From bb0972183bd568a9e45d447e122d0fba9a587ba9 Mon Sep 17 00:00:00 2001
From: lonkaars <loek@pipeframe.xyz>
Date: Mon, 22 May 2023 12:18:05 +0200
Subject: use biblatex for references (joshua)

---
 doc/base.tex |  2 +-
 doc/dui.md   | 11 +++++------
 doc/refs.bib |  7 +++++++
 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}
+}
-- 
cgit v1.2.3