Shape Analysis

Shape plays an important part in the processing of visual information, from art through to science, and within computer vision there have been many applications of shape to aid in the analysis of images.

shapes
We have developed shape descriptors for a variety of types of shape, for instance to measure ellipticity, rectangularity, and triangularity.

There are no standard methods for computing rectilinearity, but it has many potential applications. Rectilinear structures often correspond to human-made structure, and are therefore justified as attentional cues for further processing. For instance, in aerial image processing and reconstruction, where building footprints are often rectilinear on the local ground plane, building structures, once recognized as rectilinear can be matched to corresponding shapes in other views for stereo reconstruction.

The images below show a Digital Elevation Model Some simple noise filtering and segmentation techniques were applied to produce a set of polygons, and they are coloured with intensities proportional to their rectilinearity; thus rectilinear shapes generally appear bright. The third image shows the effects of applying a snake-based refinement which incorporates rectilinearity in addition to proximity to the original data. Deviations from rectilinearity have been corrected if it does not require excessive deformation of the shape.
DEM DEM DEM

More details are given in:

  • J. Zunic and P.L. Rosin, "Rectilinearity measurements for polygons", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1193-1200, 2003
  • P.L. Rosin, "Measuring shape: ellipticity, rectangularity, and triangularity", Machine Vision and Applications, vol. 14, no. 3, pp. 172-184, 2003.
  • P.L. Rosin, "Measuring rectangularity", Machine Vision and Applications, vol. 11, no. 4, pp. 191-196, 1999.
  • G. Gagaudakis and P.L. Rosin, "Shape measures for image retrieval", Pattern Recognition Letters, vol. 24, no. 15, pp. 2711-2721, 2003.
  • G. Gagaudakis and P.L. Rosin, "Incorporating shape into histograms for CBIR", Pattern Recognition, vol. 35, no. 1, pp.81-91, 2002.

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