Abstract
This work addresses two main contributions for shape measurement: First, a new circularity measure for planar shapes is introduced based on their geometrical properties in the projection space of Radon transform. Second, a general-purpose evaluation criterion, power of discrimination, for assessing the efficiency of a shape measure is proposed. The new measure ranges over the interval [0, 1] and produces the value 1 if and only if the measured shape is a perfect circle. The proposed measure is invariant with respect to translation, rotation and scaling transformations. Moreover, it is also robust against border distortion of shapes. It is theoretically well founded and can be extended to other problems of shape measurement. Our approach can deal with complex shapes composed of connected components that cannot be handled by classical contour-based methods. Several experiments show its good behavior and demonstrate the efficiency and applicability of our proposed measure. Finally, we also consider our proposed evaluation criterion for assessing different circularity measures.
Similar content being viewed by others
Notes
This can be publicly accessed at address http://tpnguyen.univ-tln.fr/download/floral_dataset.
References
Rosin, P.L.: Measuring shape: ellipticity, rectangularity, and triangularity. Mach. Vis. Appl. 14(3), 172–184 (2003)
Bowman, E.T., Soga, K., Drummond, T.: Particle shape characterization using Fourier analysis. Geotechnique 51(6), 545–554 (2001)
Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit. 13(2), 111–122 (1981)
Hoang, T.V., Tabbone, S.: The generalization of the R-transform for invariant pattern representation. Pattern Recognit. 45(6), 2145–2163 (2012)
Nguyen, T.P., Debled-Rennesson, I.: A discrete geometry approach for dominant point detection. Pattern Recognit. 44(1), 32–44 (2011)
Roman-Rangel, E., Wang, C., Marchand-Maillet, S.: Simmap: Similarity maps for scale invariant local shape descriptors. Neurocomputing 175, 888–898 (2016)
Hamza, A.B.: A graph-theoretic approach to 3d shape classification. Neurocomputing 211, 11–21 (2016)
Hoang, T.V., Tabbone, S.: Errata and comments on “Generic orthogonal moments: Jacobi–Fourier moments for invariant image description”. Pattern Recognit. 46(11), 3148–3155 (2013)
Kurnianggoro, L., Wahyono, Jo, K.-H.: A survey of 2d shape representation: methods, evaluations, and future research directions. Neurocomputing 300, 1–16 (2018)
Zhang, D., Lu, G.: A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval. J. Vis. Commun. Image Represent. 14, 39–57 (2003)
Carlin, M.: Measuring the performance of shape similarity retrieval methods. Comput. Vis. Image Underst. 84(1), 44–61 (2001)
Nasreddine, K., Benzinou, A., Fablet, R.: Variational shape matching for shape classification and retrieval. Pattern Recognit. Lett. 31(12), 1650–1657 (2010)
Chen, L., Feris, R.S., Turk, M.: Efficient partial shape matching using smith-waterman algorithm. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2008, Anchorage, AK, USA, 23–28 June, 2008, pp. 1–6, (2008)
Cui, M., Femiani, J., Hu, J., Wonka, P., Razdan, A.: Curve matching for open 2d curves. Pattern Recognit. Lett. 30(1), 1–10 (2009)
Leborgne, A., Mille, J., Tougne, L.: Hierarchical skeleton for shape matching. In: ICIP, pp. 3603–3607, (2016)
Proffitt, D.: The measurement of circularity and ellipticity on a digital grid. Pattern Recognit. 15(5), 383–387 (1982)
Peura, M., Iivarinent, J.: Efficiency of simple shape descriptors. In: Aspects of visual form processing, pp. 443–451, (1997)
Rosenfeld, A.: Compact figures in digital pictures. IEEE Trans. Syst. Man Cybern. 4, 221–223 (1974)
