Exact medial axis with euclidean distance

Rémy, É.; Thiel, Edouard

doi:10.1016/j.imavis.2004.06.007

Cited by 54 publications

(52 citation statements)

References 15 publications

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“…Implement a new "exact medial axis" algorithm (Couprie et al 2007, Saúde et al 2006) for creating a 3-D skeleton, based on Remy and Thiel (2005). This will avoid having to choose along which axis a skeletal slice-stack should be constructed, avoid skeletal artifacts such as those arising from folds lying along a slice, increase accuracy and reduce inconsistencies across acquisitions and orientations, and result in much faster processing times.…”

Section: Student Projectmentioning

confidence: 99%

Collection of informatics proposals from 2007

Klein¹

2016

RIO

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Section: Student Projectmentioning

confidence: 99%

Collection of informatics proposals from 2007

Klein¹

2016

RIO

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“…There are many algorithms for target symmetry axes extraction, such as the Euclidean distance algorithm (Remy & Thiel, 2005), neural network algorithm (Fukushima & Kikuchi, 2006), minimal inertia algorithm (Gong et al, 2001), etc. Among these, both the Euclidean distance algorithm and neural network algorithm are more applicable to complex images and are more complicated with regard to image processing, while the moment of inertia method has the characteristic of a simple operation.…”

Section: Location Of An Apple's Picking Pointmentioning

confidence: 99%

An improved contour symmetry axes extraction algorithm and its application in the location of picking points of apples

Wang

Song

et al. 2015

Span. j. agric. res.

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The key problem for picking robots is to locate the picking points of fruit. A method based on the moment of inertia and symmetry of apples is proposed in this paper to locate the picking points of apples. Image pre-processing procedures, which are crucial to improving the accuracy of the location, were carried out to remove noise and smooth the edges of apples. The moment of inertia method has the disadvantage of high computational complexity, which should be solved, so convex hull was used to improve this problem. To verify the validity of this algorithm, a test was conducted using four types of apple images containing 107 apple targets. These images were single and unblocked apple images, single and blocked apple images, images containing adjacent apples, and apples in panoramas. The root mean square error values of these four types of apple images were 6.3, 15.0, 21.6 and 18.4, respectively, and the average location errors were 4.9°, 10.2°, 16.3° and 13.8°, respectively. Furthermore, the improved algorithm was effective in terms of average runtime, with 3.7 ms and 9.2 ms for single and unblocked and single and blocked apple images, respectively. For the other two types of apple images, the runtime was determined by the number of apples and blocked apples contained in the images. The results showed that the improved algorithm could extract symmetry axes and locate the picking points of apples more efficiently. In conclusion, the improved algorithm is feasible for extracting symmetry axes and locating the picking points of apples. Additional key words: picking robot; fruit picking point; symmetry axes extraction; moment of inertia; convex hull

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“…Algorithms to compute both LUT and T (R) are given by Rémy and Thiel in arbitrary dimension for chamfer norms and SED [9,10], with code available in dimensions 2 to 6 in [11]. In recent papers, Normand andÉvenou have proposed a faster method for chamfer norms in 2D and 3D based on a polytope representation of chamfer balls [12,13].…”

Section: Introductionmentioning

confidence: 99%