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.
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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
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DOI: https://doi.org/10.1007/978-3-642-34032-1_5
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