Haralick, R.: A measure for circularity of digital figures. IEEE Trans. Syst. Man Cybern. 4, 394–396 (1974)
Danielsson, P.E.: A new shape factor. Comput. Graph. Image Process. 7(2), 292–299 (1978)
Di Ruberto, C., Dempster, A.: Circularity measures based on mathematical morphology. Electron. Lett. 36(20), 1691–1693 (2000)
Proffitt, D.: The measurement of circularity and ellipticity on a digital grid. Pattern Recognit. 15, 383–387 (1982)
Rosin, P.: Measuring shape: ellipticity, rectangularity, and triangularity. Mach. Vis. Appl. 14, 172–184 (2003)
Zunic, J.D., Hirota, K., Rosin, P.L.: A Hu moment invariant as a shape circularity measure. Pattern Recognit. 43(1), 47–57 (2010)
Stojmenovic, A., Nayak, M.: Shape based circularity measures of planar point sets. In: ICSPC, pp. 1279–1282, (2007)
Ritter, N., Cooper, J.R.: New resolution independent measures of circularity. J. Math. Imaging Vis. 35(2), 117–127 (2009)
Fisk, S.: Separating point sets by circles, and the recognition of digital disks. IEEE Trans. Pattern Anal. Mach. Intell. 8(4), 554–556 (1986)
Roussillon, T., Sivignon, I., Tougne, L.: Measure of circularity for parts of digital boundaries and its fast computation. Pattern Recognit. 43(1), 37–46 (2010)
Nguyen, T.P., Debled-Rennesson, I.: Circularity measuring in linear time. In: ICPR, pp. 2098–2101, (2010)
Bribiesca, E.: An easy measure of compactness for 2d and 3d shapes. Pattern Recognit. 41(2), 543–554 (2008)
Nguyen, T.P., Hoang, T.V.: Projection-based polygonality measurement. IEEE Trans. Image Process. 24(1), 305–315 (2015)
Zunic, D., Martinez-Ortiz, C., Zunic, J.D.: Shape rectangularity measures. IJPRAI 26(6), 1–23 (2012)
Martinez-Ortiz, C., Zunic, J.D.: Curvature weighted gradient based shape orientation. Pattern Recognit. 43(9), 3035–3041 (2010)
Zunic, J.D.: Milosmenovic, boundary based shape orientation. Pattern Recognit. 41(5), 1768–1781 (2008)
Sanz, J.L.C., Dinstein, I.: Projection-based geometrical feature extraction for computer vision: algorithms in pipeline. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 160–168 (1987)
Leavers, V.F.: Use of the Radon transform as a method of extracting information about shape in two dimensions. Image Vis. Comput. 10(2), 99–107 (1992)
Jafari-Khouzani, K., Soltanian-Zadeh, H.: Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 1004–1008 (2005)
Baudrier, E., Tajine, M., Daurat, A.: Polygonal estimation of planar convex-set perimeter from its two projections. Discrete Appl. Math. 161(15), 2252–2268 (2013)
Hjouj, H., Kammler, D.W.: Identification of reflected, scaled, translated, and rotated objects from their Radon projections. IEEE Trans. Image Process. 17(3), 301–310 (2008)
Zunic, J.D., Rosin, P.L.: An alternative approach to computing shape orientation with an application to compound shapes. Int. J. Comput. Vis. 81(2), 138–154 (2009)
Yip, R.K.K.: Genetic Fourier descriptor for the detection of rotational symmetry. Image Vis. Comput. 25(2), 148–154 (2007)
Xiao, Z., Hou, Z., Miao, C., Wang, J.: Using phase information for symmetry detection. Pattern Recognit. Lett. 26(13), 1985–1994 (2005)
Tzimiropoulos, G., Mitianoudis, N., Stathaki, T.: A unifying approach to moment-based shape orientation and symmetry classification. IEEE Trans. Image Process. 18(1), 125–139 (2009)
Rosin, P.L., Pantovic, J., Zunic, J.D.: Measuring linearity of connected configurations of a finite number of 2d and 3d curves. J. Math. Imaging Vis. 53(1), 1–11 (2015)
Rosin, P.L., Pantovic, J., Zunic, J.D.: Measuring linearity of curves in 2d and 3d. Pattern Recognit. 49, 65–78 (2016)
Pal, S., Bhowmick, P.: Determining digital circularity using integer intervals. J. Math. Imaging Vis. 42(1), 1–24 (2012)
Ma, Z., Ma, J., Xiao, B., Lu, K.: A 3d polar-radius-moment invariant as a shape circularity measure. Neurocomputing 259, 140–145 (2017)
Misztal, K., Tabor, J.: Ellipticity and circularity measuring via Kullback–Leibler divergence. J. Math. Imaging Vis. 55(1), 136–150 (2016)
Kim, C.E., Anderson, T.A.: Digital disks and a digital compactness measure, In: STOC, pp. 117–124, (1984)
Rosin, P.L.: Measuring rectangularity. Mach. Vis. Appl. 11(4), 191–196 (1999)
Rosin, P.L., Zunic, J.D.: Measuring squareness and orientation of shapes. J. Math. Imaging Vis. 39(1), 13–27 (2011)
Sarkar, D.: A simple algorithm for detection of significant vertices for polygonal approximation of chain-coded curves. Pattern Recognit. Lett. 14(12), 959–964 (1993)
Rosin, P.L.: Techniques for assessing polygonal approximations of curves. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 659–666 (1997)
Tabbone, S., Terrades, O.R., Barrat, S.: Histogram of radon transform. A useful descriptor for shape retrieval. In: ICPR, pp. 1–4, (2008)
Deans, S.R.: The Radon Transform and Some of Its Applications. Krieger Publishing Company, Malabar (1983)
Brady, M.L.: A fast discrete approximation algorithm for the radon transform. SIAM J. Comput. 27(1), 107–119 (1998)
Kim, C.E., Anderson, T.A.: Digital disks and a digital compactness measure. In: Proceedings of STOC, pp. 117–124, (1984)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 550–571 (2004)
Sharvit, D., Chan, J., Tek, H., Kimia, B.B.: Symmetry-based indexing of image databases. J. Vis. Commun. Image Represent. 9, 366–380 (1998)
Rosenfeld, A.: Compact figures in digital pictures. IEEE Trans. Syst. Man Cybern. 2, 221–223 (1974)
Acknowledgements
We are grateful to two reviewers for kindly providing us their valuable and insightful comments to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
A Material for the Inverse Problem
A Material for the Inverse Problem
The following lemmas establish the relation between convex and non-convex shapes as well as the properties of a convex shape satisfying the conditions in Proposition 2.
Lemma 1
If an arbitrary shape \(\mathcal {D}\) satisfies this condition: \(\frac{1}{2}\varLambda ^\theta (\mathcal {D})=\varDelta ^\theta _1(\mathcal {D})=\varDelta ^\theta _2(\mathcal {D})=r, \forall \theta \in [0,\pi )\), its convex hull \(CV(\mathcal {D})\) satisfies the above condition too.
Proof
First, we will prove that any extremity \(E_1^\theta \), \(E_2^\theta \) is not in a concave part of the boundary of \(\mathcal {D}\) for any direction \(\theta \). Suppose that \(E_1^\theta \) is in concave part. It means that there exist 2 points A and B on the boundary of \(\mathcal {D}\) nearby \(E_1^\theta \) at the left and right sides such that \(E_1^\theta \) is inside of triangle \(E_2^\theta AB\) (see Fig. 18). So, \(\max (AE_2^\theta , BE_2^\theta )>E_1^\theta E_2^\theta =2r\). This fact is contradictory to the above condition of \(\mathcal {D}\). On the other hand, it is evident that \(\mathcal {D}\) and its convex hull \(CV(\mathcal {D})\) have a same projected band in any direction. From the two above facts, we can deduce the conclusion of this lemma. \(\square \)
Lemma 2
Let us denote the two extremities in the boundary of \(\mathcal {D}\) corresponding to \(\rho _0^\theta (\mathcal {D})\) as \(E_1^\theta \) and \(E_2^\theta \), respectively (see also Sect. 3.1.2 for more details). \(E_1^\theta E_2^\theta \) and \(E_1^{\theta +\frac{\pi }{2}} E_2^{\theta +\frac{\pi }{2}}\) intersect at the midpoint of each segment \(\forall \theta \in [0,\frac{\pi }{2})\).
