A project performed as a part of the Computer Vision (TSBB15) course at Linköping University. The purpose of the project is to implement a tracker capable of handling occlusion, shadows and changes in the background. Object tracking is, in computer vision, defined as the process where moving objects are located in an image sequence. The project goal was to create a real-time tracker to work on e.g. a surveillance camera and track the people and objects in its view. The application was implemented in C++ using the OpenCV library.
https://github.com/Epipolarna/Tracking
Linköping University has several student kitchens all over its campuses where students are given a possibility to warm their food. Critics claim that there are too few student kitchens and that the existing ones are usually overcrowded. That all kitchens are overcrowded at the same time has not been confirmed by sample inspections. One standing hypothesis is that students do not know where all the kitchens are, nor do they want to risk going to a kitchen in another building in case it is full as well.
The aim of this project is to develop a system that will provide the students with information regarding student kitchen usage. The system uses an computer vision approach, estimating the number of people currently using the kitchens. The system was developed using C++, the OpenCV library and the Qt5 library.
https://github.com/GroupDenseKitchen/KitchenOccupation
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-mangen
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Mangen, Product Manager for Camera and Computer Vision at Qualcomm, presents the "High-resolution 3D Reconstruction on a Mobile Processor" tutorial at the May 2016 Embedded Vision Summit.
Computer vision has come a long way. Use cases that were previously not possible in mass-market devices are now more accessible thanks to advances in depth sensors and mobile processors. In this presentation, Mangen provides an overview of how we are able to implement high-resolution 3D reconstruction – a capability typically requiring cloud/server processing – on a mobile processor. This is an exciting example of how new sensor technology and advanced mobile processors are bringing computer vision capabilities to broader markets.
A project performed as a part of the Computer Vision (TSBB15) course at Linköping University. The purpose of the project is to implement a tracker capable of handling occlusion, shadows and changes in the background. Object tracking is, in computer vision, defined as the process where moving objects are located in an image sequence. The project goal was to create a real-time tracker to work on e.g. a surveillance camera and track the people and objects in its view. The application was implemented in C++ using the OpenCV library.
https://github.com/Epipolarna/Tracking
Linköping University has several student kitchens all over its campuses where students are given a possibility to warm their food. Critics claim that there are too few student kitchens and that the existing ones are usually overcrowded. That all kitchens are overcrowded at the same time has not been confirmed by sample inspections. One standing hypothesis is that students do not know where all the kitchens are, nor do they want to risk going to a kitchen in another building in case it is full as well.
The aim of this project is to develop a system that will provide the students with information regarding student kitchen usage. The system uses an computer vision approach, estimating the number of people currently using the kitchens. The system was developed using C++, the OpenCV library and the Qt5 library.
https://github.com/GroupDenseKitchen/KitchenOccupation
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-mangen
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Mangen, Product Manager for Camera and Computer Vision at Qualcomm, presents the "High-resolution 3D Reconstruction on a Mobile Processor" tutorial at the May 2016 Embedded Vision Summit.
Computer vision has come a long way. Use cases that were previously not possible in mass-market devices are now more accessible thanks to advances in depth sensors and mobile processors. In this presentation, Mangen provides an overview of how we are able to implement high-resolution 3D reconstruction – a capability typically requiring cloud/server processing – on a mobile processor. This is an exciting example of how new sensor technology and advanced mobile processors are bringing computer vision capabilities to broader markets.
A complete illustrated ppt on 3D printing technology. All the additive processes,Future and effects are well described with relevant diagram and images.Must download for attractive seminar presentation.3D Printing technology could revolutionize and re-shape the world. Advances in 3D printing technology can significantly change and improve the way we manufacture products and produce goods worldwide. If the last industrial revolution brought us mass production and the advent of economies of scale - the digital 3D printing revolution could bring mass manufacturing back a full circle - to an era of mass personalization, and a return to individual craftsmanship.
2. Reality Media Lab.
2
用語解説
点X=(X, Y, Z)T
座標系L
(こちらを基準とする)
座標系R
[R|t]
(座標系LからRへの変換行列)
エピポーラ平面
エピポール
エピポーラ線
(座標系Lから見た点X)
T
LLLLL )1,,( yx p
(座標系Rから見た点X)
T
RRRRR )1,,( yx p
LLL pKq
RRR pKq
3. Reality Media Lab.
3
基本行列E
物理座標系の点pLとpRを関連付ける行列
• 下図の関係から
• 上の式は,3つのベクトルpR,RpL,tは同一平面上にあることを
意味する
• つまり
tRpp LLRR
0)( LR Rptp
where,0][ L
T
L
T
RR
EppRptp RtE ][
点X
座標系L 座標系R
[R|t]
Lp Rp
4. Reality Media Lab.
4
基礎行列F
画像座標系の点qLとqRを関連付ける行列
• p,q内部パラメータ行列Kの関係は
• よって,
Kpq
where,0
)(
)()(
R
T
L
R
1
R
T
L
T
L
R
1
R
T
L
1
LL
T
R
Fqq
qKEKq
qKEqKEpp
1
R
T
L
EKKF
点X
座標系L 座標系R
[R|t]
Lp Rp
LLL pKq
RRR pKq
R