Skip to main content

Part of the book series: Studies in Computational Intelligence ((SCI,volume 821))

Abstract

Computer vision relies on image features describing points, edges, objects or colour. The book concerns solely so-called hand-made features contrary to learned features which exist in deep learning methods. Image features can be generally divided into global and local methods.

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 EPUB and 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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Al-Amri, S.S., Kalyankar, N.V., et al.: Image segmentation by using threshold techniques (2010). arXiv preprint arXiv:1005.4020

  2. Bansal, B., Saini, J.S., Bansal, V., Kaur, G.: Comparison of various edge detection techniques. J. Inf. Oper. Manag. 3(1), 103–106 (2012)

    Google Scholar 

  3. Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1485–1490 (2005)

    Article  Google Scholar 

  4. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Computer vision–ECCV 2006, pp. 404–417. Springer (2006)

    Google Scholar 

  5. Bazarganigilani, M.: Optimized image feature selection using pairwise classifiers. J. Artif. Intell. Soft Comput. Res. 1 (2011)

    Google Scholar 

  6. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: Binary robust independent elementary features. Comput. Vis. ECCV 2010, 778–792 (2010)

    Google Scholar 

  7. Canny, J.: A computational approach to edge detection. Pattern Anal. Mach. Intell. IEEE Trans. PAMI-8(6), 679–698 (1986). https://doi.org/10.1109/TPAMI.1986.4767851

  8. Chang, Y., Wang, Y., Chen, C., Ricanek, K.: Improved image-based automatic gender classification by feature selection. J. Artif. Intell. Soft Comput. Res. 1(3), 241–253 (2011)

    Google Scholar 

  9. Chatzichristofis, S.A., Boutalis, Y.S.: Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: International Conference on Computer Vision Systems, pp. 312–322. Springer (2008)

    Google Scholar 

  10. Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1197–1203. IEEE (1999)

    Google Scholar 

  11. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  13. Damiand, G., Resch, P.: Split-and-merge algorithms defined on topological maps for 3d image segmentation. Gr. Models 65(1), 149–167 (2003)

    Article  Google Scholar 

  14. Derpanis, K.G.: Mean shift clustering. Lecture Notes (2005). http://www.cse.yorku.ca/~kosta/CompVis_Notes/mean_shift.pdf

  15. Evans, C.: Notes on the opensurf library. University of Bristol, Technical Report CSTR-09-001, January (2009)

    Google Scholar 

  16. Fei-Fei Li, M.A., Ranzato, M.: The pascalobject recognition database collection, unannotated databases - 101 object categories (2009)

    Google Scholar 

  17. Georgescu, B., Shimshoni, I., Meer, P.: Mean shift based clustering in high dimensions: a texture classification example. In: Proceedings of Ninth IEEE International Conference on Computer Vision, 2003, pp. 456–463. IEEE (2003)

    Google Scholar 

  18. Glantz, R., Pelillo, M., Kropatsch, W.G.: Matching segmentation hierarchies. Int. J. Pattern Recogn. Artif. Intell. 18(03), 397–424 (2004)

    Article  Google Scholar 

  19. Górecki, P., Sopyła, K., Drozda, P.: Ranking by K-means voting algorithm for similar image retrieval. In: International Conference on Artificial Intelligence and Soft Computing, pp. 509–517. Springer (2012)

    Google Scholar 

  20. Gould, S., Gao, T., Koller, D.: Region-based segmentation and object detection. In: Advances in Neural Information Processing Systems, pp. 655–663 (2009)

    Google Scholar 

  21. Grycuk, R.: Novel visual object descriptor using surf and clustering algorithms. J. Appl. Math. Comput. Mech. 15(3), 37–46 (2016)

    Article  Google Scholar 

  22. Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image indexing by data clustering and inverse document frequency. Beyond Databases. Architectures and Structures 2014, Communications in Computer and Information Science, pp. 374–383. Springer, Berlin, Heidelberg (2014)

