Provide a summary for "RGB-(D) Scene Labeling: Features and Algorithms" paper, written by X Ren, L Bo, D Fox - Computer Vision and Pattern Recognition 2012 - ieeexplore.ieee.org
This document discusses using the Microsoft Kinect for 3-D mapping of rooms. It describes how the Kinect uses an infrared sensor and RGB camera to create depth images and produce 3D point clouds of environments. The document outlines how SLAM algorithms can then be used to extract visual keypoints from images to localize points in 3D space and build consistent maps over time as the Kinect moves. These maps could be used for robot navigation or teleoperation. The Kinect is presented as a low-cost alternative to more expensive depth sensing systems.
Annotation tools for ADAS & Autonomous DrivingYu Huang
The document lists over 30 tools for annotating images, videos, and point cloud data. Many of the tools are open source and used for tasks like object detection, segmentation, and labeling. The tools cover a wide range of domains from natural images to LiDAR point clouds and include both online and desktop-based annotation solutions.
3-d interpretation from single 2-d image VYu Huang
The document outlines several approaches for monocular 3D object detection from a single 2D image for autonomous driving applications. It summarizes MonoRUn, which uses self-supervised dense correspondences and geometry along with uncertainty propagation. It also summarizes M3DSSD, which uses feature alignment and asymmetric non-local attention in a single-stage detector. Additionally, it discusses analyzing and addressing localization errors, integrating differentiable NMS into training, and a flexible framework that decouples and adapts approaches for truncated vs normal objects.
This document summarizes HCChang's research interests and experience in dense visual simultaneous localization and mapping (SLAM). It begins with an overview of monoSLAM, PTAM, FAB-MAP and DTAM as examples of visual SLAM techniques. It then provides more detail on KinectFusion, the seminal dense visual SLAM method, and extensions like InfiniTAM, ElasticFusion and DynamicFusion. The document outlines HCChang's background and current work using time-of-flight cameras at EZImage to improve depth sensing. It proposes future work on dense visual SLAM including deploying to Nvidia's TX1 and TK1 platforms, adding loop closures and path optimization, and reconstruct
This document lists 37 titles of papers published in the IEEE Transactions on Image Processing journal in 2015. The titles are grouped under categories including face recognition, image denoising, texture classification, depth reconstruction, and crowd analysis. Years of publication and sequential numbers are provided for each paper.
A Three-Dimensional Representation method for Noisy Point Clouds based on Gro...Sergio Orts-Escolano
Slides used for the thesis defense of the PhD candidate Sergio Orts-Escolano.
The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time.This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problems and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.
2019年6月13日、SSII2019 Organized Session: Multimodal 4D sensing。エンドユーザー向け SLAM 技術の現在。登壇者:武笠 知幸(Research Scientist, Rakuten Institute of Technology)
https://confit.atlas.jp/guide/event/ssii2019/static/organized#OS2
This document discusses using the Microsoft Kinect for 3-D mapping of rooms. It describes how the Kinect uses an infrared sensor and RGB camera to create depth images and produce 3D point clouds of environments. The document outlines how SLAM algorithms can then be used to extract visual keypoints from images to localize points in 3D space and build consistent maps over time as the Kinect moves. These maps could be used for robot navigation or teleoperation. The Kinect is presented as a low-cost alternative to more expensive depth sensing systems.
Annotation tools for ADAS & Autonomous DrivingYu Huang
The document lists over 30 tools for annotating images, videos, and point cloud data. Many of the tools are open source and used for tasks like object detection, segmentation, and labeling. The tools cover a wide range of domains from natural images to LiDAR point clouds and include both online and desktop-based annotation solutions.
3-d interpretation from single 2-d image VYu Huang
The document outlines several approaches for monocular 3D object detection from a single 2D image for autonomous driving applications. It summarizes MonoRUn, which uses self-supervised dense correspondences and geometry along with uncertainty propagation. It also summarizes M3DSSD, which uses feature alignment and asymmetric non-local attention in a single-stage detector. Additionally, it discusses analyzing and addressing localization errors, integrating differentiable NMS into training, and a flexible framework that decouples and adapts approaches for truncated vs normal objects.
This document summarizes HCChang's research interests and experience in dense visual simultaneous localization and mapping (SLAM). It begins with an overview of monoSLAM, PTAM, FAB-MAP and DTAM as examples of visual SLAM techniques. It then provides more detail on KinectFusion, the seminal dense visual SLAM method, and extensions like InfiniTAM, ElasticFusion and DynamicFusion. The document outlines HCChang's background and current work using time-of-flight cameras at EZImage to improve depth sensing. It proposes future work on dense visual SLAM including deploying to Nvidia's TX1 and TK1 platforms, adding loop closures and path optimization, and reconstruct
This document lists 37 titles of papers published in the IEEE Transactions on Image Processing journal in 2015. The titles are grouped under categories including face recognition, image denoising, texture classification, depth reconstruction, and crowd analysis. Years of publication and sequential numbers are provided for each paper.
A Three-Dimensional Representation method for Noisy Point Clouds based on Gro...Sergio Orts-Escolano
Slides used for the thesis defense of the PhD candidate Sergio Orts-Escolano.
The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time.This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problems and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.
