Michael Norel - High Accuracy Camera Calibration
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This document discusses high accuracy camera calibration. It covers the importance of robust calibration and current approaches using tools like OpenCV and Matlab that can have errors. Problems are identified with locating features precisely and systematic noise from location and luminance errors. The document introduces achieving subpixel accuracy through techniques like digital image correlation on circular markers and bundle adjustment that accounts for real feature locations. It emphasizes eliminating errors from correlation and optimization to contribute improved calibration methods to open source tools like OpenCV.
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This "must see" session is not only for data modelers and architects, but also the decision makers for these initiatives, with information that is vital to modelers, IT executives and business sponsors. So, bring your boss to the session!
Close range Photogrammeetry
The document discusses the science and techniques of photogrammetry. Photogrammetry involves deriving precise 3D coordinates of points by viewing an area from two angles and mathematically intersecting converging lines in space. It allows for the creation of accurate 3D models, textured models, and dense surface models from photographs for applications like measurements, visualization, and meshing. The process involves camera calibration, data acquisition through stereo or all-directional photography, feature marking, orientation, idealization, point cloud generation, meshing, surface generation, texturing, and exporting the 3D data.
EGUE Technikrom Final_8_12_13
This document describes implementing real-time non-linear regression models in a DeltaV controller to determine chromatography elution endpoints. Three models were used - smoothing, linear regression, and a non-linear extreme value function. The non-linear model fits peak data to an extreme value function using Newton-Raphson iterative numerical solving to determine peak height, width, and retention time. This allows calculating the endpoint as a percentage of peak maximum. The model was validated against laboratory data to within acceptable error ranges and allows robust, real-time endpoint determination for chromatography processes.
tatacara kalibrasi Kendaraan.ppt
This document presents research on automatic camera calibration for vision-based traffic speed sensing. Traditional sensors have limitations, so the researchers developed a system using machine vision with a camera. It uses pattern detection-based vehicle tracking to initialize calibration and estimate vanishing points. This allows automatic calibration of pan-tilt-zoom cameras without manual setup. The algorithm was tested and achieved real-time vehicle detection, tracking, classification and calibration. Future work aims to improve accuracy by incorporating lane markings and develop online tuning of the system.
vehicle calibration.ppt
This document presents research on automatic camera calibration for vision-based traffic speed sensing. Traditional sensors have limitations, so the researchers developed a system using machine vision with a pattern detection-based tracking algorithm. It automatically calibrates pan-tilt-zoom cameras without manual setup by estimating vanishing points and performing RANSAC fitting on vehicle tracking data. This allows real-time detection, tracking and classification of vehicles under varying conditions without road markings or predefined parameters. A demonstration of the system showed it can eliminate false alarms while handling occlusion. Future work will refine the calibration and enable turn counting at intersections.
Dip syllabus
This document outlines the units of study for a course in Digital Image Processing. It covers topics such as image fundamentals, transforms including Fourier, filters in both the spatial and frequency domains, image restoration and reconstruction, color image processing, wavelets and multi-resolution processing, image compression, morphological image processing, and image segmentation. References for further reading on digital image processing are also provided.
Panoramic image mosaics
This document discusses techniques for panoramic image mosaicing, including motion parallax, image warping, blending, and registration. It describes algorithms for constructing cylindrical and spherical panoramas by warping images into cylindrical or spherical coordinates. The Schum-Szeliski algorithm is summarized, which uses a hierarchical framework to sequentially construct panoramas from overlapping images by estimating incremental motions and minimizing intensity errors. It also discusses representing motions using 3D rotations and scales instead of the more complex 8-parameter model.
Convolutional Neural Network for pixel-wise skyline detection
ICANN 2017
26th International Conference on Artificial Neural Networks
Alghero, Sardinia, Italy
September 2017
CNN vs SIFT-based Visual Localization - Laura Leal-Taixé - UPC Barcelona 2018
CNN vs SIFT-based Visual Localization - Laura Leal-Taixé - UPC Barcelona 2018Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.Camera Calibration Market
An Overview of Market Opportunities for Intelligent Systems Using a Calibrated Camera as an Input Measuring Tool
Visual diagnostics for more effective machine learning
The model selection process is a search for the best combination of features, algorithm, and hyperparameters that maximize F1, R2, or silhouette scores after cross-validation. This view of machine learning often leads us toward automated processes such as grid searches and random walks. Although this approach allows us to try many combinations, we are often left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this talk, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more effective.
Large Scale Image Retrieval 2022.pdf
The document summarizes techniques for finding correspondences between images, including local feature detection, descriptors, and matching. It discusses how local features can be used for large scale image retrieval applications. Local feature detectors aim to find repeatable keypoints that are robust to changes in viewpoint and occlusion. Descriptors are used to represent image patches around keypoints to enable matching. Techniques like bag-of-words models and geometric verification allow matching features across large databases to perform image search and retrieval.
Lecture 01 frank dellaert - 3 d reconstruction and mapping: a factor graph ...
Frank Dellaert presented an overview of visual SLAM, bundle adjustment, and factor graphs. Visual SLAM uses visual odometry to estimate camera poses incrementally from frame to frame. Bundle adjustment refines the camera pose estimates using non-linear optimization over all camera poses and 3D landmarks jointly. Factor graphs provide a graphical representation of the optimization problem in bundle adjustment.
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Convolutional Neural Network for pixel-wise skyline detection
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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Michael Norel - High Accuracy Camera Calibration
- 1. High accuracy camera calibration Michael Norel Head of R&D at Augmented Pixels michael@augmentedpixels.com
- 2. High accuracy camera calibration - importance - robustness
- 3. Current state - openCV - matlab - current approach
- 4. Problems - features precise location - errors of corner location - errors of ellipse center - perspective(radial) warp
- 5. Systematic noise - from location errors - from luminance offsets
- 6. High accuracy calibration - introduction to high accuracy - old style calibration without errors - subpixel accuracy - precise marker location
- 7. Digital image correlation frontal image (fronto-parallel)
- 8. Digital image correlation brief intro onto DIC precision (sort of precise optical flow)
- 9. Digital image correlation - circular marker approach - model based - symmetric
- 10. Bundle adjustment - imperfection of calibration pattern - real feature locations
- 11. Numeric problems - importance of parametrization - optimization stuck - different cost functions
- 12. Uncertainty - correct uncertainty estimation - cross correlation - parameters elimination
- 13. Just do it - how to eliminate errors from symmetric correlation? - different types of parametrization - new types of fiducial markers - contribute to openCV, add your name to CV history
- 14. list of sources 1. «Advanced geometric camera calibration for machine vision» Minh Vo,Zhaoyang Wang,Long Luu,and Jun Ma 2. «Precision Landmark Location for Machine Vision and Photogrammetry», Jose A.Gutierrez , Brian S.R. Armstrong 3. «Image Correlation for Shape, Motion and Deformation Measurements», Michael A. Sutton, Jean-José Orteu, Hubert W. Schreier
- 15. Questions?