2martens/uni
1
0
Fork 0
mirror of https://github.com/2martens/uni.git synced 2026-05-06 11:26:25 +02:00
Code Releases Activity

[Masterproj] Added introduction

Browse Source 
Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2018-05-30 12:12:35 +02:00
parent 1d8e4fd062
commit b329548d6b
2 changed files with 34 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
masterproj/seminar_report.tex
View File

@ -95,14 +95,29 @@ The short abstract (100-150 words) is intended to give the reader an overview of
\clearpage
\section{Introduction}
Use this template as a starting point for preparing your seminar report.
For more information on \LaTeX, please consult, e.g., the online book at \url{https://en.wikibooks.org/wiki/LaTeX}.
Refer also to material on scientific writing.
The length of the report should not exceed 10 pages (excluding the reference list).
This part contains the introduction to the topic.
It introduces the general problem area of the paper, and leads the reader to the next section that provides more details.
This part should also cite other related work (not only the seminar paper you are working on) and compare the approaches on a high level.
Object detection is a central task in the field of neural networks. It is a
combination of classification and localization tasks and aims to classify
and locate objects inside an image. It may be restricted to certain classes
that indicate objects of interest so that not every stone or leaf of a tree
is detected as an object. The output of object detection networks is usually
a collection of bounding boxes, one for each detected object, and the corresponding
classifications.
The area of 2D object detection has matured over many years. Single Shot Multibox
Detector\cite{Liu2016} uses a convolutional neural network (CNN) and the RGB
data of an image to detect objects. The result is a 2D bounding box and the
classification for each object.
With increasing availability of depth cameras, images gain the depth component
and approaches utilizing the depth are becoming more relevant. Depth RCNN\cite{Gupta2015}
uses the depth as a fourth channel of a 2D image. After the bounding box
is calculated they fit a 3D model to the points within the bounding box.
Deep Sliding Shapes\cite{Song2016} is utilizing the depth for actual 3D deep
learning but also uses the RGB channels of an RGB-D image to benefit from the
strength of 2D object detectors. The results of both the 3D and 2D parts are
combined and the result is a 3D bounding box and classification.
\section{Method description}
% This section describes the proposed approach in the paper in more detail.