2019年6月13日、SSII2019 Organized Session: Multimodal 4D sensing。エンドユーザー向け SLAM 技術の現在。登壇者:武笠 知幸(Research Scientist, Rakuten Institute of Technology)
https://confit.atlas.jp/guide/event/ssii2019/static/organized#OS2
Normal mapping is a technique used in 3D computer graphics to add detail to 3D models without increasing the number of polygons. It works by encoding normal vector information for light calculation into RGB texture maps. This allows more detailed surface shapes and lighting than would be possible with just the base polygon mesh. The technique was introduced in the late 1990s and became widely used in video games starting in the early 2000s as hardware accelerated shaders became available, enabling real-time normal mapping rendering. It provides a good quality to performance ratio for complex surface details.
This document discusses principles and practices of drone flight planning for photogrammetry purposes. It explains that overlapping photos are needed to reconstruct 3D geometry through structure from motion algorithms. The key stages of SfM processing are described, including feature detection, sparse and dense reconstruction, meshing, and texturing. Several open-source SfM software options are listed as alternatives to commercial programs. Limitations of SfM related to feature similarity, non-Lambertian surfaces, and thin structures are also noted.
Semantic mapping of road scenes, PhD thesis. The main aim of the thesis is to investigate and propose solutions to the scene understanding problem of finding 'what' objects are present in the world and 'where' are they located.
YARCA (Yet Another Raycasting Application) Projectgraphitech
The scope of this project is to extend NASA’s World Wind to make it possible to visualize ray casting not only in intersection with the terrain, but also to consider 3D objects, which we call barriers, that will be hit by rays emitted by other objects which we call transmitters, calculating the coverage area and field of view of the transmitters and showing how the transmission signal is reflected onto the objects’ surfaces.
ieee Image processing project title 2014-2015allmightinfo
This document lists 32 image processing project titles from 2014-2015. The projects cover a wide range of topics including remote sensing image classification, super resolution, moving target imaging in SAR, image fusion, quality estimation, shadow removal, object detection, bilateral filtering, inspection systems, change detection, crop classification, data hiding, vehicle classification and counting, edge detection, classification, dark spot detection, color morphology, license plate detection, food calorie analysis, target analysis, segmentation, camera planning, text detection, resizing, building extraction, volume estimation, satellite image analysis, land cover classification, histogram equalization, and vehicle reidentification for travel time estimation.
Urban 3D Semantic Modelling Using Stereo Vision, ICRA 2013Sunando Sengupta
1) Given a sequence of stereo images, the pipeline generates a dense 3D semantic model of the urban environment.
2) Depth maps are generated from stereo images and fused into a volumetric representation using camera poses from feature tracking.
3) Semantic segmentation of street view images is done using a CRF model, and labels are projected onto the 3D model faces to generate the semantic model.
4) The semantic model is evaluated by projecting it back to the input images and calculating metrics like recall and intersection over union. Future work includes real-time implementation and combining image and geometric context.
This document proposes a semantic octree framework that unifies recognition, reconstruction, and representation of 3D scenes using an octree constrained higher order Markov random field. It combines associative higher-order random fields (AHRF) for semantic segmentation with octree-based volumetric mapping. The framework takes stereo images as input, generates point clouds and class hypotheses, then fuses the data into an octree. Inference over the octree voxels assigns labels to produce a semantically labelled 3D scene. The approach allows for efficient access and manipulation of 3D models through the octree representation.
Intellectual property, traceability and the counterfeiting of 3D printable objects
3D Robust Blind Watermarking : A tool for 3D copyrighted printing?
Benoit Macq and Patrice Rondão Alface - ICL-ICTEAM
Pengantar Structure from Motion PhotogrammetryDany Laksono
Structure from Motion (SfM) photogrammetry can be used to extract 3D point cloud data and generate digital elevation models (DEMs) from optical camera sensors. The SfM process involves feature detection, feature matching between images, sparse reconstruction to estimate camera positions and an initial 3D geometry, dense reconstruction using multi-view stereo to generate depth maps and a dense point cloud, and texturing to create 3D models. The resulting products include sparse and dense point clouds, DEMs, and textured 3D models. While powerful, SfM has limitations for scenes with featureless surfaces, repetitive patterns, or thin structures. Open-source SfM software includes WebODM, OpenMVG,
Build Your Own 3D Scanner:
Introduction
http://mesh.brown.edu/byo3d/
SIGGRAPH 2009 Courses
Douglas Lanman and Gabriel Taubin
This course provides a beginner with the necessary mathematics, software, and practical details to leverage projector-camera systems in their own 3D scanning projects. An example-driven approach is used throughout; each new concept is illustrated using a practical scanner implemented with off-the-shelf parts. The course concludes by detailing how these new approaches are used in rapid prototyping, entertainment, cultural heritage, and web-based applications.
DimEye Corp Presents Revolutionary VLS (Video Laser Scan) at SS IMMR 2013Patrick Raymond
DimEye Corp. Introduces the Revolutionary VLS (Video Laser Scan) to the Subsea Survey IMMR audience in Galveston Texas (November 2013).
VLS™ (Video Laser Scan) by DimEye Corp. is a revolution in Optical 3D Measurement. VLS Provides High Definition Visual Inspection, As-Built 3D Modeling of Industrial/Subsea Equipment, 3D High Density Mapping of Deformations and Defects (for example: Cracks, Dents, Bulges, Corrosion)
VLS™ is a unique combination of photogrammetry and Laser Techniques which provides the Advantages of both technologies without the disadvantages. VLS™ can also be operated by your existing technicians.
