing (IJIGSP)8(5), 76-82.

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