Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-f46jp Total loading time: 0 Render date: 2025-01-27T17:01:03.920Z Has data issue: false hasContentIssue false

9 - Geometric features extraction

from Part III - Computational geometry

Published online by Cambridge University Press:  05 November 2014

Aly A. Farag
Aly A. Farag
Affiliation:
University of Louisville, Kentucky
Get access

Summary

Objects may be represented by various forms, but robust representations must maintain the features that describe the objects and enable analysis and decision making. As objects may not have a specific geometric description, these features may not be easy to specify. Likewise, known features about an object may be altered in the imaging process. Furthermore, if an object is to be compared with similar ones in a database, it is important that features involved in this comparison be robust (ideally invariant) to changes in scale, rotation, and translation. This chapter deals with feature definitions and characterization through feature descriptors. In the computer vision and image analysis literature, various approaches have been introduced to define, detect, and describe features. Local photometric and geometric features have proven to be very successful in applications such as object recognition, stereo matching, image retrieval, robot localization, video data mining, building panoramas, and recognition of object categories (e.g. [9.1]–[9.5]). This chapter will discuss global and local features, and how to extract corners, edges, contours or salient regions, which are among the common features used in image analysis algorithms. The chapter will describe feature detection and a number of efficient descriptors, including SIFT, ASIFT, and SURF. Good surveys of interest-point detectors and feature descriptors exist in the computer vision literature (e.g. [9.6],[9.7]).

Introduction

This section will provide basic definitions related to features and descriptors. The previous chapter discussed geometric and topological representations of objects. As much as possible, we will maintain the same terminologies and concepts.

A local feature is a point or pattern in an image that differs from its immediate neighborhood, and is associated with a change in an image property or a number of properties simultaneously.■