VLS™ is a High Accuracy Metrology Tool Linked to NIST (National Industry of Standards and Technology) that provides High Redundancy through volume of data collected (1000s of stills can be captured from HD video in seconds rather than individual photos taken manually at each location). VLS™ provides Reliable Accuracy Estimates (thanks to advanced processing and calibration algorithms developed by DimEye after years of industry experience in multiple measurement environments and scenarios).
Automatic Dense Semantic Mapping From Visual Street-level ImagerySunando Sengupta
This talk presented at IROS 2012, Portugal, discusses a method to generate an overhead semantic map, akin to google maps but with associated object class labels. We run experiment on tens of kilometres of data.
Digital Image Processing Projects for Final Year StudentsManoj Subramanian
The document lists 66 projects related to image and signal processing. The projects cover various applications including satellite imaging, computer vision, biomedical imaging, machine vision, data security, storage and transmission, and mobile apps. The projects utilize different technologies such as Matlab, TMS320C5505, TMS320C6745, H.264, AI systems, wavelets, NSCT, SURF, and more. The document provides a high level overview of the projects, their codes, themes, applications, and technologies.
Visual Environment by Semantic Segmentation Using Deep Learning: A Prototype ...Tomohiro Fukuda
This document describes a proposed method for estimating sky view factor (SVF) using semantic segmentation with deep learning networks. Specifically:
- It develops a system using SegNet and U-Net deep learning models to perform pixel-wise semantic segmentation of sky and non-sky areas from images to calculate SVF ratios.
- The system was trained on 300 manually segmented images and tested on 100 fisheye photographs, achieving 98% accuracy in estimating SVF under different sky conditions.
- Future work is needed to apply the system to live video streams rather than static images. The method provides an efficient, high-precision way to estimate important urban environmental metrics like SVF.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.
An Open Source solution for Three-Dimensional documentation: archaeological a...Giulio Bigliardi
The modern techniques of Structure from Motion (SfM) and Image-Based Modelling
(IBM) open new perspectives in the field of archaeological documentation, providing
a simple and accurate way to record three dimensional data.
The software Python Photogrammetry Toolbox (PPT) is an Open Source solution that
implements a pipeline to perform 3D reconstruction from a set of pictures. It takes
pictures as input and performs automatically the 3D reconstruction for the images for
which 3D registration is possible.
It is composed of python scripts that automate the different steps of the workflow.
The entire process is reduced in two commands, calibration and dense reconstruction.
The user can run it from a graphical interface or from terminal command. Calibration
is performed with Bundler while dense reconstruction is done through CMVS/PMVS.
Despite the automation, the user can control the final result choosing two initial
parameters: the image size and the feature detector. Acting on the first parameter
determines a reduction of the computation time and a decreasing density of the point
cloud. Acting on the feature detector influences the final result: PPT can work both
with SIFT (patent of the University of British Columbia - freely usable only for
research purpose) and with VLFEAT (released under GPL v.2 license). The use of
VLFEAT ensures a more accurate result, though it increases the time of calculation.
Python Photogrammetry Toolbox, released under GPL v.3 license, is a classical
example of FLOSS project in which instruments and knowledge are shared. The community works for the development of the software, sharing code modification,
feed-backs and bug-checking.
The document provides an overview of the Point Cloud Library (PCL), an open-source library for point cloud processing. PCL contains algorithms for filtering, segmentation, registration, surface reconstruction and more. It has a modular structure with libraries for filters, features, keypoints, visualization and I/O. PCL data can be read from and written to various file formats like PCD and PLY. It also integrates with MeshLab for point cloud visualization. Examples are given to demonstrate converting a PCD file to PLY format using PCL.
Augmented Reality - Connecting Physical and Virtual WorldsDunavNET
- DunavNET is a Serbian company established in 2006 that focuses on Internet of Things, smart cities, augmented reality, and mobile applications. It has 50 employees with expertise in these areas.
- The company has experience developing augmented reality applications and platforms, as well as IoT and smart city solutions. It currently has several projects funded by the EU involving these technologies.
- Augmented reality enhances the real world with computer-generated perceptual information. DunavNET's ARgenie platform allows developers to easily create augmented reality applications involving markers, images, and location without needing to write code.
Normal mapping is a technique used in 3D computer graphics to add detail to 3D models without increasing the number of polygons. It works by encoding normal vector information for light calculation into RGB texture maps. This allows more detailed surface shapes and lighting than would be possible with just the base polygon mesh. The technique was introduced in the late 1990s and became widely used in video games starting in the early 2000s as hardware accelerated shaders became available, enabling real-time normal mapping rendering. It provides a good quality to performance ratio for complex surface details.
This document discusses principles and practices of drone flight planning for photogrammetry purposes. It explains that overlapping photos are needed to reconstruct 3D geometry through structure from motion algorithms. The key stages of SfM processing are described, including feature detection, sparse and dense reconstruction, meshing, and texturing. Several open-source SfM software options are listed as alternatives to commercial programs. Limitations of SfM related to feature similarity, non-Lambertian surfaces, and thin structures are also noted.
Semantic mapping of road scenes, PhD thesis. The main aim of the thesis is to investigate and propose solutions to the scene understanding problem of finding 'what' objects are present in the world and 'where' are they located.
