Object Detection in Digital Images under Non-Standardized Conditions Using Illumination and Shadow Filtering

In recent years, object detection has gained much
attention and very encouraging research area in the field of computer
vision. The robust object boundaries detection in an image is
demanded in numerous applications of human computer interaction
and automated surveillance systems. Many methods and approaches
have been developed for automatic object detection in various fields,
such as automotive, quality control management and environmental
services. Inappropriately, to the best of our knowledge, object
detection under illumination with shadow consideration has not
been well solved yet. Furthermore, this problem is also one of
the major hurdles to keeping an object detection method from the
practical applications. This paper presents an approach to automatic
object detection in images under non-standardized environmental
conditions. A key challenge is how to detect the object, particularly
under uneven illumination conditions. Image capturing conditions
the algorithms need to consider a variety of possible environmental
factors as the colour information, lightening and shadows varies
from image to image. Existing methods mostly failed to produce the
appropriate result due to variation in colour information, lightening
effects, threshold specifications, histogram dependencies and colour
ranges. To overcome these limitations we propose an object detection
algorithm, with pre-processing methods, to reduce the interference
caused by shadow and illumination effects without fixed parameters.
We use the Y CrCb colour model without any specific colour
ranges and predefined threshold values. The segmented object regions
are further classified using morphological operations (Erosion and
Dilation) and contours. Proposed approach applied on a large image
data set acquired under various environmental conditions for wood
stack detection. Experiments show the promising result of the
proposed approach in comparison with existing methods.




References:
[1] Dawson-Howe, K. (2014), “A Practical Introduction to Computer Vision
with OpenCV,” Hoboken, NJ: Wiley. 235.
[2] W. Y. D. K. H. Yoon (2007), “Fast Group Verification System for
Intelligent Robot Service,” IEEE Transactions on Consumer Electronics,
vol. 53, pp.1731-1735.
[3] W. U. R. Butt, L. Lombardi (2013), “Comparisons of Visual
Features Extraction Towards Automatic Lip Reading,” 5th International
Conference on Education and New Learning Technologies, Barcelona,
Spain, vol. 5, pp.2188-2196.
[4] Luca Lombardi, Waqqas ur Rehman Butt, Marco Grecuccio (2014),
“Lip Tracking Towards an Automatic Lip Reading Approach,” Journal
of Multimedia Processing and Technologies, vol. 5, pp.1-11. ISSN:
0976-4127. [5] W. U. R. Butt, L. Lombardi (2013), “A Survey of Automatic Lip Reading
Approaches,” 8th ICDIM 2013 (The 8th International Conference on
Digital Information Management) in Islamabad, Pakistan, pp.299-302.
[6] Nusirwan A. Rahman, Kit C. Wei, John See. (2006), “RGB−H− CbCr
Skin Colour Model for Human Face Detection,” In Proceedings of The
MMU International Symposium on Information and Communications
Technologies.
[7] V. Vezhnevets, V. Sazonov, and A. Andreeva. (2003), “A Surveyon
Pixel-based Skin Color Detection Techniques,” 8th ICDIM 2013 (In
Proceedings of the GraphiCon, Moscow, Russia, pp.85-92.
[8] Moscariello, Antonio, et al. (2011), “Coronary CT angiography: image
quality, diagnostic accuracy, and potential for radiation dose reduction
using a novel iterative image reconstruction technique comparison with
traditional filtered back projection,” (European radiology, pp.2130-2138.
[9] Guo, Ruiqi, Qieyun Dai, and Derek Hoiem (2011), “Single-image shadow
detection and removal using paired regions,” (Computer Vision and
Pattern Recognition - CVPR, pp.2033-2040.
[10] Ferguson, P. D., Arslan, T., Erdogan, A. T., Parmley, A. (2008),
“Evaluation of contrast limited adaptive histogram equalization (clahe)
enhancement on a FPGA,” (SoCC), pp.119-122.
[11] J. Majumdar, S. Kumar K. L. (2014), “Modified CLAHE: An adaptive
algorithm for contrast enhancement of aerial, medical and underwater
images, ” International Journal of Computer Engineering and Technology
(IJCET), vol. 5, pp.32-47.
[12] Intel Open Source Computer Vision Library, (OPENCV)
“http://sourceforge.net/projects/opencvlibrary/,
[13] de Dios J. J., Garcia, N.(2004), “Fast face segmentation in component
color space, ” Int. Conf. on Image Processing, (ICIP), vol. 1, pp.191-194.
[14] S. Gundimada, L. Tao, and V. Asari(2004), “Face Detection Technique
based on Intensity and SkinColor Distribution, ” Int. Conf. on Image
Processing, (ICIP), pp.1413-1416.
[15] R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain (2002), “Face Detection
in Color Images, ” IEEE Trans. PAMI, 24(5), pp.696-706.
[16] P. Peer, J. Kovac, F. Solina (2003), “Human Skin Colour Clustering for
Face Detection, ” EUROCON1993, Ljubljana, Slovenia, pp.144-148.
[17] Bassiou, Nikoletta, and Constantine Kotropoulos (2007), “Color image
histogram equalization by absolute discounting back-off. ” Computer
Vision and Image Understanding 107.1 , pp.108-122.
[18] Simonoff, Jeffrey S. (1998), “Smoothing Methods in Statistics. ” 2nd
edition. Springer ISBN 978-0387947167 .
[19] Yiu-ming Cheung, Xin Liu, Xinge You (2012), “A local region
based approach to lip tracking ” Pattern Recognition , vol.45 (12),
pp.3336-3347.
[20] Guo, Ruiqi, Qieyun Dai, and Derek Hoiem. (2011), “Single-image
shadow detection and removal using paired regions ” PComputer Vision
and Pattern Recognition (CVPR), IEEE Conference.
[21] S. Wang, W. Lau, S. Leung (2004), “Automatic lip contour extraction
from colour images ” Pattern Recognition, vol.37(12), pp.2375-2387.
[22] D. Xu, J. Liu, X. Li, Z. Liu, X. Tang (2005), “Insignificant shadow
detection for video segmentation,” IEEE Transactions on Circuits and
Systems for Video Technology, vol.15, pp.1058-1064.