Commenced in January 2007
Paper Count: 31515
Object Detection in Digital Images under Non-Standardized Conditions Using Illumination and Shadow Filtering
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1314628Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 702
 Dawson-Howe, K. (2014), “A Practical Introduction to Computer Vision with OpenCV,” Hoboken, NJ: Wiley. 235.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 Intel Open Source Computer Vision Library, (OPENCV) “http://sourceforge.net/projects/opencvlibrary/,
 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.
 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.
 R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain (2002), “Face Detection in Color Images, ” IEEE Trans. PAMI, 24(5), pp.696-706.
 P. Peer, J. Kovac, F. Solina (2003), “Human Skin Colour Clustering for Face Detection, ” EUROCON1993, Ljubljana, Slovenia, pp.144-148.
 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.
 Simonoff, Jeffrey S. (1998), “Smoothing Methods in Statistics. ” 2nd edition. Springer ISBN 978-0387947167 .
 Yiu-ming Cheung, Xin Liu, Xinge You (2012), “A local region based approach to lip tracking ” Pattern Recognition , vol.45 (12), pp.3336-3347.
 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.
 S. Wang, W. Lau, S. Leung (2004), “Automatic lip contour extraction from colour images ” Pattern Recognition, vol.37(12), pp.2375-2387.
 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.