YARCA (Yet Another Raycasting Application) Projectgraphitech
The scope of this project is to extend NASA’s World Wind to make it possible to visualize ray casting not only in intersection with the terrain, but also to consider 3D objects, which we call barriers, that will be hit by rays emitted by other objects which we call transmitters, calculating the coverage area and field of view of the transmitters and showing how the transmission signal is reflected onto the objects’ surfaces.
ieee Image processing project title 2014-2015allmightinfo
This document lists 32 image processing project titles from 2014-2015. The projects cover a wide range of topics including remote sensing image classification, super resolution, moving target imaging in SAR, image fusion, quality estimation, shadow removal, object detection, bilateral filtering, inspection systems, change detection, crop classification, data hiding, vehicle classification and counting, edge detection, classification, dark spot detection, color morphology, license plate detection, food calorie analysis, target analysis, segmentation, camera planning, text detection, resizing, building extraction, volume estimation, satellite image analysis, land cover classification, histogram equalization, and vehicle reidentification for travel time estimation.
Urban 3D Semantic Modelling Using Stereo Vision, ICRA 2013Sunando Sengupta
1) Given a sequence of stereo images, the pipeline generates a dense 3D semantic model of the urban environment.
2) Depth maps are generated from stereo images and fused into a volumetric representation using camera poses from feature tracking.
3) Semantic segmentation of street view images is done using a CRF model, and labels are projected onto the 3D model faces to generate the semantic model.
4) The semantic model is evaluated by projecting it back to the input images and calculating metrics like recall and intersection over union. Future work includes real-time implementation and combining image and geometric context.
This document proposes a semantic octree framework that unifies recognition, reconstruction, and representation of 3D scenes using an octree constrained higher order Markov random field. It combines associative higher-order random fields (AHRF) for semantic segmentation with octree-based volumetric mapping. The framework takes stereo images as input, generates point clouds and class hypotheses, then fuses the data into an octree. Inference over the octree voxels assigns labels to produce a semantically labelled 3D scene. The approach allows for efficient access and manipulation of 3D models through the octree representation.
Intellectual property, traceability and the counterfeiting of 3D printable objects
3D Robust Blind Watermarking : A tool for 3D copyrighted printing?
Benoit Macq and Patrice Rondão Alface - ICL-ICTEAM
Pengantar Structure from Motion PhotogrammetryDany Laksono
Structure from Motion (SfM) photogrammetry can be used to extract 3D point cloud data and generate digital elevation models (DEMs) from optical camera sensors. The SfM process involves feature detection, feature matching between images, sparse reconstruction to estimate camera positions and an initial 3D geometry, dense reconstruction using multi-view stereo to generate depth maps and a dense point cloud, and texturing to create 3D models. The resulting products include sparse and dense point clouds, DEMs, and textured 3D models. While powerful, SfM has limitations for scenes with featureless surfaces, repetitive patterns, or thin structures. Open-source SfM software includes WebODM, OpenMVG,
Build Your Own 3D Scanner:
Introduction
http://mesh.brown.edu/byo3d/
SIGGRAPH 2009 Courses
Douglas Lanman and Gabriel Taubin
This course provides a beginner with the necessary mathematics, software, and practical details to leverage projector-camera systems in their own 3D scanning projects. An example-driven approach is used throughout; each new concept is illustrated using a practical scanner implemented with off-the-shelf parts. The course concludes by detailing how these new approaches are used in rapid prototyping, entertainment, cultural heritage, and web-based applications.
DimEye Corp Presents Revolutionary VLS (Video Laser Scan) at SS IMMR 2013Patrick Raymond
DimEye Corp. Introduces the Revolutionary VLS (Video Laser Scan) to the Subsea Survey IMMR audience in Galveston Texas (November 2013).
VLS™ (Video Laser Scan) by DimEye Corp. is a revolution in Optical 3D Measurement. VLS Provides High Definition Visual Inspection, As-Built 3D Modeling of Industrial/Subsea Equipment, 3D High Density Mapping of Deformations and Defects (for example: Cracks, Dents, Bulges, Corrosion)
VLS™ is a unique combination of photogrammetry and Laser Techniques which provides the Advantages of both technologies without the disadvantages. VLS™ can also be operated by your existing technicians.
VLS™ is a High Accuracy Metrology Tool Linked to NIST (National Industry of Standards and Technology) that provides High Redundancy through volume of data collected (1000s of stills can be captured from HD video in seconds rather than individual photos taken manually at each location). VLS™ provides Reliable Accuracy Estimates (thanks to advanced processing and calibration algorithms developed by DimEye after years of industry experience in multiple measurement environments and scenarios).
Automatic Dense Semantic Mapping From Visual Street-level ImagerySunando Sengupta
This talk presented at IROS 2012, Portugal, discusses a method to generate an overhead semantic map, akin to google maps but with associated object class labels. We run experiment on tens of kilometres of data.
Digital Image Processing Projects for Final Year StudentsManoj Subramanian
The document lists 66 projects related to image and signal processing. The projects cover various applications including satellite imaging, computer vision, biomedical imaging, machine vision, data security, storage and transmission, and mobile apps. The projects utilize different technologies such as Matlab, TMS320C5505, TMS320C6745, H.264, AI systems, wavelets, NSCT, SURF, and more. The document provides a high level overview of the projects, their codes, themes, applications, and technologies.
Visual Environment by Semantic Segmentation Using Deep Learning: A Prototype ...Tomohiro Fukuda
This document describes a proposed method for estimating sky view factor (SVF) using semantic segmentation with deep learning networks. Specifically:
- It develops a system using SegNet and U-Net deep learning models to perform pixel-wise semantic segmentation of sky and non-sky areas from images to calculate SVF ratios.
- The system was trained on 300 manually segmented images and tested on 100 fisheye photographs, achieving 98% accuracy in estimating SVF under different sky conditions.
