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Diffstat (limited to 'doc/dui.md')
| -rw-r--r-- | doc/dui.md | 11 |
1 files changed, 5 insertions, 6 deletions
@@ -415,7 +415,10 @@ fB(i) = max(0, min(iB − iR, iB − iG)/s), fY(i) = max(0, min(iR − iB, iG − iB)/s). ``` -This method can result in some issues on the blue channel (see source 1 page 86583 for more explanation). As a solution to this issue use the following formula for the blue channel instead: +This method can result in some issues on the blue channel +\parencite[86583]{ieee:sign-detection}. As a solution to this issue use the +following formula for the blue channel instead: + ```py f′B(i) = max((0, iB − iR)/s). ``` @@ -449,7 +452,7 @@ This color space is used for finding uncorrelated color components, the L\*a\*b\ This method avoids the use of fixed thresholds that might need adjusting at times. In order to resolve this some authors tried to transfer the problem into pixel classification where a neural network classifies every pixel in the input image, the pixel classification algorithms are often slower than other color extraction methods. -#### results +#### Results \def\signDetectionColor{ The above described methods where also applied to a database in order to compare each method. This resulted in the conclusion that, using a normalized RGB space is giving the a mixture of most detections and least false-positve results. See source 1 page 86584 for the full report. @@ -516,7 +519,3 @@ While making a binary tree is seemingly the most simple, yet effective solution. \signDetectionShape \signRecognition -## Sources: - -1. [IEEE, Digital Object Identifier June 26, 2019 (pages 86578 - 86596)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8746141) - |