Techniques for Improving Color Segmentation in the Task of Identifying Objects on Aerial Images

Kasimov, Denis R.

doi:10.23919/fruct.2019.8711891

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…It has been used in several studies on various fields to detect objects for example in detecting skin colour, object from an aerial image as well as detecting any object in chessboard. [27][28] [29] An RGB colour space is a mixture of three premier colours which are red, green, and blue additive while Lab colour space is derived from the CIE XYZ tristimulus values. The Lab space consists of L as the lightness or brightness layer while a and b are the direction of an along the red-green axis, and layer 'b' along the blue-yellow axis.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Composite Defect and Segmentation in RGB and Lab Colour Space

Anwar¹

2019

IJATCSE

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Composite material has the advantage where it has been used in manufacturing electric vehicle and aerospace components due to the stiffness, hardness and lightness of the material. However, because of the anisotropy of the composite material, the inspection of material is still become a challenge. Active thermography has been applied as one of non-destructive tools to test, assess and identify composite fibre defects. In this project a low-resolution infrared camera is used to measure composite defect using active thermography method. The infrared images taken were then converted into RGB and Lab Colour Space. Image filtering and enhancement were then utilized and finally using circle detection algorithm to determine the number of defects on composite material. A comparison was done on segmentation between RGB and Lab colour spaces and determine which colour space that could produce optimum result by identifying the maximum total of actual defects. Simulation depth defect of a glass fibre reinforced polymer (CFRP) with different depth and diameter of defects is used as the sample testing.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Composite Defect and Segmentation in RGB and Lab Colour Space

Anwar¹

2019

IJATCSE

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Principles of Organization of the Strategies of Content-Based Analysis of Aerospace Images

Kasimov

Kuchuganov

2020

Advances in Intelligent Systems and Computing

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Methods and Tools for Developing Decision Rules for Classifying Objects in Aerial Images

Kasimov

Kuchuganov

2020

2020 26th Conference of Open Innovations Association (FRUCT)

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Techniques for Improving Color Segmentation in the Task of Identifying Objects on Aerial Images

Cited by 3 publications

References 18 publications

Comparative Study of Composite Defect and Segmentation in RGB and Lab Colour Space

Comparative Study of Composite Defect and Segmentation in RGB and Lab Colour Space

Principles of Organization of the Strategies of Content-Based Analysis of Aerospace Images

Methods and Tools for Developing Decision Rules for Classifying Objects in Aerial Images

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