- Future work is needed to apply the system to live video streams rather than static images. The method provides an efficient, high-precision way to estimate important urban environmental metrics like SVF.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.
An Open Source solution for Three-Dimensional documentation: archaeological a...Giulio Bigliardi
The modern techniques of Structure from Motion (SfM) and Image-Based Modelling
(IBM) open new perspectives in the field of archaeological documentation, providing
a simple and accurate way to record three dimensional data.
The software Python Photogrammetry Toolbox (PPT) is an Open Source solution that
implements a pipeline to perform 3D reconstruction from a set of pictures. It takes
pictures as input and performs automatically the 3D reconstruction for the images for
which 3D registration is possible.
It is composed of python scripts that automate the different steps of the workflow.
The entire process is reduced in two commands, calibration and dense reconstruction.
The user can run it from a graphical interface or from terminal command. Calibration
is performed with Bundler while dense reconstruction is done through CMVS/PMVS.
Despite the automation, the user can control the final result choosing two initial
parameters: the image size and the feature detector. Acting on the first parameter
determines a reduction of the computation time and a decreasing density of the point
cloud. Acting on the feature detector influences the final result: PPT can work both
with SIFT (patent of the University of British Columbia - freely usable only for
research purpose) and with VLFEAT (released under GPL v.2 license). The use of
VLFEAT ensures a more accurate result, though it increases the time of calculation.
Python Photogrammetry Toolbox, released under GPL v.3 license, is a classical
example of FLOSS project in which instruments and knowledge are shared. The community works for the development of the software, sharing code modification,
feed-backs and bug-checking.
The document provides an overview of the Point Cloud Library (PCL), an open-source library for point cloud processing. PCL contains algorithms for filtering, segmentation, registration, surface reconstruction and more. It has a modular structure with libraries for filters, features, keypoints, visualization and I/O. PCL data can be read from and written to various file formats like PCD and PLY. It also integrates with MeshLab for point cloud visualization. Examples are given to demonstrate converting a PCD file to PLY format using PCL.
Augmented Reality - Connecting Physical and Virtual WorldsDunavNET
- DunavNET is a Serbian company established in 2006 that focuses on Internet of Things, smart cities, augmented reality, and mobile applications. It has 50 employees with expertise in these areas.
- The company has experience developing augmented reality applications and platforms, as well as IoT and smart city solutions. It currently has several projects funded by the EU involving these technologies.
- Augmented reality enhances the real world with computer-generated perceptual information. DunavNET's ARgenie platform allows developers to easily create augmented reality applications involving markers, images, and location without needing to write code.
Today I talked at a publisher's conference in Berlin. I was the only one representing authors and talked about how the power relationships in publishing will shift to communities, readers and others - away from publishers. I explained it with our business model innovation book project
This presentation gives an overview about semi-supervised learning methods (Least square solution, Eigen vectors and Eigen functions). It points to some of the applications these methods can be used like object categorization and Interactive Image segmentation
On June 19, 2009, we are organizing a gathering of business model innovation practitioners to share knowledge and experience.
The crowning of the day will be the "Business Model Generation" book lunch with a special limited edition for participants.
http://businessmodelfair.eventbrite.com
This document discusses several patterns of business models that can be identified. It describes the pattern of unbundling, where a business is divided into customer relationship, product innovation, and infrastructure areas. It also outlines the long tail pattern of selling many niche products in small quantities. Another pattern is the multi-sided platform that connects different user groups. The document examines free business models where one user group subsidizes another. Finally, it introduces the open business model pattern of collaborating systematically with external partners.
This document discusses business model innovation through the example of the iPod. It highlights that while the iPod was a beautifully designed product, its success was also due to its carefully designed business model. The document directs the reader to the author's blog for more information on business model design and examples, and provides the author's contact information.
The document discusses the long tail business model concept coined by Chris Anderson, which focuses on selling a large number of products with low sales volumes rather than a small number of popular products. It suggests businesses consider this model and its implications for their own business models, partnerships, key activities, resources, costs, distribution channels, customer segments, and revenue streams. The long tail model emphasizes selling many less popular products in addition to hits to maximize profits and opportunities.
Business Model Patterns and Examples Part IAhmed Taha
These slides are part I of 2 parts presentation about Business Model. I
n this part I present the business model definition, well-known business model patterns. Examples about each patterns is provided as well.
The document summarizes Steve Jobs' leadership style through his career as the co-founder of Apple Computers and Pixar Animation Studios. It describes how Jobs was a transformational leader who focused on innovation and passion to motivate his employees. While Jobs drove innovation and success, he was also known to be demanding and difficult to work with at times. The document outlines Jobs' biography and major career accomplishments, and references sources for further information.
This document discusses business model innovation using the Business Model Canvas as a tool. It provides an example of how Nespresso changed their business model to become successful. Their original model in 1987 almost failed, but by changing to a model where they control the pod production and recycling, they were able to grow significantly with annual growth over 30% and global sales of over $3.8 billion. The document advocates testing business model prototypes with customers to find the right model rather than relying only on the ideas of management.
How to become a more effective leader/manager/supervisor. How to recognize your default leadership style, and how to incorporate other styles and methods in order to develop your leadership capabilities.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now 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 text captioning.
1. The document discusses various leadership styles and tactics for managing people effectively, including Likert's four styles and the Tannenbaum-Schmidt continuum of leadership behavior.
2. It also covers the skills needed for persuasion, motivation, and conflict resolution as leaders work to lead their teams and implement agendas. Specific tactics discussed include identifying key relationships, assessing sources of power and resistance, and developing relationships to enable cooperation.
