Discrete Curvature Flows for Surfaces and 3-Manifolds

Estimation of curvature on digital curves plays a critical role for various applications in the field of image processing and computer vision. The refined L-curvature has proven to be an efficient measure of discrete curvature for corner detection and curve matching. In this paper its stability under rotation transformations is evaluated. Experiments with comparisons to other existing curvature measures show that the refined L-curvature performs best.

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Fingerprint reference point identification based on chain encoded discrete curvature and bending energy

Pattern Analysis and Applications ◽

10.1007/s10044-016-0560-0 ◽

2016 ◽

Vol 20 (1) ◽

pp. 253-267 ◽

Cited By ~ 2

Author(s):

Geevar C. Zacharias ◽

Madhu S. Nair ◽

P. Sojan Lal

Keyword(s):

Reference Point ◽

Bending Energy ◽

Discrete Curvature

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Mesh Processing for Computerized Facial Anthropometry

Journal of Computing and Information Science in Engineering ◽

10.1115/1.3330420 ◽

2010 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Dhanannjay Deo ◽

Dibakar Sen

Keyword(s):

Critical Points ◽

Sampling Technique ◽

Facial Feature ◽

Morphological Operations ◽

Discrete Curvature ◽

Physical Measurements ◽

The Face ◽

Using Data ◽

Noise Tolerant ◽

Facial Anthropometry

Understanding of the shape and size of different features of the human body from scanned data is necessary for automated design and evaluation of product ergonomics. In this paper, a computational framework is presented for automatic detection and recognition of important facial feature regions, from scanned head and shoulder polyhedral models. A noise tolerant methodology is proposed using discrete curvature computations, band-pass filtering, and morphological operations for isolation of the primary feature regions of the face, namely, the eyes, nose, and mouth. Spatial disposition of the critical points of these isolated feature regions is analyzed for the recognition of these critical points as the standard landmarks associated with the primary facial features. A number of clinically identified landmarks lie on the facial midline. An efficient algorithm for detection and processing of the midline, using a point sampling technique, is also presented. The results obtained using data of more than 20 subjects are verified through visualization and physical measurements. A color based and triangle skewness based schemes for isolation of geometrically nonprominent features and ear region are also presented.

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