Abstract
Object detection is a challenging field of research in computer vision. Research approaches have become increasingly popular in overcoming the challenges of object detection like occlusions, changes in scale, rotation, and illumination. Object detection methods that utilize RGB cameras are used to accurately identify objects in the real world, but they do not consider shape and three-dimensional characteristics of the object. Recognizing the objects in 3D is not an easy task for computers, like as in humans. Robust features like shape, color, size, etc., are necessary for 3D object detection for ensuring accuracy.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
J. Shotton, J. Winn, C. Rother, A. Criminisi, Texton Boost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comput. Vision 81, 2–23 (2009)
M. Varma, A. Zisserman, A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2032–2047 (2009)
H. Wang, J. Oliensis, Rigid shape matching by segmentation averaging. IEEE Trans. Pattern Anal. Mach. Intell. 32, 619–635 (2010)
D. Lowe, Distinctive image features from scale-invariant key points. IJCV 60(2), 91–110 (2004)
L.-C. Chen, X.-L. Nguyen, S.-T. Lin, Automated object detection employing viewing angle histogram for range images, in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2012), pp. 196–201
B. Ommer, J. Buhmann, Learning the compositional nature of visual object categories for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 501–516 (2010)
P. Carbonetto, G. Dorko’, C. Schmid, H. Kuck, N. De Freitas, Learning to recognize objects with little supervision. Int. J. Comput. Vision 77, 219–237 (2008)
Z. Si, H. Gong, Y.N. Wu, S.C. Zhu, Learning mixed templates for object recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 272–279 (2009)
K.K. Thyagharajan, R.I. Minu, prevalent color extraction and indexing. Int. J. Eng. Technol. 5(6), (2013–2014)
J. Dou, J. Li, J. Li, Robust object detection based on deformable part model and improved scale invariant feature transform. Int. J. Light Electron. Opt. 124, 6485–6492 (2013)
C. Richao, Y. Gaobo, Z. Ningbo, Detection of object-based manipulation by the statistical features of object contour. Forensic Sci. Int. 236, 164–169 (2014)
J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–714 (1986)
C. Ma et al., An improved Sobel algorithm based on median filter, in Institute of Electrical and Electronics Engineers, 2nd International IEEE Conference 1, pp. 88–93 (2010)
A. Seif et al., A hardware architecture of Prewitt edge detection, in Sustainable Utilization and Development in Engineering and Technology, 2010 IEEE Conference, pp. 99–101 (2010)
A.-L. Quintanilla, J.-L. Lopez-Ramirez, M.A. Ibarra-Manzano, Detecting objects using color and depth segmentation with kinectsensor, in Iberoamerican Conference on Electronics Engineering and Computer Science, pp. 196–204 (2012)
B. Leibe, A. Leonardis, B. Schiele, Robust object detection with interleaved categorization and segmentation. Int. J. Comput. Vision 77, 259–289 (2008)
S. Tangruamsub, K. Takada, O. Hasegawa, 3D object recognition using voting algorithm in a real-world environment, in 2011 IEEE Conference on Applications of Computer Vision, pp. 153–158 (2011)
A. Mansur, Y. Kuno, Integration of multiple methods for Robust object recognition, in SICE Annual Conference, pp. 1990–1995 (2007)
D. Lowe, Distinctive image features from scale invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
T. Serre, L. Wolf, T. Poggio, A new biologically motivated framework for robust object recognition. Ai memo 2004–026 (2004)
C. Harris, M. Stephens, A combined corner and edge detector, in Presented at the Alvey Vision Conference (1988)
A. Opelt, A. Pinz, A. Zisserman, Learning an alphabet of shape and appearance for multi-class object detection. Int. J. Comput. Vision 80, 16–44 (2008)
Z. Si, H. Gong, Y.N. Wu, S.C. Zhu, Learning mixed templates for object recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 272–279 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Kumar, K.B., Venkataraman, D. (2015). Object Detection Using Robust Image Features. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 324. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2126-5_32
Download citation
DOI: https://doi.org/10.1007/978-81-322-2126-5_32
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2125-8
Online ISBN: 978-81-322-2126-5
eBook Packages: EngineeringEngineering (R0)