    Google Scholar 

  23. Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Romanowski, J.: Improved digital image segmentation based on stereo vision and mean shift algorithm. In: Parallel Processing and Applied Mathematics 2013, Lecture Notes in Computer Science. Springer Berlin Heidelberg (2014). Manuscript accepted for publication

    Google Scholar 

  24. Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Voloshynovskiy, S.: From single image to list of objects based on edge and blob detection. In: International Conference on Artificial Intelligence and Soft Computing, pp. 605–615. Springer (2014)

    Google Scholar 

  25. Gunn, S.R.: On the discrete representation of the laplacian of gaussian. Pattern Recogn. 32(8), 1463–1472 (1999)

    Article  Google Scholar 

  26. Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Comput. Vis. Graph. Image Process. 29(1), 100–132 (1985)

    Article  Google Scholar 

  27. Hare, J.S., Samangooei, S., Lewis, P.H.: Efficient clustering and quantisation of sift features: exploiting characteristics of the sift descriptor and interest region detectors under image inversion. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 2. ACM (2011)

    Google Scholar 

  28. Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Royal Stat. Soc Ser. C (Appl. Stat.) 28(1), 100–108 (1979)

    Google Scholar 

  29. Iakovidou, C., Bampis, L., Chatzichristofis, S.A., Boutalis, Y.S., Amanatiadis, A.: Color and edge directivity descriptor on gpgpu. In: 2015 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 301–308. IEEE (2015)

    Google Scholar 

  30. Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision, vol. 5. McGraw-Hill New York (1995)

    Google Scholar 

  31. Jiang, X., Bunke, H.: Edge detection in range images based on scan line approximation. Comput. Vis. Image Underst. 73(2), 183–199 (1999)

    Article  Google Scholar 

  32. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  Google Scholar 

  33. Katto, J., Ohta, M.: Novel algorithms for object extraction using multiple camera inputs. In: Proceedings of International Conference on Image Processing, 1996, vol. 1, pp. 863–866. IEEE (1996)

    Google Scholar 

  34. Kirillov, A.: Detecting some simple shapes in images. (2010). http://www.aforgenet.com

  35. Kumar, P.P., Aparna, D.K., PhD, V.R.: Compact descriptors for accurate image indexing and retrieval: Fcth and cedd. Int. J. Eng. Res. Technol. (IJERT) 1, 2278–0181 (2012)

    Google Scholar 

  36. Lowe, D.G.: Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on Computer vision, 1999, vol. 2, pp. 1150–1157. IEEE (1999)

    Google Scholar 

  37. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  38. Luo, Y., Duraiswami, R.: Canny edge detection on nvidia cuda. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  39. Macedo-Cruz, A., Pajares-Martinsanz, G., Peñas, M.S.: Unsupervised cassification of images in RGB color model and cluster validation techniques. In: IPCV, pp. 526–532 (2010)

    Google Scholar 

  40. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, pp. 281–297. Oakland, CA, USA (1967)

    Google Scholar 

  41. Maintz, T.: Digital and Medical Image Processing. Universiteit Utrecht (2005)

    Google Scholar 

  42. Marugame, A., Yamada, A., Ohta, M.: Focused object extraction with multiple cameras. Circuits Syst. Video Technol. IEEE Trans. 10(4), 530–540 (2000)

    Article  Google Scholar 

  43. Montazer, G.A., Giveki, D.: Content based image retrieval system using clustered scale invariant feature transforms. Optik-Int. J. Light and Electron. Opt. 126(18), 1695–1699 (2015)

    Article  Google Scholar 

  44. Moon, W.K., Shen, Y.W., Bae, M.S., Huang, C.S., Chen, J.H., Chang, R.F.: Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images. IEEE Trans. Med. Imag. 32(7), 1191–1200 (2013)

