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-rw-r--r--doc/base.tex2
-rw-r--r--doc/dui.md11
-rw-r--r--doc/refs.bib7
3 files changed, 13 insertions, 7 deletions
diff --git a/doc/base.tex b/doc/base.tex
index d51d0c4..e8bc8f1 100644
--- a/doc/base.tex
+++ b/doc/base.tex
@@ -66,7 +66,7 @@
\input{\jobname.md.tex}
-% \printbibliography[heading=bibintoc]
+\printbibliography[heading=bibintoc]
% \printglossaries
% \listoftables
% \listoffigures
diff --git a/doc/dui.md b/doc/dui.md
index 5cf6630..22d74a6 100644
--- a/doc/dui.md
+++ b/doc/dui.md
@@ -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)
-
diff --git a/doc/refs.bib b/doc/refs.bib
index e69de29..7ae5f95 100644
--- a/doc/refs.bib
+++ b/doc/refs.bib
@@ -0,0 +1,7 @@
+@article{ieee:sign-detection,
+ author = {Chunsheng Liu and Shuang Li and Faliang Chang and Yinhai Wang},
+ title = {Machine Vision Based Traffic Sign Detection Methods: Review,
+ Analyses and Perspectives},
+ year = {2019},
+ url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8746141}
+}