3. Effective leadership requires the ability to motivate different types of employees through clarity of vision, caring, setting goals, and managing crisis, as well as dealing constructively with problem employees or difficult situations.
The document discusses business model innovation and introduces the Business Model Canvas as a tool. It is made up of 9 building blocks: key partners, key activities, key resources, value propositions, customer relationships, channels, customer segments, cost structure, and revenue streams. The canvas has been widely used and can be customized with additional "modules". Refining business ideas too quickly can lead to attachment while exploring alternatives is important for finding the best model.
The document provides an example case study on the topic of coffee production and deforestation in the Amazon rainforest. It outlines the problem of thousands of acres of rainforest being burned to grow coffee trees. It then summarizes key points from several websites that were researched on this topic, finding that vast amounts of primary forest have been cleared for coffee cultivation, leading to rampant deforestation and impacts to wildlife habitats and migration routes. Potential solutions discussed include crop rotation, replanting forests, and promoting conservation and shade-grown coffee methods to help reduce environmental impacts.
The document discusses business model design and testing. It emphasizes that business plans often fail upon contact with customers, so business models need to be tested through prototypes and by talking to customers to validate hypotheses. The document encourages designing business models systematically using tools like the Business Model Canvas, and iterating models through testing and pivoting based on customer feedback.
An efficient technique for color image classification based on lower feature ...Alexander Decker
This document discusses an efficient technique for color image classification using support vector machines with radial basis functions (SVM-RBF). It presents SVM-RBF as an improvement over other classification methods like SVM with ant colony optimization (SVM-ACO) and directed acyclic graph (SVM-DAG). The paper tests the different classifiers on 600 images across 3 classes, finding SVM-RBF achieved the highest precision and recall rates, with precision of 92.3-94% and recall of 84.8-91%. It concludes SVM-RBF more effectively reduces noise and the semantic gap to enhance image classification performance compared to the other methods.
Shallow vs. Deep Image Representations: A Comparative Study with Enhancements...CSCJournals
This document compares shallow and deep image representations for object recognition. It discusses the traditional pipeline approach using handcrafted features extracted via local feature detectors and descriptors, then encoded and pooled. It proposes enhancements to this pipeline by augmenting features. It also discusses end-to-end deep learning models that learn representations directly from images in multiple layers without prior domain knowledge. The purpose is to compare shallow and deep representations, and improve results by combining deep models in an ensemble.
Deep Learning Fundamentals and Case studies using IBM POWER SystemsGanesan Narayanasamy
This document summarizes Satyadhyan Chickerur's presentation on AI fundamentals and deep learning frameworks using IBM Power Systems. The presentation introduces neural network architectures like feedforward neural networks, recurrent neural networks, LSTMs and CNNs. It then summarizes four case studies applying these architectures: automatic detection of facial expressions using 3D modeling, an LSTM approach for lip reading Devanagari script, comparing change detection algorithms on multispectral imagery for classification, and combining RGB and depth images for indoor scene classification with deep learning. The document also briefly discusses machine learning versus deep learning and popular deep learning frameworks like TensorFlow, Caffe and IBM PowerAI.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
This document summarizes a proposed method for super-resolution of multispectral images using principal component analysis. It begins with background on multispectral imaging and issues with resolution. The proposed method first uses PCA to reduce the dimensionality of the multispectral data. It then learns edge details from a high-resolution database by matching blocks of the principal components. After learning, the modified principal components are inverse transformed to generate a higher resolution multispectral image. The method is tested on real multispectral data sets and shown to reconstruct higher resolution images.
This document proposes a method for remote sensing image retrieval using convolutional neural networks with weighted distance and result re-ranking. It has two stages: 1) An offline stage where a pre-trained CNN is fine-tuned on labeled images to extract features for the retrieval dataset. 2) An online stage where the fine-tuned CNN extracts features from a query image and calculates weighted distances to retrieved images, giving more preference to images from similar classes to the query. Experiments on two datasets show the method improves retrieval performance compared to state-of-the-art methods.
The document discusses a Bayesian approach called localized multi-kernel relevance vector machine (LMK-RVM) that uses multiple kernel functions to perform classification. LMK-RVM allows different kernel functions or parameters to be used in different areas of feature space, providing more flexibility than single-kernel models. It combines multi-kernel learning with the sparsity of the relevance vector machine (RVM) model. The document outlines LMK-RVM and provides examples showing it can improve classification accuracy and potentially provide sparser models compared to single-kernel approaches.
The document discusses integrating support vector machines (SVMs) and Markov random fields (MRFs) for remote sensing image classification. SVMs are good at identifying optimal discriminant hypersurfaces but do not consider context between samples. The paper aims to integrate SVMs and MRFs to allow for contextual classification. A novel classifier is proposed that reformulates the MRF minimum-energy decision rule as an SVM discriminant function with a "contextual kernel." Experimental results on real remote sensing datasets show the proposed method provides significantly more accurate classifications compared to a standard noncontextual SVM.
The document proposes a new method called Kernel-based Dynamic Subspace Method (KDSM) for classifying high-dimensional data. KDSM combines an ensemble technique of support vector machines with an optimal kernel method. It uses a dynamic subspace approach to select informative feature subsets and an optimal algorithm to select parameters for the radial basis function kernel. The method is tested on hyperspectral image data and achieves higher classification accuracy compared to other methods while also reducing computation time, especially for datasets with small training sizes.
