A depth optimization method for 2D-to-3D conversion based on RGB-D images

Chen, Hua; Liu, Tao; Ge, Ming; Zhuang, Bojin

doi:10.1109/icnidc.2014.7000298

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…They collected data from 40 patients, and attained an accuracy of 90%. In Reference [ 8 ], the authors proposed a depth optimization method for 2D-to-3D conversion based on RGB-D images. These measurements can aid a great deal in keratoconus correction procedures.…”

Section: Background and Literature Surveymentioning

confidence: 99%

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Mahmoud

Mengash

2021

Sensors

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This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shaped cornea. Early diagnosis can decrease the risk of eyesight loss. Our aim is to create a method of fully automated keratoconus detection using digital-camera frontal and lateral eye images. The presented technique accurately determines case severity. Geometric features are extracted from 2D images to estimate depth information used to build 3D images of the cornea. The proposed methodology is easy to implement and time-efficient. 2D images of the eyes (frontal and lateral) are used as input, and 3D images from which the curvature of the cornea can be detected are produced as output. Our method involves two main steps: feature extraction and depth calculation. Machine learning from the 3D images dataset Dataverse, specifically taken by the Cornea/Anterior Segment OCT SS-1000 (CASIA), was performed. Results show that the method diagnosed the four stages of keratoconus (severe, moderate, mild, and normal) with an accuracy of 97.8%, as compared to manual diagnosis done by medical experts.

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Section: Background and Literature Surveymentioning

confidence: 99%

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Mahmoud

Mengash

2021

Sensors

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“…Hua et al in [18] proposed a depth image optimization method to improve the quality and minimize errors and noise. The input is given as RGB-D image which provides color information and original depth information.…”

Section: Conversion Based On Rgb-d Imagesmentioning

confidence: 99%

Review on 2D-to-3D Image and Video Conversion Methods

Patil¹,

Charles²

2015

2015 International Conference on Computing Communication Control and Automation

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We present a review paper on state of the art methods of 2D to 3D image and/or video conversion. In this modern era popularity of 3D hardware is increased but, 3D contents are still dominated by its 2D counterpart. Until now many researchers have proposed different methods to close this gap. Mainly, these conversion methods are categorized in an automatic method and semi-automatic method. In automatic method human intervention is not involved, where as in semi-automatic method human operator is involved. There are distinct attributes that can be considered during conversion, like for video conversion motion and optical flow are mostly considered parameter; while for image conversion local attributes of images are considered. Computational time and design cost are the main design metrics that should be considered while designing algorithm. Each method is having its own pros and cons, depending on specification which method will be suitable for conversion is decided.

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A fuzzy integrated genetic method for information persistent 3D to 2D graph transformation

Malik

Choudhary

2016

2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC)

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A depth optimization method for 2D-to-3D conversion based on RGB-D images

Cited by 3 publications

References 22 publications

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

Review on 2D-to-3D Image and Video Conversion Methods

A fuzzy integrated genetic method for information persistent 3D to 2D graph transformation

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