Morphological Image Analysis for Computer Vision Applications

2014 ◽  
pp. 9-58 ◽  
Author(s):  
Y.V. Vizilter ◽  
Y.P. Pyt’ev ◽  
A.I. Chulichkov ◽  
L. M. Mestetskiy
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Morphological Image Analysis ◽  
Computer Vision Applications ◽  
Morphological Image

scholarly journals Integral-geometry morphological image analysis

2001 ◽  
Vol 347 (6) ◽  
pp. 461-538 ◽  
Author(s):  
K. Michielsen ◽  
H. De Raedt
Keyword(s):  
Image Analysis ◽  
Integral Geometry ◽  
Morphological Image Analysis ◽  
Morphological Image

Curve Parameterization and Curvature via Method of Hurwitz-Radon Matrices

2011 ◽  
Vol 16 (1-2) ◽  
pp. 49-56 ◽  
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Object Recognition ◽  
Explicit Form ◽  
Local Maximum ◽  
Parametric Representation ◽  
Feature Points ◽  
Implicit Representation ◽  
Computer Vision Applications ◽  
Interpolation Nodes

Curve Parameterization and Curvature via Method of Hurwitz-Radon MatricesParametric representation of the curve is more appropriate in computer vision applications then explicit formy=f(x)or implicit representationf(x, y) = 0. Proposed method of Hurwitz-Radon Matrices (MHR) can be used in parameterization and interpolation of curves in the plane. Suitable parameterization leads to curvature calculations. Points with local maximum curvature are treated as feature points in object recognition and image analysis. This paper contains the way of curve parameterization and computing the curvature in the range of two successive interpolation nodes via MHR method. Proposed method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skew-symmetric and possess columns composed of orthogonal vectors. The operator of Hurwitz-Radon (OHR), built from these matrices, is described. It is shown how to create the orthogonal OHR and how to use it in a process of curve parameterization and curvature calculation.

scholarly journals IMAGE AND SHAPE COMPARISON VIA MORPHOLOGICAL CORRELATION

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 207-211
Author(s):  
Y. V. Vizilter ◽  
S. Y. Zheltov ◽  
M. A. Lebedev
Keyword(s):  
Image Analysis ◽  
Correlation Coefficient ◽  
Image Matching ◽  
Shape Matching ◽  
Image Correlation ◽  
Shape Comparison ◽  
Shape Models ◽  
Morphological Image Analysis ◽  
Morphological Image ◽  
Morphological Correlation

Abstract. A lot of image matching applications require image comparison to be invariant relative to intensity values variations. The Pyt’ev theory for Morphological Image Analysis (MIA) was developed based on image-to-shape matching with mosaic shape models. Within the framework of this theory, the problem of shape-to-shape comparison was previously considered too. The most sophisticated and weakest part of MIA theory is the comparison of mosaic shapes with some arbitrary restrictions described by graphs or relations. In this paper we consider the possible options for comparing images and shapes using morphological projection and morphological correlation. Our contribution is a new scheme of morphological shape-to-image projection and, correspondingly, the new morphological correlation coefficient (MCC) for shape-to-image correlation with restricted mosaic models. We also refine the expressions for shape-to-shape comparison.

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