Object Detection Using Robust Image Features

  • Khande Bharath Kumar
  • D. Venkataraman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


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.


Feature extraction Color model SIFT Object detection 


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    M. Varma, A. Zisserman, A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2032–2047 (2009)CrossRefGoogle Scholar
  3. 3.
    H. Wang, J. Oliensis, Rigid shape matching by segmentation averaging. IEEE Trans. Pattern Anal. Mach. Intell. 32, 619–635 (2010)CrossRefGoogle Scholar
  4. 4.
    D. Lowe, Distinctive image features from scale-invariant key points. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    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. 196–201Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    K.K. Thyagharajan, R.I. Minu, prevalent color extraction and indexing. Int. J. Eng. Technol. 5(6), (2013–2014)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–714 (1986)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    B. Leibe, A. Leonardis, B. Schiele, Robust object detection with interleaved categorization and segmentation. Int. J. Comput. Vision 77, 259–289 (2008)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    A. Mansur, Y. Kuno, Integration of multiple methods for Robust object recognition, in SICE Annual Conference, pp. 1990–1995 (2007)Google Scholar
  19. 19.
    D. Lowe, Distinctive image features from scale invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  20. 20.
    T. Serre, L. Wolf, T. Poggio, A new biologically motivated framework for robust object recognition. Ai memo 2004–026 (2004)Google Scholar
  21. 21.
    C. Harris, M. Stephens, A combined corner and edge detector, in Presented at the Alvey Vision Conference (1988)Google Scholar
  22. 22.
    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)CrossRefGoogle Scholar
  23. 23.
    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)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations