This document discusses several techniques for image-based rendering including light field rendering, the lumigraph, view-dependent texture mapping, the unstructured lumigraph, blending fields, and unstructured light fields. It provides intuitive explanations of each technique and how they represent scenes and allow novel views to be rendered from different positions and angles.
This document discusses object detection and tracking algorithms. It covers the following key points:
- Background modeling uses a mixture of Gaussian model to identify foreground objects from backgrounds. Tracking includes correlating current objects to previous ones and handling occlusion.
- Challenges include noisy images with false positives, shadows, and removing noise while detecting moving regions. Distance filtering and morphological operations help address these.
- Objects are identified using contours and bounding boxes. Kalman filtering can be used for prediction but has difficulties with smoothing.
- Performance is evaluated using multiple object tracking accuracy (MOTA) and multiple object tracking precision (MOTP) metrics. Areas for improvement include handling size changes and removing lost objects.
Link?ping 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
Lecture 4 Reconstruction from Two ViewsJoaquim Salvi
?
The document summarizes lecture 4 on reconstruction from two views. It discusses techniques for shape reconstruction including shape from X methods using multiple camera views or additional information. It then covers the triangulation principle for reconstructing 3D points from 2D point correspondences in multiple views. Finally, it introduces epipolar geometry which models the geometric relationship between two views and can be used to reconstruct the fundamental matrix and epipolar lines.
This document discusses cryo-electron microscopy (cryo-EM) 3D reconstruction techniques. It describes the cryo-EM imaging process and challenges in reconstructing 3D structures from 2D projection images, including large noise and data size. The document proposes a memory-saving algorithm using tight wavelet frames for cryo-EM 3D reconstruction that formulates the reconstruction as a sparse representation problem solved with soft-thresholding and gradient descent. Simulation results on an E. coli ribosome and experimental results on an adenovirus demonstrate the proposed algorithm can reconstruct 3D structures from noisy projection data.
The document describes the mean shift algorithm and its application to object tracking in computer vision. Mean shift is an iterative procedure that moves data points to the average of nearby points, converging at modes of the data's probability density function. It can be used for tracking by modeling a target object's color distribution and applying mean shift to match candidate locations in subsequent frames. The algorithm maximizes the Bhattacharyya coefficient between color distributions to find the best match for the target's new location in each frame.
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.
This document discusses several techniques for image-based rendering including light field rendering, the lumigraph, view-dependent texture mapping, the unstructured lumigraph, blending fields, and unstructured light fields. It provides intuitive explanations of each technique and how they represent scenes and allow novel views to be rendered from different positions and angles.
This document discusses object detection and tracking algorithms. It covers the following key points:
- Background modeling uses a mixture of Gaussian model to identify foreground objects from backgrounds. Tracking includes correlating current objects to previous ones and handling occlusion.
- Challenges include noisy images with false positives, shadows, and removing noise while detecting moving regions. Distance filtering and morphological operations help address these.
- Objects are identified using contours and bounding boxes. Kalman filtering can be used for prediction but has difficulties with smoothing.
- Performance is evaluated using multiple object tracking accuracy (MOTA) and multiple object tracking precision (MOTP) metrics. Areas for improvement include handling size changes and removing lost objects.
Link?ping 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
Lecture 4 Reconstruction from Two ViewsJoaquim Salvi
?
The document summarizes lecture 4 on reconstruction from two views. It discusses techniques for shape reconstruction including shape from X methods using multiple camera views or additional information. It then covers the triangulation principle for reconstructing 3D points from 2D point correspondences in multiple views. Finally, it introduces epipolar geometry which models the geometric relationship between two views and can be used to reconstruct the fundamental matrix and epipolar lines.
This document discusses cryo-electron microscopy (cryo-EM) 3D reconstruction techniques. It describes the cryo-EM imaging process and challenges in reconstructing 3D structures from 2D projection images, including large noise and data size. The document proposes a memory-saving algorithm using tight wavelet frames for cryo-EM 3D reconstruction that formulates the reconstruction as a sparse representation problem solved with soft-thresholding and gradient descent. Simulation results on an E. coli ribosome and experimental results on an adenovirus demonstrate the proposed algorithm can reconstruct 3D structures from noisy projection data.
The document describes the mean shift algorithm and its application to object tracking in computer vision. Mean shift is an iterative procedure that moves data points to the average of nearby points, converging at modes of the data's probability density function. It can be used for tracking by modeling a target object's color distribution and applying mean shift to match candidate locations in subsequent frames. The algorithm maximizes the Bhattacharyya coefficient between color distributions to find the best match for the target's new location in each frame.
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.
1) Kernel Bayes' rule provides a nonparametric approach to Bayesian inference using positive definite kernels. It represents probabilities as elements in a reproducing kernel Hilbert space.
2) Using kernel mean embeddings, kernel Bayes' rule computes the posterior kernel mean directly from covariance operators without needing to compute integrals or approximations.
3) Given samples from the joint distribution and the prior kernel mean, kernel Bayes' rule computes the posterior kernel mean as a weighted sum of prior sample kernel embeddings, providing a nonparametric realization of Bayesian inference.
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?