Intelligent scissors for image composition

Mortensen, Eric N.; Barrett, William A.

doi:10.1145/218380.218442

Cited by 650 publications

(396 citation statements)

References 16 publications

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“…Binary masks used to assign Fresnel phases in the CDI reconstructions were generated using the intelligent scissors algorithm [47].…”

Section: Height Mapsmentioning

confidence: 99%

“…) were generated from masks (a) and (c) by excluding points further than one standard deviation from the mean reflectivity of the included areas. Masks (a) and (c) were made using the intelligent scissors algorithm [47]. Masks (b) and (d) were used to scale the multicolor reconstructions so that their average reflectivity matches the RAPTR-CDI reconstructions.…”

Section: E Ptychography Reconstructionsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Chemically Specific Coherent Diffractive Imaging of Reactions at Buried Interfaces with Few Nanometer Precision

et al. 2016

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Characterizing buried layers and interfaces is critical for a host of applications in nanoscience and nano-manufacturing. Here we demonstrate non-invasive, non-destructive imaging of buried interfaces using a tabletop, extreme ultraviolet (EUV), coherent diffractive imaging (CDI) nanoscope. Copper nanostructures inlaid in SiO2 are coated with 100 nm of aluminum, which is opaque to visible light and thick enough that neither optical microscopy nor atomic force microscopy can image the buried interfaces. Short wavelength (29 nm) high harmonic light can penetrate the aluminum layer, yielding high-contrast images of the buried structures. Moreover, differences in the absolute reflectivity of the interfaces before and after coating reveal the formation of interstitial diffusion and oxidation layers at the Al-Cu and Al-SiO2 boundaries. Finally, we show that EUV CDI provides a unique capability for quantitative, chemically-specific imaging of buried structures, and the material evolution that occurs at these buried interfaces, compared with all other approaches.

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“…Binary masks used to assign Fresnel phases in the CDI reconstructions were generated using the intelligent scissors algorithm [47].…”

Section: Height Mapsmentioning

confidence: 99%

Section: E Ptychography Reconstructionsmentioning

confidence: 99%

Quantitative Chemically Specific Coherent Diffractive Imaging of Reactions at Buried Interfaces with Few Nanometer Precision

et al. 2016

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“…Snakes (Kass et al, 1988), active contour models, based on energy minimization. Intelligent Scissors (Mortensen et al, 1995) take the boundary detect as graph searching problem which is to find the minimum cumulative cost path between a start pixel and a goal pixel. Geometric active contours (Caselles et al, 1997) based on active contours evolving.…”

Section: Related Workmentioning

confidence: 99%

SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images

Cui¹,

Wang²,

Feng³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT) descriptor and the histogram of oriented gradients (HOG) & HSV descriptor to characterize the spatial structure and color information of the targets. With the kmeans cluster method, we get the bag of visual words, and then, we adopt three levels' spatial pyramid (SP) to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.

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“…15 Y Computer-assisted. Feature extraction is partly interactive, e.g., shapes segmented with the "livewire" algorithm, 16 which completes shape segmentation, such as for vertebrae on spine X-ray images, based on a few user-supplied "guiding points"; another example is interactive region segmentation on histology images by regiongrowing or K-means clustering algorithms. 17 Y Automatic.…”

Section: Extraction Gapmentioning

confidence: 99%