Measuring Shapes with Desired Convex Polygons

In this paper we have developed a family of shape measures. All the measures from the family evaluate the degree to which a shape looks like a predefined convex polygon. A quite new approach in designing object shape based measures has been applied. In most cases such measures were defined by exploiting some shape properties. Such properties are optimized (e.g., maximized or minimized) by certain shapes and based on this, the new shape measures were defined. An illustrative example might be the shape circularity measure derived by exploiting the well-known result that the circle has the largest area among all the shapes with the same perimeter. Of course, there are many more such examples (e.g., ellipticity, linearity, elongation, and squareness measures are some of them). There are different approaches as well. In the approach applied here, no desired property is needed and no optimizing shape has to be found. We start from a desired convex polygon, and develop the related shape measure. The method also allows a tuning parameter. Thus, there is a new 2-fold family of shape measures, dependent on a predefined convex polygon, and a tuning parameter, that controls the measure's behavior. The measures obtained range over the interval (0,1] and pick the maximal possible value, equal to 1, if and only if the measured shape coincides with the selected convex polygon that was used to develop the particular measure. All the measures are invariant with respect to translations, rotations, and scaling transformations. An extension of the method leads to a family of new shape convexity measures.

[1]  Anil K. Jain,et al.  Shape-Based Retrieval: A Case Study With Trademark Image Databases , 1998, Pattern Recognit..

[2]  Jacek Tabor,et al.  Ellipticity and Circularity Measuring via Kullback–Leibler Divergence , 2015, Journal of Mathematical Imaging and Vision.

[3]  Enrico Grisan,et al.  A Novel Method for the Automatic Grading of Retinal Vessel Tortuosity , 2008, IEEE Transactions on Medical Imaging.

[4]  T. Kamae,et al.  Can one measure the temperature of a curve? , 1986 .

[5]  Dong Xu,et al.  Geometric moment invariants , 2008, Pattern Recognit..

[6]  B. Horton,et al.  The Development and Application of a Diatom‐Based Quantitative Reconstruction Technique in Forensic Science , 2006, Journal of forensic sciences.

[8]  Esa Rahtu,et al.  A new convexity measure based on a probabilistic interpretation of images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Danilo P. Mandic,et al.  Signal nonlinearity in fMRI: a comparison between BOLD and MION , 2003, IEEE Transactions on Medical Imaging.

[10]  Horst Bunke,et al.  Diatom identification: a double challenge called ADIAC , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[11]  Esther M. Arkin,et al.  An efficiently computable metric for comparing polygonal shapes , 1991, SODA '90.

[12]  Ognjen Arandjelovic Computationally efficient application of the generic shape-illumination invariant to face recognition from video , 2012, Pattern Recognit..

[13]  C Marendaz,et al.  Detection of shape orientation depends on salient axes of symmetry and elongation: Evidence from visual search , 2001, Perception & psychophysics.

[14]  K. Revathy,et al.  Galaxy classification using fractal signature , 2003 .

[15]  B. Rosenhahn,et al.  Automatic track recognition of footprints for identifying cryptic species. , 2009, Ecology.

[16]  S. Gyergyek,et al.  Quantifying shapes of nanoparticles using modified circularity and ellipticity measures , 2016 .

[17]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  D. Proffitt,et al.  The measurement of circularity and ellipticity on a digital grid , 1982, Pattern Recognit..

[19]  Kaoru Hirota,et al.  A Hu Invariant as a Shape Circularity Measure , 2014 .

[20]  Jovisa D. Zunic,et al.  A Family of Shape Ellipticity Measures for Galaxy Classification , 2013, SIAM J. Imaging Sci..

[21]  Christopher P. Chambers,et al.  A Measure of Bizarreness , 2007 .

[22]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[23]  David R. Lee,et al.  A Method of Measuring Shape , 1970 .

[24]  A. Q. Tool A Method for Measuring Ellipticity and the Determination of Optical Constants of Metals , 1910 .

[25]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[26]  Thanh Phuong Nguyen,et al.  Projection-Based Polygonality Measurement , 2015, IEEE Transactions on Image Processing.

[27]  S. M. Elshoura,et al.  Analysis of noise sensitivity of Tchebichef and Zernike moments with application to image watermarking , 2013, J. Vis. Commun. Image Represent..

[28]  Harold Davenport,et al.  On a Principle of Lipschitz , 1951 .

[29]  Amiya Nayak,et al.  Measuring linearity of planar point sets , 2008, Pattern Recognit..

[30]  Paul L. Rosin,et al.  A Convexity Measurement for Polygons , 2002, BMVC.

[31]  Edward K. Wong,et al.  DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  J. Smol,et al.  Diatom-inferred climatic and environmental changes over the last ∼ 9000 years from a low Arctic (Nunavut, Canada) tundra lake , 2010 .

[33]  Paul L. Rosin,et al.  Measuring Convexity via Convex Polygons , 2015, PSIVT Workshops.

[34]  Paul L. Rosin Measuring shape: ellipticity, rectangularity, and triangularity , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[35]  Remco C. Veltkamp,et al.  A survey of content based 3D shape retrieval methods , 2004, Proceedings Shape Modeling Applications, 2004..

[36]  J. Flusser,et al.  Moments and Moment Invariants in Pattern Recognition , 2009 .

[37]  Lazar Kopanja,et al.  Re-formation of metastable ε-Fe2O3 in post-annealing of Fe2O3/SiO2 nanostructure: Synthesis, computational particle shape analysis in micrographs and magnetic properties , 2017 .

[38]  Paul L. Rosin,et al.  Measuring Squareness and Orientation of Shapes , 2010, Journal of Mathematical Imaging and Vision.

[39]  Elisabeth T. Bowman,et al.  Particle Shape Characterisation using Fourier Analysis , 2001 .

[40]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[41]  Roland T. Chin,et al.  On image analysis by the methods of moments , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[42]  Vladimir Tikhomirov,et al.  Stories about maxima and minima , 1990 .

[43]  Bin Wang Shape retrieval using combined Fourier features , 2011 .

[44]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[45]  Ye Mei,et al.  Robust Affine Invariant Region-Based Shape Descriptors: The ICA Zernike Moment Shape Descriptor and the Whitening Zernike Moment Shape Descriptor , 2009, IEEE Signal Processing Letters.

[46]  Christine L. Mumford,et al.  A symmetric convexity measure , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..