A hybrid defocused region segmentation approach using image matting

Amin, Benish; Riaz, Muhammad Mohsin; Ghafoor, Abdul

doi:10.1007/s11045-018-0570-8

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Köhler et al [22] proposed to separate motion blurred foreground through explicit modeling of object motion. Amin et al [4] applied image matting to segment out-of-focus regions. Yeung et al [44] proposed attenuation-refraction matte to mask out and re-composite transparent object with refraction consideration.…”

Section: Related Workmentioning

confidence: 99%

Semantic Image Matting

Sun¹,

Tang²,

Tai³

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Section: Related Workmentioning

confidence: 99%

Semantic Image Matting

Sun¹,

Tang²,

Tai³

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Amin, Riaz and Ghafoor 11 introduced the hybrid defocused region segmentation technique that uses slope of the magnitude spectrum, total variation and local binary patterns as a measure to produce the sharpness maps. This helps to distinguish between the blurred region and in‐focus region of the image.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel solution of using mixed reality in bowel and oral and maxillofacial surgical telepresence: 3D mean value cloning algorithm

Maharjan

Alsadoon

Prasad

et al. 2021

Robotics Computer Surgery

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Background and Aim Most of the mixed reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial–temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. Methodology The proposed system enhanced mean‐value cloning algorithm that helps to maintain the spatial–temporal consistency of the final composite video. The enhanced algorithm includes the three‐dimensional mean‐value coordinates and improvised mean‐value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region. Results The accuracy in terms of overlay error of the proposed solution is improved from 1.01 to 0.80 mm, whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 s from 0.211 s. The processing time and the accuracy of the proposed solution are enhanced as compared to the state‐of‐art solution. Conclusion Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video.

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“…Alpha matting is the technique to separate the foreground object from image, that is used in various applications, e.g. to extract objects accurately from images and videos [42, 43], to segment the defoused region [44] and to fuse multi‐focus images [45] etc. The purpose of matting is to extract the foreground by finding the opacity value