Curve and boundaries measurement using B-splines and virtual images

Abstract

This paper presents a digital image correlation method able to detect and measure a contour or silhouette in a digital image, with a sub-pixel precision. A virtual image that describes a simple contour or silhouette roughly similar to the physical object to detect is created. The matching with the physical image of the object is then performed based on digital image correlation. We look for the displacement field which transforms the physical image which is the closest to the virtual one. In order to keep a problem of finite dimension, this field is decomposed on a B-spline basis. The algorithm is close to the digital image correlation method; an efficient implementation is proposed that makes use of levelsets. The full method is tested over both synthesized and real images, one of a star shaped toy and one on an ill-defined image of a high temperature metallic specimen on which no other boundary measurement was possible. The influence of the arbitrary choice of the virtual image contour or silhouette arbitrary parameters is discussed with respect to the precision of the method. This one is shown to be sub-pixel in any case.

Validation of a multimodal set‐up for the study of zirconium alloys claddings' behaviour under simulated LOCA conditions

“…The edge detection method is based on the analysis of the grey level gradient of an image. Recent improvements allow for a subpixel definition of a curvilinear shape position and low CPU time calculations . The reference line (i.e., edge before loading) was defined between −8 mm and + 8 mm of the eulerian variable Z (see Figure c).…”

“…Recent improvements allow for a subpixel definition of a curvilinear shape position and low CPU time calculations. [12] The reference line (i.e., edge before loading) was defined between −8 mm and + 8 mm of the eulerian variable Z (see Figure 5c). It was discretized by 30 elements associated with a B-spline shape functions basis.…”

“…Since the 1990s, non‐contact techniques in both kinematic and thermal fields measurements have been improved. Digital image correlation and edge detection are frequently used for strain measurements in experimental mechanics. Recent improvements in algorithms (global approaches with finite element basis, multigrid solver, integrated‐DIC, FEMU) allow for material behaviour identification as proposed by Réthoré et al…”

“…[3][4][5][6][7][8][9] Since the 1990s, non-contact techniques in both kinematic and thermal fields measurements have been improved. Digital image correlation [10,11] and edge detection [12] are frequently used for strain measurements in experimental mechanics. Recent improvements in algorithms (global approaches with finite element basis, [13] multigrid solver, [14] integrated-DIC, [15] FEMU [16] ) allow for material behaviour identification as proposed by Réthoré et al [17] Infrared (IR) cameras are now commonly used in the field of experimental mechanics for computing thermal fields as demonstrated in Maynadier et al [18] with infrared image correlation or in Chrysochoos et al [19] to explore the energy balance associated with high-cycle fatigue.…”

B‐spline Based Multi‐organ Detection in Magnetic Resonance Imaging

“…The intensity profile of the virtual image is defined by a levelset function : v ∈ ℝ → ℝ where v is the distance from a position X to the contour (Equation ). In , the choice of the levelset intensity was discussed; a bell‐shaped function was used to approach the intensity profile in the real image. The choice of sinusoidal function is kept in this paper for its simplicity, and the consistency is proved (Figure ).…”