Surface Detection Using Round Cut

Dahl, Vedrana Andersen; Dahl, Anders Bjorholm; Larsen, Rasmus

doi:10.1109/3dv.2014.60

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…In this paper, we enforced a star-like prior for the C-SLGs by using radial resampling. However, we believe there is a large potential in using C-SLGs with sampling schemes based on other priors [7,9,17], or by sampling based on surfaces in 3D [3,15].…”

Section: Discussionmentioning

confidence: 99%

Sparse Layered Graphs for Multi-Object Segmentation

Jeppesen

Christensen

Dahl

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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We introduce the novel concept of a Sparse Layered Graph (SLG) for s-t graph cut segmentation of image data. The concept is based on the widely used Ishikawa layered technique for multi-object segmentation, which allows explicit object interactions, such as containment and exclusion with margins. However, the spatial complexity of the Ishikawa technique limits its use for many segmentation problems. To solve this issue, we formulate a general method for adding containment and exclusion interaction constraints to layered graphs. Given some prior knowledge, we can create a SLG, which is often orders of magnitude smaller than traditional Ishikawa graphs, with identical segmentation results. This allows us to solve many problems that could previously not be solved using general graph cut algorithms. We then propose three algorithms for further reducing the spatial complexity of SLGs, by using ordered multi-column graphs. In our experiments, we show that SLGs, and in particular ordered multi-column SLGs, can produce high-quality segmentation results using extremely simple data terms. We also show the scalability of ordered multi-column SLGs, by segmenting a highresolution volume with several hundred interacting objects.

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Section: Discussionmentioning

confidence: 99%

Sparse Layered Graphs for Multi-Object Segmentation

Jeppesen

Christensen

Dahl

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

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“…We will first review an algorithm for finding optimal layered surfaces, with the focus on the inputs and the outputs. For details on how this algorithm works, the reader is referred to [11], [12], [19]. We also briefly cover the principle of transforming the data into volumetric cost, which is the input to the layered surface detection algorithm.…”

Section: Methodsmentioning

confidence: 99%

Layered Surface Detection for Virtual Unrolling

Dahl

Trinderup

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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We present a method for virtual unrolling of a thin rolled object. From a volumetric image of the rolled object we obtain a flat image of the object's surface, which allows visual inspection of the object and has a number of applications. Our method exploits the geometric constrains of the problem and detects a single rolled surface. For surface detection we adapt a solution to an optimal net surface problem, previously used for terrain-like and tubular surfaces. We present our approach on an example of a rolled sheet of microelectronic, which has a layer of flexible polymer substrate and a thin metal layer lithographically coated onto the polymer. Our approach is automatic and robust. The unrolled image is undistorted, and the surface structures may be accurately quantified making our approach a good candidate for an industrial application of virtual unrolling.

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“…The detailed expressions of those forces, our simulation method, and a table of parameters used to match experimental conditions can be found in Materials and Methods. Each of our simulations begins with an initially spherical droplet formed by vertices on an icosphere (33) where particles are spaced approximately 2r p apart and equilibrated with no applied magnetic force over 0.25 s to allow for elastic deformation due to gravitation. For convenience, we make the droplet heavier than the medium so it settles and rolls on the bottom substrate.…”

Section: Numerical Simulationmentioning

confidence: 99%

Rolling of soft microbots with tunable traction

et al. 2023

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Microbot (μbot)–based targeted drug delivery has attracted increasing attention due to its potential for avoiding side effects associated with systemic delivery. To date, most μbots are rigid. When rolling on surfaces, they exhibit substantial slip due to the liquid lubrication layer. Here, we introduce magnetically controlled soft rollers based on Pickering emulsions that, because of their intrinsic deformability, fundamentally change the nature of the lubrication layer and roll like deflated tires. With a large contact area between μbot and wall, soft μbots exhibit tractions higher than their rigid counterparts, results that we support with both theory and simulation. Upon changing the external field, surface particles can be reconfigured, strongly influencing both the translation speed and traction. These μbots can also be destabilized upon pH changes and used to deliver their contents to a desired location, overcoming the limitations of low translation efficiency and drug loading capacity associated with rigid structures.

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Surface Detection Using Round Cut

Cited by 6 publications

References 22 publications

Sparse Layered Graphs for Multi-Object Segmentation

Sparse Layered Graphs for Multi-Object Segmentation

Layered Surface Detection for Virtual Unrolling

Rolling of soft microbots with tunable traction

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