    Article  Google Scholar 

  45. Nakib, A., Najman, L., Talbot, H., Siarry, P.: Application of graph partitioning to image segmentation. Graph Parti., 249–274 (2013)

    Google Scholar 

  46. Ng, P.C., Henikoff, S.: Sift: predicting amino acid changes that affect protein function. Nucleic Acid. Res. 31(13), 3812–3814 (2003)

    Article  Google Scholar 

  47. Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: Proceedings of the Fourth ACM International Conference on Multimedia, pp. 65–73. ACM (1997)

    Google Scholar 

  48. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Computer Vision–ECCV 2006, pp. 430–443. Springer (2006)

    Google Scholar 

  49. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)

    Google Scholar 

  50. Schreiber, J., Schubert, R., Kuhn, V.: Femur detection in radiographs using template-based registration. In: Bildverarbeitung für die Medizin 2006, pp. 111–115. Springer (2006)

    Google Scholar 

  51. Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation-a survey of soft computing approaches. Int. J. Recent Trends Eng. 1(2), 250–254 (2009)

    Google Scholar 

  52. Shrivakshan, G., Chandrasekar, C., et al.: A comparison of various edge detection techniques used in image processing. IJCSI Int. J. Comput. Sci. Issues 9(5), 272–276 (2012)

    Google Scholar 

  53. Šváb, J., Krajník, T., Faigl, J., Přeučil, L.: Fpga based speeded up robust features. In: IEEE International Conference on Technologies for Practical Robot Applications, 2009. TePRA 2009, pp. 35–41. IEEE (2009)

    Google Scholar 

  54. Tamaki, T., Yamamura, T., Ohnishi, N.: Image segmentation and object extraction based on geometric features of regions. In: Electronic Imaging 1999, pp. 937–945. International Society for Optics and Photonics (1998)

    Google Scholar 

  55. Tao, D.: The corel database for content based image retrieval (2009)

    Google Scholar 

  56. Terriberry, T.B., French, L.M., Helmsen, J.: GPU accelerating speeded-up robust features. In: Proceedings International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), pp. 355–362. Citeseer (2008)

    Google Scholar 

  57. Velmurugan, K., Baboo, L.D.S.S.: Content-based image retrieval using surf and colour moments. Global J. Comput. Sci. Technol. 11(10) (2011)

    Google Scholar 

  58. Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S., et al.: Constrained k-means clustering with background knowledge. ICML 1, 577–584 (2001)

    Google Scholar 

  59. Wang, B., Fan, S.: An improved canny edge detection algorithm. In: Second International Workshop on Computer Science and Engineering, 2009. WCSE 2009. , vol. 1, pp. 497–500. IEEE (2009)

    Google Scholar 

  60. Wani, M.A., Batchelor, B.G.: Edge-region-based segmentation of range images. IEEE Trans. Pattern Anal. Mach. Intell. 16(3), 314–319 (1994). https://doi.org/10.1109/34.276131

  61. Wu, M.N., Lin, C.C., Chang, C.C.: Brain tumor detection using color-based k-means clustering segmentation. In: Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007, vol. 2, pp. 245–250. IEEE (2007)

    Google Scholar 

  62. Wu, Q., Yu, Y.: Two-level image segmentation based on region and edge integration. In: DICTA, pp. 957–966 (2003)

    Google Scholar 

  63. Yoon, Y., Ban, K.D., Yoon, H., Kim, J.: Blob extraction based character segmentation method for automatic license plate recognition system. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2192–2196. IEEE (2011)

    Google Scholar 

  64. Young, R.A.: The gaussian derivative model for spatial vision: I. retinal mechanisms. Spat. Vis. 2(4), 273–293 (1987)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafał Scherer .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Scherer, R. (2020). Feature Detection. In: Computer Vision Methods for Fast Image Classification and Retrieval. Studies in Computational Intelligence, vol 821. Springer, Cham. https://doi.org/10.1007/978-3-030-12195-2_2

Download citation

Publish with us

Policies and ethics