Type
Chapter
Information
Biomedical Image Analysis
Statistical and Variational Methods
 , pp. 213 - 272
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Lowe, D. G., Distinctive image features from scale-invariant keypoints. Int. J. Computer Vision, 60(2) (2004) 91–110.CrossRefGoogle Scholar
Tuytelaars, T. and Van Gool, L., Matching widely separated views based on affine invariant regions, Int. J. Computer Vision, 1(59) (2004) 61–85.CrossRefGoogle Scholar
Schmid, C. and Mohr, R., Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Machine Intel. 19(5) (1997) 530–535.CrossRefGoogle Scholar
Mikolajczyk, K., Tuytelaars, T., Schmid, C. et al., A comparison of affine region detectors. Int. J. Computer Vision 65(1/2) (2005) 43–72.CrossRefGoogle Scholar
Brown, M. and Lowe, D., Recognising panoramas, Proc. Ninth Int. Conf. Computer Vision (2003) 1218–1227.CrossRefGoogle Scholar
Schmid, C., Mohr, R. and Bauckhage, C., Evaluation of interest point detectors. Int. J. Computer Vision 37(2) (2000) 151–172.CrossRefGoogle Scholar
Mikolajczyk, K. and Schmid, C., A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Intel. 27(10) (2005) 1615–1630.CrossRefGoogle ScholarPubMed
Hafner, J., Sawhney, H. S., Equitz, W. et al., Efficient color histogram indexing for quadratic form distance functions. IEEE Trans. Pattern Anal Machine Intel. 17(7) (1995) 729–736.CrossRefGoogle Scholar
Belongie, S., Malik, J. and Puzicha, J., Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Machine Intel 24(4) (2002) 509–522.CrossRefGoogle Scholar
Nayar, S. and Bolle, R., Reflectance based object recognition. Int. J. Computer Vision 17(3) (1996) 219–240.CrossRefGoogle Scholar
Pentland, A., Picard, R. W. and Sclaroff, S., Photobook: content-based manipulation of image databases. Int. J. Computer Vision 18(3) (1996) 233–254.CrossRefGoogle Scholar
Canny, J., A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intel 8(6) (1986) 679–698.CrossRefGoogle ScholarPubMed
Harris, C. and Stephens, M., A combined corner and edge detector. Proc. Fourth Alvey Vision Conf. (1988) 147–152.Google Scholar
Lindeberg, T., Scale-Space Theory in Computer Vision. Norwell, MA: Kluwer Academic (1994).CrossRefGoogle Scholar
Belongie, S., Malik, J. and Puzicha, J., Shape context: a new descriptor for shape matching and object recognition. In Leen, T. K., Dietterich, T. G. and Tresp, V. (eds.) Advances in Neural Information Processing Systems 13. MIT Press (2001) 831–837.Google Scholar
Zhang, Y., Brady, M. and Smith, S., Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans. Medical Imaging 20(1) (2001) 45–57.CrossRefGoogle ScholarPubMed
Mikolajczyk, K. and Schmid, C., An affine invariant interest point detector. Proc. 7th Eur. Conf. Computer Vision-Part I (ECCV ‘02). Copenhagen: Springer (2002) 128–142.Google Scholar
Mikolajczyk, K. and Schmid, C., Scale and affine invariant interest point detectors. Int. J. Computer Vision 60(1) (2004) 63–86.CrossRefGoogle Scholar
Kadir, T. and Brady, M., Scale, saliency and image description. Int. J. Computer Vision 45(2) (2001). 83–105.CrossRefGoogle Scholar
Matas, J., Chum, O., Urban, M. and Pajdla, T., Robust wide-baseline stereo from maximally stable extremal regions. In Proc. British Machine Vision Conf.Cardiff, UK (2002) 384–393.Google Scholar
Klinger, A., Pattern and search statistics. In Rustagi, J. S. (ed.) Optimizing Methods in Statistics. New York: Academic Press (1971).Google Scholar
Daubechies, I., Orthonormal bases of compactly supported wavelets. Comm. Pure Appl. Math. 41 (1988) 909–996.CrossRefGoogle Scholar
Witkin, A., Scale-space filtering. Int. Joint Conf. Artificial Intelligence, Karlsruhe, Germany (1983) 1019–1022.Google Scholar
Koenderink, J., The structure of images. Biol. Cybernet. 50 (1984) 363–370.CrossRefGoogle ScholarPubMed
Kanatani, K., Group Theoretical Methods in Image Understanding. Secaucus, NJ: Springer (1990).CrossRefGoogle Scholar
Florack, L., Haar Romeny, B., Koenderink, J. and Viergever, M., Cartesian differential invariants in scale-space. J. Math. Imaging Vision 3(4) (1993). 327–348.CrossRefGoogle Scholar
Bruce, J. and Giblin, P., Curves and Singularities. Cambridge: Cambridge University Press (1984).Google Scholar
Shepp, L. A. and Logan, B. F., The Fourier reconstruction of a head section. IEEE Trans. Nucl. Sci. (21) (1974) 21–43.CrossRefGoogle Scholar
Morel, J. M. and Yu, G., ASIFT: A new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2(2) (2009) 438–469.CrossRefGoogle Scholar
Abdel-Hakim, A. and Farag, A., CSIFT: A SIFT descriptor with color invariant characteristics. IEEE Conf. Computer Vision and Pattern Recognition (CVPR06), New York City, 17–22 June (2006). 1978–1983.Google Scholar
Brown, M. and Süsstrunk, S., Multispectral SIFT for Scene Category Recognition. Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR2011), Colorado Springs (2011) 177–184.Google Scholar
Fischler, M. A. and Bolles, R. C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6) (1981) 381–395.CrossRefGoogle Scholar
Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L., Speeded-Up Robust Features (SURF), Computer Vision Image Understand. 110 (3) (2008) 346–359.CrossRefGoogle Scholar
Ojala, T., Pietikainen, M. and Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Machine Intel. 24 (2002) 971–987.CrossRefGoogle Scholar
Farag, Amal, Elhabian, S., Graham, J., Aly Farag and R. Falk, Toward precise pulmonary nodule descriptors for nodule type classification. Proc. 13th Int. Conf. Medical Image Computing and Computer Assisted Intervention (MICCAI), Beijing, China (2010) 626–633.Google Scholar
Farag, Amal, Modeling small size objects under uncertainty: novel algorithms and applications. Unpublished Ph.D. dissertation, CVIP Lab., University of Louisville, May 2012.
Farag, A., El-Baz, A., Gimel’farb, G. and Abdel-Hakim, A., Robust image registration based on Markov–Gibbs appearance model. In IEEE Int. Conf. Pattern Recognition ICPR06, Hong Kong, August 20–24 (2006) 1204–1207.Google Scholar
Hartley, R. and Zisserman, A., Multiple View Geometry in Computer Vision. Cambridge: Cambridge University Press (2003).Google Scholar
Ahmed, M. N., Yamany, S. M., Hemayed, E. E. and Farag, A. A., 3D reconstruction of the human jaw from a sequence of images, IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR’97), Puerto Rico (1997) 646–653.CrossRefGoogle Scholar
Yamany, S. M., Farag, A. A., Tasman, D. and Farman, A. G., Robust 3-D modeling of the human jaw using sequence of intra-oral images. IEEE Trans. Med. Imaging 19(5) (2000) 538–547.CrossRefGoogle Scholar
Farag, A. and Eid, A., Video reconstructions in dentistry, Orthod Craniofacial Res. 6 (Suppl. 1) (2003) 108–116.CrossRefGoogle ScholarPubMed
Farag, A., Yamany, S. and Tasman, D., US Patent 7084868: System and method for 3-D digital reconstruction of an oral cavity from sequence of 2-D images. Issued 8/1/2006.
Abdelrahim, A., Abderahman, M., Abdelmunim, H., Farag, A. and Miller, M., Novel image-based 3D reconstruction of the human jaw using shape from shading and feature descriptors, 22nd British Machine Vision Conf. (BMVC), 41 (2011) 1–11.Google Scholar
Hassouna, M. Sabry and Farag, A., PDE-based three dimensional path planning for virtual endoscopy. Proc. Information Processing in Medical Imaging (IPMI), Glenwood Springs, CO, July 11–15 (2005) 529–540.Google ScholarPubMed
Chen, D., Farag, A., Falk, R. and Dryden, G., Variational approach based image pre-processing techniques for virtual colonoscopy. In Gonzalez, F. and Romero, E. (eds.) Biomedical Image Analysis and Machine Learning Technologies: Application and Techniques, Hershey, PA: Medical Information Science Reference (2009) Chapter 4.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Geometric features extraction
  • Aly A. Farag, University of Louisville, Kentucky
  • Book: Biomedical Image Analysis
  • Online publication: 05 November 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139022675.014
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Geometric features extraction
  • Aly A. Farag, University of Louisville, Kentucky
  • Book: Biomedical Image Analysis
  • Online publication: 05 November 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139022675.014
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Geometric features extraction
  • Aly A. Farag, University of Louisville, Kentucky
  • Book: Biomedical Image Analysis
  • Online publication: 05 November 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139022675.014
Available formats
×