Skip to main content

Industry and Object Recognition: Applications, Applied Research and Challenges

  • Chapter
Book cover Toward Category-Level Object Recognition

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4170))

Abstract

Object recognition technology has matured to a point at which exciting applications are becoming possible. Indeed, industry has created a variety of computer vision products and services from the traditional area of machine inspection to more recent applications such as video surveillance, or face recognition. In this chapter, several representatives from industry present their views on the use of computer vision in industry. Current research conducted in industry is summarized and prospects for future applications and developments in industry are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. The NIST humanid evaluation framework (2003), http://www.frvt.org

  2. The TREC video retrieval evaluation (2003), http://www-nlpir.nist.gov/projects/trecvid

  3. The Pascal visual object classes challenge (2005), http://www.pascal-network.org/challenges/VOC

  4. Chan, M., Hoogs, A., Schmiederer, J., Petersen, M.: Detecting rare events in video using semantic primitives with HMM. In: Proc. ICPR, vol. 4, pp. 150–154 (2004)

    Google Scholar 

  5. Chan, M., Hoogs, A., Perera, A., Bhotika, R., Schmiederer, J., Doretto, G.: Joint recognition of complex events and track matching. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  6. Fergus, R., Zisserman, A., Perona, P.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2003) (2003)

    Google Scholar 

  7. Garcia, C., Delakis, M.: Convolutional face finder: A neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1408–1423 (2004)

    Article  Google Scholar 

  8. Gavrila, D.M.: Pedestrian Detection from a Moving Vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  9. Gheissari, N., Sebatian, T.B., Tu, P.H., Rittscher, J., Hartley, R.: A novel approach to person reidentification. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  10. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proc.Conference on Very Large Databases (1999)

    Google Scholar 

  11. Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 433–449 (1999)

    Article  Google Scholar 

  12. Hirano, Y., Kitahama, K., Yoshizawa, S.: Image-based Object Recognition and Dexterous Hand/Arm Motion Planning Using RRTs for Grasping in Cluttered Scene. In: IEEE/RSJ Conference on Intelligent Robots and Systems (IROS 2005), Edmonton, Canada (2005)

    Google Scholar 

  13. Hoiem, D., Sukthankar, R., Schneiderman, H., Huston, L.: Object-based image retrieval using the statistics of images. In: Proc.Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  14. Hoogs, A., Collins, R., Kaucic, R., Mundy, J.: A common set of perceptual observables for grouping, figure-ground discrimination and texture classification. T. PAMI 25, 458–475 (2003)

    Google Scholar 

  15. Hoogs, A., Rittscher, J., Stein, G., Schmiederer, J.: Video content annotation using visual analysis and large semantic knowledgebase. In: Proc. CVPR. IEEE, Los Alamitos (2003)

    Google Scholar 

  16. Kaucic, R., Perera, A.G.A., Brooksby, G., Kaufhold, J., Hoogs, A.: A unified framework for tracking through occlusions and across sensor gaps. In: Proc. CVPR, pp. 990–997 (2005)

    Google Scholar 

  17. Kaucic, R.A., McCulloch, C.C., Mendonça, P.R.S., Walter, D.J., Avila, R.S., Mundy, J.L.: Model-based detection of lung nodules in CT exams. In: Lemke, H.U., Vannier, M.W., Inamura, K., Farman, A.G., Doi, K., Reiber, J.H.C. (eds.) Computer Assisted Radiology and Surgery, London, UK. International Congress Series, vol. 1256, pp. 990–997. Elsevier, Amsterdam (2003)

    Google Scholar 

  18. Kaufhold, J., Hoogs, A.: Learning to segment images using region-based perceptual features. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos (2004)

    Google Scholar 

  19. Ke, Y., Sukthankar, R., Huston, L.: Efficient near-duplicate and sub-image retrieval. In: Proc. ACM Multimedia (2004)

    Google Scholar 

  20. Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proc. Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  21. Krahnstoever, N., Mendonca, P.: Bayesian autocalibration for surveillance. In: Proc. ICCV. IEEE, Los Alamitos (2005)

