Abstract
Images with known shapes can be analyzed through template matching and segmentation; in this approach the question is how to represent a known shape. The digital representation to which the shape is sampled, the image, may be subject to noise. If we compare a known and idealized shape to the real-life occurrences, a considerable variation is observed. With respect to the shape, this variation can have affine characteristics as well as non-linear deformations. We propose a method based on a deformable template starting from a low-level vision and proceeding to high-level vision. The latter part is typically application dependent, here the shapes are annotated according to an ideal template and are normalized by a straightening process. The underlying algorithm can deal with a range of deformations and does not restrict to a single instance of a shape in the image. Experimental results from an application of the algorithm illustrate low error rate and robustness of the method. The life sciences are a challenging area in terms of applications in which a considerable variation of the shape of object instances is observed. Successful application of this method would be typically suitable for automated procedures such as those required for biomedical high-throughput screening. As a case study, we, therefore, illustrate our method in this context, i.e. retrieving instances of shapes obtained from a screening experiment.
Keywords
- Content-based Indexing
- Search and Retrieval
- Object detection and Localization
- Object Recognition
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. John Wiley & Sons, Ltd. (2009)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models: their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Cootes, T.F., Taylor, C.J., Lanitis, A.: Active Shape Models: Evaluation of a Multiresolution Method for Improving Image Searches. In: Proceedings of the British Machine Vision Conference, vol. 1, pp. 327–336 (1994)
Dormand, J.R., Prince, P.J.: A family of embedded Runge–Kutta formulae. J. Comp. Appl. Math. 6(6), 19–26 (1980)
Felzenszwalb, P.: Representation and Detection of Shapes in Images. Ph.D. dissertation, Massachusetts Institute of Technology (2003)
Felzenszwalb, P.: Representation and Detection of Deformable Shapes. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), vol. 1, p. 102 (2003)
Garrido, A., Perez de la Blanca, N.: Applying deformable templates for cell segmentation. Pattern Recognition 33 (2000)
Gonzales, R., Woods, R.: Digital Image Processing, 2nd edn. Addison-Wesley, London (2001)
Jain, A.K., Zhong, Y., Lakshmanan, S.: Object matching using deformable templates. IEEE Tran. on Pattern Analysis and Machine Intell. 18(3) (1996)
Jain, A.K., Zhong, Y., Dubuisson-Jolly, M.: Deformable Template Models: a Review. In: Signal Processing - Special Issue on Deformable Models and Techniques for Image and Signal. Elsevier (1998)
Zhong, Y., Jain, A.K.: Object localization using color, texture and shape. Pattern Recognition 33 (2000)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. Journal of Comput. Vision 1(4) (1987)
Kim, H.Y., de Araújo, S.A.: Grayscale Template-Matching Invariant to Rotation, Scale, Translation, Brightness and Contrast. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 100–113. Springer, Heidelberg (2007)
Leroy, B., Herlin, I., Cohen, L.D.: Multi-resolution algorithms for active contour models. In: Proceedings of the 12th International Conference on Analysis and Optimization of Systems Images, Wavelets and PDE’S, Rocquencourt (1996)
Liu, Z., Wang, Y.: Face detection and tracking in video using dynamic programming. In: Proceedings of International Conference on Image Processing (2000)
Nezhinsky, A.E., Verbeek, F.J.: Pattern Recognition for High Throughput Zebrafish Imaging Using Genetic Algorithm Optimization. In: Dijkstra, T.M.H., Tsivtsivadze, E., Marchiori, E., Heskes, T. (eds.) PRIB 2010. LNCS (LNBI), vol. 6282, pp. 301–312. Springer, Heidelberg (2010)
Ng, H.P., Ong, S.H., Goh, P.S., Foong, K.W.C., Nowinski, W.L.: Template-based Automatic Segmentation of Masseter Using Prior Knowledge. In: Proceeding SSIAI 2006 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation (2006)
Peng, H., et al.: Straightening Caenorhabditis elegans images. Bioinformatics 24, 234–242 (2008)
Ren, M., Yang, J., Sun, H.: Tracing boundary contours in a binary image. In: Image and Vision Computing (2002)
Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. System, Man and Cybernetics SMC-8, 630–632 (1978)
Stoop, E.J.M., Schipper, T., Rosendahl Huber, S.K., Nezhinsky, A.E., Verbeek, F.J., Gurcha, S.S., Besra, G.S., Vandenbroucke-Grauls, C.M.J.E., Bitter, W., van der Sar, A.M.: Zebrafish embryo screen for mycobacterial genes involved in the initiation of granuloma formation reveals a newly identified ESX-1 component. Dis. Model. Mech. 4(4), 526–536 (2011)
Tagare, H.D.: Deformable 2-D template matching using orthogonal curves. IEEE Transactions on Medical Imaging 16(1), 108–117 (1997), http://www.ncbi.nlm.nih.gov/pubmed/9050413
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Transactions on Systems Man Cybernet (1978)
Verbeek, F.J.: Three-dimensional reconstruction from serial sections including deformation correction Delft University of Technology, Delft (1995)
Verbeek, F.J., Boon, P.J.: High-resolution 3D reconstruction from serial sections: microscope instrumentation, software design, and its implementations. In: Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing IX (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nezhinsky, A.E., Verbeek, F.J. (2012). Efficient and Robust Shape Retrieval from Deformable Templates. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies. ISoLA 2012. Lecture Notes in Computer Science, vol 7610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34032-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-34032-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34031-4
Online ISBN: 978-3-642-34032-1
eBook Packages: Computer ScienceComputer Science (R0)