Proof
Let us consider direction \(\theta \), we have the following condition: \(E_1^\theta E_2^\theta =2r\) because of \(\frac{1}{2}\varLambda ^\theta (\mathcal {D})=r\). Therefore, in direction \(\theta +\frac{\pi }{2}\), the length of \(\mathcal {D}\) is at least 2r and the minimal value is obtained when \(E_1^{\theta }\) and \(E_2^{\theta }\) must be on the two supporting lines of \(\mathcal {D}\) in this direction, respectively. In Fig. 19, these supporting lines, namely \(d_1\) and \(d_2\), are perpendicular with \(E_1^\theta E_2^\theta \) at \(E_1^\theta \) and \(E_2^\theta \), respectively. In addition, \(\varDelta ^{\theta +\frac{\pi }{2}}_1(\mathcal {D})+\varDelta ^{\theta +\frac{\pi }{2}}_2(\mathcal {D})=2r\). Therefore, in direction \(\theta +\frac{\pi }{2}\), the projected band of \(\mathcal {D}\) must be sandwiched between two lines \(d_1\) and \(d_2\) (see Fig. 19).
Because of \(\varDelta ^\theta _1(\mathcal {D})=\varDelta ^\theta _2(\mathcal {D})=r, \forall \theta \in [0,\pi )\), \(E_1^{\theta +\frac{\pi }{2}} E_2^{\theta +\frac{\pi }{2}}\) must be equidistant from \(d_1\) and \(d_2\). So, we have \(E_1^\theta O =E_2^\theta O\). Similarly, by considering direction \(\theta +\frac{\pi }{2}\), we obtain \(E_1^{\theta +\frac{\pi }{2}}O= E_2^{\theta +\frac{\pi }{2}}O\). \(\square \)
Lemma 3
Suppose that O (resp. \(O^\theta \)) is the intersection between \(E_1^0 E_2^0\) and \(E_1^{\frac{\pi }{2}} E_2^{\frac{\pi }{2}}\) (resp. \(E_1^\theta E_2^\theta \) and \(E_1^{\theta +\frac{\pi }{2}} E_2^{\theta +\frac{\pi }{2}}\)) (please see Sect. 3.1.2 for the definitions of \(E^\theta _1\) and \(E^\theta _2\)). We have the following property: \(OO^\theta \le \sqrt{2}r (\sqrt{5-4\cos (\theta )}-1) \)
Proof
Lemma 2 allows to deduce that the border of \(\mathcal {D}\) is decomposed into a set of tuples (\(E_1^\theta \), \(E_2^\theta \), \(E_1^{\theta +\frac{\pi }{2}}\), \(E_2^{\theta +\frac{\pi }{2}}\)) where the intersection \(O^\theta \) is the midpoints of two segments \(E_1^\theta E_2^\theta \), \(E_1^{\theta +\frac{\pi }{2}} E_2^{\theta +\frac{\pi }{2}}\). Suppose that \({\zeta }(O,r)\) is the circle of center O, of radius r. Without loss of generality, suppose that \(O^\theta \) is in the fourth octant (see Fig. 20). In addition, for the simplicity of presentation, we consider \(\theta \le \frac{\pi }{4}\) and the other cases can be considered similarly. We then denote \(O_1\) (resp. \(O_2\)) the projection of O on \(E_1^\theta E_2^\theta \) (resp. \(E_1^{\theta +\frac{\pi }{2}} E_2^{\theta +\frac{\pi }{2}}\)); \(X^\theta \) and \(\overline{E_1^\theta }\) (resp. \(X^{\theta +\frac{\pi }{2}}\) and \(\overline{E_1^{\theta +\frac{\pi }{2}}}\)) are the intersections between \(OE_1^\theta \) (resp. \(OE_1^{\theta +\frac{\pi }{2}}\)) and \({\zeta }(O,r)\) and \({\zeta }(E_2^0,2r)\) (resp. \({\zeta }(O,r)\) and \({\zeta }(E_2^{\frac{\pi }{2}},2r)\)).