SYNOPSIS on Parse representation and Linear SVM.bhavinecindus
1. The document discusses a thesis on using sparse feature parameterization and multi-kernel SVM for large scale scene classification. The objective is to improve accuracy for large datasets using sparse representations and machine learning algorithms.
2. Key challenges include high dimensionality reducing accuracy for large datasets, nonlinear distributions, and computational costs of deep learning models. The research aims to address these issues.
3. The motivation from literature shows that multi-kernel SVMs have proved effective but could be improved by minimizing redundancy and optimizing kernel parameters for feature sets.
An ensemble classification algorithm for hyperspectral imagessipij
Hyperspectral image analysis has been used for many purposes in environmental monitoring, remote
sensing, vegetation research and also for land cover classification. A hyperspectral image consists of many
layers in which each layer represents a specific wavelength. The layers stack on top of one another making
a cube-like image for entire spectrum. This work aims to classify the hyperspectral images and to produce
a thematic map accurately. Spatial information of hyperspectral images is collected by applying
morphological profile and local binary pattern. Support vector machine is an efficient classification
algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature
subjected for classification. Selected features are classified for obtaining the classes and to produce a
thematic map. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. Proposed
method produces accuracy as 93% for Indian Pines and 92% for Pavia University.
Digital image classification is the process of sorting pixels into categories based on their spectral values. There are supervised and unsupervised classification methods. Supervised classification involves using training sites of known categories to define statistical signatures for each class. Unsupervised classification groups pixels into clusters without prior class definitions. Validation is needed to assess classification accuracy by comparing results to ground truth data. Factors like training site selection and signature separability impact classification performance.
Automatic Detection of Window Regions in Indoor Point Clouds Using R-CNNZihao(Gerald) Zhang
The document describes a method to automatically detect window regions in 3D point cloud data of indoor environments collected using a backpack sensor system. The method is based on R-CNN and uses MCG to generate region proposals, extracts features from proposals using a CNN, and classifies proposals as windows or non-windows using a random forest. Experiments on a dataset of 400 images achieved an F1 score of 89.79% and mAP of 96.64% for window detection, outperforming an existing method. Adding a small amount of manually labeled data further improved results.
Object Recogniton Based on Undecimated Wavelet TransformIJCOAiir
Object Recognition (OR) is the mission of finding a specified object in an image or video sequence
in computer vision. An efficient method for recognizing object in an image based on Undecimated Wavelet
Transform (UWT) is proposed. In this system, the undecimated coefficients are used as features to recognize the
objects. The given original image is decomposed by using the UWT. All coefficients are taken as features for
the classification process. This method is applied to all the training images and the extracted features of
unknown object are used as an input to the K-Nearest Neighbor (K-NN) classifier to recognize the object. The
assessment of the system is agreed on using Columbia Object Image Library Dataset (COIL-100) database.
Video Stitching using Improved RANSAC and SIFTIRJET Journal
1. The document discusses techniques for stitching multiple video frames into a panoramic video using Scale-Invariant Feature Transform (SIFT) and an improved RANSAC algorithm.
2. Key points and feature descriptors are extracted from frames using SIFT to find correspondences between frames. The improved RANSAC algorithm is used to estimate homography matrices between frames and filter outlier matches.
3. Frames are blended together to compensate for exposure differences and misalignments before being mapped to a reference plane to create the panoramic video mosaic. The algorithm aims to produce a high quality panoramic video in real-time.
This document summarizes research comparing three machine learning classification methods - Decision Tree, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN) - for classifying land use from high and low resolution satellite imagery. The researchers applied each method to classify Pleiades satellite images of Taiwan and Colorado. SVM achieved the highest overall accuracy of 78.6% for high resolution imagery and 83.3% for low resolution imagery. Decision Trees and k-NN were less accurate. The document outlines the methodology, including image preprocessing, parameter selection, accuracy assessment, and findings.
This document discusses digital image classification and accuracy assessment. It covers topics such as spectral signatures, supervised vs. unsupervised classification, object-based image analysis, and accuracy assessment methods. The key points are:
- Digital image classification uses spectral information from pixels to categorize land cover types based on spectral patterns. Both supervised and unsupervised methods are described.
- Supervised classification uses training samples of known identity to classify pixels, while unsupervised classification uses computer clustering to group spectrally similar pixels without training data.
- Object-based image analysis first segments an image into meaningful image objects before classification, allowing use of texture, shape, and context versus just spectral values.
- Accuracy assessment requires reference data
An Analysis and Comparison of Quality Index Using Clustering Techniques for S...CSCJournals
This document presents a proposed methodology for microarray image segmentation using clustering techniques. The methodology involves three main steps: preprocessing, gridding, and segmentation. Segmentation is performed using an enhanced fuzzy c-means clustering algorithm (EFCMC) that uses neighborhood pixel information and gray levels. EFCMC can accurately detect absent spots and is tolerant to noise. The methodology is tested on real microarray images and its segmentation quality is assessed using a quality index. Results show EFCMC improves the quality index compared to k-means clustering and fuzzy c-means clustering.
