Abstract
A conventional approach to image analysis is to separately perform feature extraction at a low level (such as edge detection) and follow this with high level feature extraction to determine structure (e.g. by collecting edge points) using the Hough transform. The original image Ray Transform (IRT) demonstrated capability to extract structures at a low level. Here we extend the IRT to add shape specificity that makes it select specific shapes rather than just edges; the new capability is achieved by addition of a single parameter that controls which shape is selected by the extended IRT. The extended approach can then perform low-and high-level feature extraction simultaneously. We show how the IRT process can be extended to focus on chosen shapes such as lines and circles. Histogram patterns, which are an extension to this new capability, can describe extracted features showing that the extracted patterns are robust to change in orientation, position and scale. We confirm the new capability by using conventional methods for exact shape location, such as the Hough transform. We analyse performance with images from the Caltech-256 dataset and show that the new approach can indeed select chosen shapes. Further research will aim to capitalise on the new extraction ability to extend descriptive capability.
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References
Beucher S, Lantuejoul C (1979) Use of watersheds in contour detection. Proceedings of International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation
Cummings AH, Nixon MS, Carter JN (2010) A novel ray analogy for enrolment of ear biometrics. Proc. IEEE BTAS
Cummings AH, Nixon MS, Carter JN (2011) The image ray transform for structural feature detection. Pattern Recogn Lett 32(15):2053–2060
Direkoglu C, Nixon MS (2011) Moving-edge detection via heat flow analogy. Pattern Recogn Lett 32(2):270–279
Griffin G, Holub AD, Perona P (2006) Caltech-256 Object Category Dataset, Caltech Technical Report
Hough PVC (1962) “Method and means for recognizing complex patterns”, U.S. Patent 3.069.654
Hurley DJ, Nixon MS, Carter JN (2002) Force field energy functionals for image feature extraction. Image Vis Comput 20:311–317
Madabhushi A, Metaxas DN (2003) Combining low-, high lebel and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans Med Imaging 22(2):155–169
Maragos P (2005) PDEs for morphological scale-spaces and eikonal applications. In: Bovik AC (ed) The Image and Video Processing Handbook, chapter 4.16, 2nd edn. Elsevier, pp 587–612
Oh A, Nixon MS (2013) On a shape adaptive image ray transform. Proc. Signal-Image Technology & Internet-based Systems. pp 100–105
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Rubner Y, Tomasi C, Guibas LJ (1998) A Metric for distributions with applications to image databases. Proceedings of the 6th International Conference on Computer Vision, pp 59–66
Smeaton AF, Over P, Kraaij W (2009) High-level feature detection from video in TRECVid: a 5-year retrospective of achievements. In: Ajay D (ed) Multimedia Content Analysis: Theory and Applications, Springer series on Signals and Communication Technology. pp 151–174
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Oh, AR., Nixon, M.S. Extending the image ray transform for shape detection and extraction. Multimed Tools Appl 74, 8597–8612 (2015). https://doi.org/10.1007/s11042-014-2348-9
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DOI: https://doi.org/10.1007/s11042-014-2348-9