In this condition, it is evident to deduce that \(E_1^\theta \) and \(E_1^{\theta +\frac{\pi }{2}}\) are outside or on the boundary of \({\zeta }(O,r)\). In addition, because the length of \(\mathcal {D}\) in any direction is a constant (2r), we deduce that \(E_2^0 E_1^\theta \le 2r\) and \(E_2^{\frac{\pi }{2}}E_1^{\theta +\frac{\pi }{2}}\le 2r\). Therefore, \(E_1^\theta \) (resp. \(E_1^{\theta +\frac{\pi }{2}}\)) must be in the small zone determined by 3 circles: \({\zeta }(O,r)\), \({\zeta }(E_2^0,2r)\), \({\zeta }(E_2^{\frac{\pi }{2}},2r)\) (resp. \({\zeta }(O,r)\), \({\zeta }(E_1^0,2r)\), \({\zeta }(E_2^{\frac{\pi }{2}},2r)\)) (see also Fig. 20). So, we have:
Moreover, because \(O_1\) and \(O_2\) are projections of O, it is evident that \(E_1^\theta O_1 \le E_1^\theta O\)\(\Leftrightarrow E_1^\theta O^\theta + O^\theta O_1 \le E_1^\theta X^\theta +X^\theta O\)\(\Leftrightarrow \)\(r+O^\theta O_1\le E_1^\theta X^\theta +r \Leftrightarrow O^\theta O_1\le E_1^\theta X^\theta \) and \(E_1^{\theta +\frac{\pi }{2}} O_2 \le E_1^{\theta +\frac{\pi }{2}} O\)\(\Leftrightarrow E_1^{\theta +\frac{\pi }{2}} O^\theta + O^\theta O_2 \le E_1^{\theta +\frac{\pi }{2}} X^{\theta +\frac{\pi }{2}} +X^{\theta +\frac{\pi }{2}} O\)\(\Leftrightarrow \)\(r+O^\theta O_2\le E_1^{\theta +\frac{\pi }{2}} X^{\theta +\frac{\pi }{2}} +r \Leftrightarrow O^\theta O_2\le E_1^{\theta +\frac{\pi }{2}} X^{\theta +\frac{\pi }{2}}\). Briefly, we have:
Consider now the triangle \(\overline{E_1^\theta } O E_2^0\), we have \(\overline{E_1^\theta } O{=}\sqrt{{|\overline{E_1^\theta } E_2^0|}^2{+}{|O E_2^0|}^2{-}2|\overline{E_1^\theta } E_2^0| |O E_2^0| \cos (\angle \overline{E_1^\theta } E_2^0 O) }\)\(\Leftrightarrow \overline{E_1^\theta }X^\theta {+}r {=}\sqrt{5r^2{-}4r^2\cos (\angle \overline{E_1^\theta } E_2^0 O)}\)\(\Leftrightarrow \overline{E_1^\theta }X^\theta {=}r(\sqrt{5{-}4\cos (\angle \overline{E_1^\theta } E_2^0 O)}{-}1)\). Because of \(\angle \overline{E_1^\theta } E_2^0 O \le \theta \), we deduce that \(\overline{E_1^\theta }X^\theta \le r(\sqrt{5{-}4\cos (\theta )}{-}1)\). Similarly, by considering the triangle \(\overline{E_1^{\theta {+}\frac{\pi }{2}}} O E_2^{\frac{\pi }{2}}\), the following remarks are obtained.
Using the results in Eqs. (11), (12), (13), we have the conclusion of this lemma: \(OO^\theta =\sqrt{|OO_1|^2+|OO_2|^2} \le \sqrt{2}r(\sqrt{5-4\cos (\theta )}-1)\). \(\square \)
Lemma 4
\(\lim \limits _{n \rightarrow +\infty } n (\sqrt{5-4\cos (\frac{\pi }{4n})}-1)=0\)
Proof
Due to Taylor’s series, we have \(1-\frac{x^2}{2}\le cos(x) \le 1\); therefore, \(1\le 5-4\cos (x)\le 1+2x^2\le (1+x^2)^2\). So, we have \(0\le \sqrt{5-4\cos (x)} -1\le x^2\). Replacing x by \(\frac{\pi }{4n}\), we obtain the following result \(0\le n (\sqrt{5-4\cos (\frac{\pi }{4n})}-1)\le \frac{\pi ^2}{16n}\). It is evident that \(\lim \limits _{n \rightarrow +\infty }\frac{\pi ^2}{16n}=0\). This fact proves our conclusion. \(\square \)
Rights and permissions
About this article
Cite this article
Nguyen, T.P., Nguyen, X.S., Borgi, M.A. et al. A Projection-Based Method for Shape Measurement. J Math Imaging Vis 62, 489–504 (2020). https://doi.org/10.1007/s10851-019-00932-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10851-019-00932-w