Improving the Accuracy of Object Based Supervised Image Classification using ...CSCJournals
A lot of research has been undertaken and is being carried out for developing an accurate classifier for extraction of objects with varying success rates. Most of the commonly used advanced classifiers are based on neural network or support vector machines, which uses radial basis functions, for defining the boundaries of the classes. The drawback of such classifiers is that the boundaries of the classes as taken according to radial basis function which are spherical while the same is not true for majority of the real data. The boundaries of the classes vary in shape, thus leading to poor accuracy. This paper deals with use of new basis functions, called cloud basis functions (CBFs) neural network which uses a different feature weighting, derived to emphasize features relevant to class discrimination, for improving classification accuracy. Multi layer feed forward and radial basis functions (RBFs) neural network are also implemented for accuracy comparison sake. It is found that the CBFs NN has demonstrated superior performance compared to other activation functions and it gives approximately 3% more accuracy.
Similar to Rgb(d) Scene Labeling- features and algorithms (20)
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This presentation mainly summarize the content of "Maximize your Brainpower" Book, it takes about creativity,problem solving, memory and Agility of mind and how to improve and enhance such ability in your mind.
It also give a brief overview about IQ Tests
This document provides background information on Lecico, an Egypt-based company that manufactures tiles and sanitary ware. It discusses Lecico's history dating back to 1959, its corporate structure including principal subsidiaries, social responsibility efforts for employees and the community, and environmental policies. It also analyzes Lecico's external environment, internal strengths and weaknesses, and recommends a strategic direction using various strategic planning tools.
Inar (First Egyptian Tablet) Market ResearchAhmed Taha
In this presentation, I made a market research to determine whether Inar (First Egyptian Tablet) would succeed in the egyptian market or not, in terms of features and market perception and expectations
Bibliotheca Alexandrina vs. Library of CongressAhmed Taha
In this presentation, I compare between Bibliotheca Alexandrina vs. Library of Congress in terms of Information System and E-Commerce. Comparison points Online Services, Technologies and Accessibility.
This presentation gives a general overview about Human Mind, how to use Mind Map technique, types of Mind Map, Mind Map guidelines, It also highlight some of the free tools that can help you with Mind Mapping like Coggle.it , MindMup.com
This presentation describes the Evolution in Nabisco Supply Chain in details (at each step), also It gives an overview about supply chain management solutions
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document discusses various financial measures for evaluating revenue opportunities and projects. It defines types of revenue including new, incremental, and retained revenue. It also discusses metrics like net present value, internal rate of return, payback period, and discounted payback period for assessing projects. Sample calculations are provided to illustrate how to forecast revenues and costs over several quarters and calculate net cash flow. Key considerations in choosing a project include risk, return on investment, net present value, and balancing highest reward with least risk.
The document discusses electronic voting (e-voting) and its advantages over traditional paper voting. It outlines the roles in an e-voting system including voters, election authorities, auditors, and help organizations. The document also discusses key advantages of e-voting systems like integrity of votes being cast, recorded, and counted accurately as well as voter privacy. However, it notes threats to e-voting systems including authority knowledge attacks and issues around maintaining the privacy of voting booths and chain of custody of ballots.
The document discusses the basics of marketing segmentation, targeting, and positioning. It defines segmentation as dividing a market into distinct groups based on demographics, psychographics, behaviors, benefits, and ethnicity. Targeting is described as evaluating the size and growth potential of segments and their compatibility. Positioning is presented as determining the attributes, quality, price, use, and competition for a product or service.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
2. Introduction
Scene labeling challenges
Pipeline
Feature Extraction
Super-pixel formulation and classification
Classifying segmentation tree paths
Classifying super-pixels MRF
Datasets and results
Agenda
3. Scene Labeling
Labeling of each pixel in an image to a certain class
Scene Labeling can be done
Indoors
Label a Sofa in a Bedroom
Label a door in a living room
Outdoors
Label a car in street
Label building in street
Scene Labeling
6. Indoor scene labeling challenges
Large variations of scene types
Lack of distinctive features
Poor illumination
Scene Labeling
7. Benefits of using depth feature in scene labeling
Increased accuracy and robustness
Body pose estimation
3D mapping
Object recognition
3D modeling and interaction
Scene Labeling
9. 1. Extract features using Kernel descriptor (KDES).
2. Aggregate descriptors in dense region into super-
pixels using Efficient match kernels (EMK)
3. Classify super-pixels using Linear support vector
machine (SVM)
4. Label super-pixels by classifying paths of
segmentation tree.
5. Label super-pixels using super-pixel MRF
Pipeline
10. Kernel Descriptors (KDES), a unified framework that
uses different aspects of similarity (kernel) to derive
patch descriptors.
Image gradient
Spin/normal
Color
Depth gradient
Features Extraction (Step 1)
11. Efficient match kernels (EMK) to transform and
aggregate descriptors in a set S (grid locations in the
interior of a superpixel ‘s’).
Super-pixels are not of the same size.
Super-pixel formation (Step 2)
12. Linear Support vector machine (SVM)
Non-probabilistic binary linear classifier.
Classify superpixels (Step 3)
16. Classifying paths in segmentation tree
If we accumulate features over paths, the accuracy
continues to increase to the top level
The initial part of the curves overlap, suggesting there is
little benefit going to superpixels at too fine scales
Contextual Models
18. Superpixel MRF with gPb
standard MRF formulation. We use Graph Cut to find
the labeling that mini- mizes the energy of a pairwise
MRF
Contextual Models (Step 5)
22. Rgb-(d) scene labeling: Features and algorithms
X Ren, L Bo, D Fox - Computer Vision and Pattern
Recognition 2012 - ieeexplore.ieee.org
Context by region ancestry
JJ Lim, P Arbeláez, C Gu, J Malik - Computer Vision, 2009
IEEE 2009 - ieeexplore.ieee.org
References