    Google Scholar 

  22. Krahnstoever, N., Kelliher, T., Rittscher, J.: Obtaining pareto optimal performance of visual surveillance algorithms. In: Proc. of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2005)

    Google Scholar 

  23. Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA (2005)

    Google Scholar 

  24. Liu, X., Chen, T., Rittscher, J.: Optimal pose for face recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  25. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (2004)

    Google Scholar 

  26. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)

    Article  Google Scholar 

  27. McCulloch, C.C., Kaucic, R.A., Mendonça, P.R.S., Walter, D.J., Avila, R.S.: Model-based detection of lung nodules in computed tomography exams. Academic Radiology 11, 258–266 (2004)

    Article  Google Scholar 

  28. Meng, Y., Chang, E., Li, B.: Enhancing DPF for near-replica image recognition. In: Proc. Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  29. Perera, A., Srinivas, C., Hoogs, A., Brooksby, G., Hu, W.: Multi-object tracking through simultaneous long occlusions and split-merge conditions. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  30. Rothganger, F., Lazebnik, S., Schmid, C., Ponce, J.: 3D Object Modeling and Recognition Using Affine-Invariant Patches and Multi-View Spatial Constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2003), Madison, WI, vol. II, pp. 272–277 (June 2003)

    Google Scholar 

  31. Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proc. IEEE International Conference on Computer Vision (2003)

    Google Scholar 

  32. Rittscher, J., Tu, P., Krahnstoever, N.: Simultaneous estimation of segmentation and shape. In: Proc. CVPR. IEEE, Los Alamitos (2005)

    Google Scholar 

  33. Rittscher, J., Blake, A., Hoogs, A., Stein, G.: Mathematical modeling of animate and intentional motion. Philosophical Transactions of the Royal Society of London: Biological Sciences 358, 475–490 (2003)

    Article  Google Scholar 

  34. Sanson, H.: Video indexing: Myth and reality. In: Fourth International Workshop on Content-Based Multimedia Indexing, Riga, Latvia (2005)

    Google Scholar 

  35. Snoek, C., Worring, M.: Multimodal video indexing: A review of the state-of-the-art. Multimedia Tools and Applications 25, 5–35 (2005)

    Article  Google Scholar 

  36. Stein, G., Rittscher, J., Hoogs, A.: Enabling video annotation using a semantic database extended with visual knowledge. In: Proceedings of the International Conference on Multimedia and Expo. IEEE, Los Alamitos (2003)

    Google Scholar 

  37. Tu, P., Mendonca, P.: Surface reconstruction via helmholtz reciprocity with a single image pair. In: Proc. CVPR (2003)

    Google Scholar 

  38. Tu, P., Rittscher, J., Kelliher, T.: Challenges to Fingerprints (2005)

    Google Scholar 

  39. Tu, P., Hartley, R.: Statistical significance as an aid to system performance evaluation. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 366–378. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  40. Tu, P., Hartley, R., Allyassin, A., Lorensen, W., Gupta, R., Heier, L.: Face reconstructions using flesh deformation modes. In: International Association for Craniofacial Identification (2000)

    Google Scholar 

  41. Tu, P., Rittscher, J., Kelliher, T.: Site calibration for large indoor scenes. In: Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE, Los Alamitos (2003)

    Google Scholar 

  42. Varma, M., Zisserman, A.: Classifying images of materials: Achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  43. Veltkamp, R.C., Tanase, M.: Content-based image retrieval systems: A survey. IEEE Image Processing 1, 100–148 (2001)

    Google Scholar 

  44. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Int. Conf. on Computer Vision and Patttern Recognition, Hawaii, US, pp. 511–518 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hirano, Y., Garcia, C., Sukthankar, R., Hoogs, A. (2006). Industry and Object Recognition: Applications, Applied Research and Challenges. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957959_3

Download citation

  • DOI: https://doi.org/10.1007/11957959_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68794-8

  • Online ISBN: 978-3-540-68795-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics