This document proposes a method for automatically detecting compound structures from multiple hierarchical segmentations of remote sensing images. Compound structures contain spatial arrangements of primitive objects like buildings, trees, and roads. The method models compound structures as probabilistic region processes and learns their appearance and spatial models from training data. Candidate regions are extracted from hierarchical segmentations, and a constrained region selection framework is used to detect compound structure instances by selecting coherent subsets of regions that satisfy constraints. Approximate inference is performed using Markov chain Monte Carlo sampling or quadratic programming under constraints.
This document proposes a method for automatically detecting compound structures from multiple hierarchical image segmentations. Compound structures consist of spatial arrangements of primitive objects. The method models compound structures as probabilistic region processes and learns their characteristics. Candidate regions are extracted from hierarchical segmentations and the most coherent subsets are selected that constitute compound structure instances. The selection is formulated as optimizing a constrained region selection framework.
This document presents a method for classifying road environments using images from a vehicle-mounted camera. It extracts color and texture features from subregions of road images and classifies them using k-NN and artificial neural networks (ANN). For a four-class problem distinguishing off-road, urban, major road, and motorway classes, the accuracy is around 80%. For a two-class problem distinguishing off-road and on-road, the accuracy increases to around 90% using ANN classification. The method achieves a near real-time classification rate of 1Hz by classifying one video frame per second.
The document summarizes research using Cosmo-SkyMed SAR images to automatically extract features and classify land cover in suburban areas. A neural network classifier achieved over 80% accuracy distinguishing four classes (asphalt, vegetation, trees, manmade structures) using backscatter intensity and GLCM texture features. Ongoing work includes optimizing the algorithm and incorporating information from multiple dates, polarizations and a change detection method.
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...IAEME Publication
Aerial laser scanning for creating digital relief models has been widely applied in
Russia for over 20 years. For processing results of aerial laser scanning, it is
necessary to classify cloud of laser points. Classification of cloud of laser points is
performed via commercial software products (produced abroad), which frequently use
refinement algorithms of “Earth” class data, which consider the relief particularities.
The paper puts forward a proprietary algorithm of laser scanning data interpolation,
enabling to remove redundant cloud of laser points of “Earth” class when data
granularity reduces, in the first place, for flat terrain. The paper provides a detailed
stepwise description of algorithm operation, and the results of constructing a digital
relief model on the basis of the cloud of laser points of “Earth” class, processed via
the proposed algorithm.
The retrieval algorithms in remote sensing generally involve complex physical forward models that are nonlinear and computationally expensive to evaluate. Statistical emulation provides an alternative with cheap computation and can be used to calibrate model parameters and to improve computational efficiency of the retrieval algorithms. We introduce a framework of combining dimension reduction of input and output spaces and Gaussian process emulation
technique. The functional principal component analysis (FPCA) is chosen to reduce to the output space of thousands of dimensions by orders of magnitude. In addition, instead of making restrictive assumptions regarding the correlation structure of the high-dimensional input space,
we identity and exploit the most important directions of this space and thus construct a Gaussian process emulator with feasible computation. We will present preliminary results obtained from applying our method to OCO-2 data, and discuss how our framework can be generalized in
distributed systems. This is joint work with Jon Hobbs, Alex Konomi, Pulong Ma, and Anirban Mondal, and Joon Jin Song.
The retrieval algorithms in remote sensing generally involve complex physical forward models that are nonlinear and computationally expensive to evaluate. Statistical emulation provides an alternative with cheap computation and can be used to calibrate model parameters and to improve computational efficiency of the retrieval algorithms. We introduce a framework of combining dimension reduction of input and output spaces and Gaussian process emulation technique. The functional principal component analysis (FPCA) is chosen to reduce to the output space of thousands of dimensions by orders of magnitude. In addition, instead of making restrictive assumptions regarding the correlation structure of the high-dimensional input space, we identity and exploit the most important directions of this space and thus construct a Gaussian process emulator with feasible computation. We will present preliminary results obtained from applying our method to OCO-2 data, and discuss how our framework can be generalized in distributed systems.
The document discusses several methods for hidden surface removal in 3D computer graphics:
1) Back-face detection uses polygon normals and viewing direction to determine if a polygon is facing away from the viewer.
2) Depth-buffer methods like the z-buffer algorithm use a depth buffer to store the depth value of the visible surface at each pixel location.
3) Scan-line methods process all polygons intersecting a scan-line at once before moving to the next scan-line.
This document proposes a method for automatically detecting compound structures from multiple hierarchical image segmentations. Compound structures consist of spatial arrangements of primitive objects. The method models compound structures as probabilistic region processes and learns their characteristics. Candidate regions are extracted from hierarchical segmentations and the most coherent subsets are selected that constitute compound structure instances. The selection is formulated as optimizing a constrained region selection framework.
This document presents a method for classifying road environments using images from a vehicle-mounted camera. It extracts color and texture features from subregions of road images and classifies them using k-NN and artificial neural networks (ANN). For a four-class problem distinguishing off-road, urban, major road, and motorway classes, the accuracy is around 80%. For a two-class problem distinguishing off-road and on-road, the accuracy increases to around 90% using ANN classification. The method achieves a near real-time classification rate of 1Hz by classifying one video frame per second.
The document summarizes research using Cosmo-SkyMed SAR images to automatically extract features and classify land cover in suburban areas. A neural network classifier achieved over 80% accuracy distinguishing four classes (asphalt, vegetation, trees, manmade structures) using backscatter intensity and GLCM texture features. Ongoing work includes optimizing the algorithm and incorporating information from multiple dates, polarizations and a change detection method.
PROPOSED ALGORITHM OF ELIMINATING REDUNDANT DATA OF LASER SCANNING FOR CREATI...IAEME Publication
Aerial laser scanning for creating digital relief models has been widely applied in
Russia for over 20 years. For processing results of aerial laser scanning, it is
necessary to classify cloud of laser points. Classification of cloud of laser points is
performed via commercial software products (produced abroad), which frequently use
refinement algorithms of “Earth” class data, which consider the relief particularities.
The paper puts forward a proprietary algorithm of laser scanning data interpolation,
enabling to remove redundant cloud of laser points of “Earth” class when data
granularity reduces, in the first place, for flat terrain. The paper provides a detailed
stepwise description of algorithm operation, and the results of constructing a digital
relief model on the basis of the cloud of laser points of “Earth” class, processed via
the proposed algorithm.
The retrieval algorithms in remote sensing generally involve complex physical forward models that are nonlinear and computationally expensive to evaluate. Statistical emulation provides an alternative with cheap computation and can be used to calibrate model parameters and to improve computational efficiency of the retrieval algorithms. We introduce a framework of combining dimension reduction of input and output spaces and Gaussian process emulation
technique. The functional principal component analysis (FPCA) is chosen to reduce to the output space of thousands of dimensions by orders of magnitude. In addition, instead of making restrictive assumptions regarding the correlation structure of the high-dimensional input space,
we identity and exploit the most important directions of this space and thus construct a Gaussian process emulator with feasible computation. We will present preliminary results obtained from applying our method to OCO-2 data, and discuss how our framework can be generalized in
distributed systems. This is joint work with Jon Hobbs, Alex Konomi, Pulong Ma, and Anirban Mondal, and Joon Jin Song.
The retrieval algorithms in remote sensing generally involve complex physical forward models that are nonlinear and computationally expensive to evaluate. Statistical emulation provides an alternative with cheap computation and can be used to calibrate model parameters and to improve computational efficiency of the retrieval algorithms. We introduce a framework of combining dimension reduction of input and output spaces and Gaussian process emulation technique. The functional principal component analysis (FPCA) is chosen to reduce to the output space of thousands of dimensions by orders of magnitude. In addition, instead of making restrictive assumptions regarding the correlation structure of the high-dimensional input space, we identity and exploit the most important directions of this space and thus construct a Gaussian process emulator with feasible computation. We will present preliminary results obtained from applying our method to OCO-2 data, and discuss how our framework can be generalized in distributed systems.
The document discusses several methods for hidden surface removal in 3D computer graphics:
1) Back-face detection uses polygon normals and viewing direction to determine if a polygon is facing away from the viewer.
2) Depth-buffer methods like the z-buffer algorithm use a depth buffer to store the depth value of the visible surface at each pixel location.
3) Scan-line methods process all polygons intersecting a scan-line at once before moving to the next scan-line.
The document describes a new GIS tool that classifies lands around selected monuments using texture analysis and machine learning. The tool extracts sub-images around the monument, calculates texture features using GLCM, and classifies the lands using minimum distance classification to identify flat areas for constructing buildings like museums or visitor centers. Key steps include feature extraction using GLCM, calculating metrics like entropy and correlation, and classifying new images based on closest texture feature vectors in the training database.
The document discusses applications of machine learning for robot navigation and control. It describes how surrogate models can be used for predictive modeling in engineering applications like aircraft design. Dimension reduction techniques are used to reduce high-dimensional design parameters to a lower-dimensional space for faster surrogate model evaluation. For robot navigation, regression models on image manifolds are used for visual localization by mapping images to robot positions. Manifold learning is also applied to find low-dimensional representations of valid human hand poses from images to enable easier robot control.
This document discusses using wavelet transforms as a framework for describing inhomogeneity and anisotropy in variational data assimilation. It summarizes some benefits and limitations of using Fourier transforms and discrete wavelet transforms compared to global models of the background error covariance matrix B. The document also provides an overview of work being done to implement wavelet transforms in the ALADIN model, including developing software to estimate the wavelet coefficient error statistics matrix D and designing boundary wavelets.
This document contains exam questions for a Remote Sensing and GIS Applications course. It includes questions about rainfall-runoff relationships and models, scanner systems for remote sensing, key aspects of making effective maps from geospatial data, GIS workflow processes and cognitive models, disadvantages of remotely sensed data and physics concepts related to electromagnetic radiation, and questions about photogrammetry applications, raster data models, and comparing aerial photographs to topographic maps.
This document describes an efficient method for segmenting organized point cloud data using connected components analysis. The method works by assigning integer labels to points in the cloud that are similar according to a comparison function. It can be used for tasks like planar segmentation and tabletop object detection. Planar segmentation works by first computing surface normals and plane equations for each point, then comparing points and merging those that are part of the same plane segment. The method enables real-time segmentation of RGB-D point clouds.
Petrel course Module_1: Import data and management, make simple surfacesMarc Diviu Franco
This document outlines an introduction course to Petrel software. It covers 5 modules: 1) Loading and editing data, 2) Digital mapping, 3) Surface reconstruction and editing, 4) Fault modeling, and 5) Facies modeling. The course will teach important Petrel functions like surface reconstruction, property modeling between horizons, and making grids and horizons. It provides examples of specific tasks like importing elevation data, draping maps, digitizing polygons for mapping, and modeling zones between reconstructed surfaces.
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
The document discusses different mathematical concepts including operations, formulas, shapes, and calculations. It covers topics such as permutations, combinations, fractions, areas, volumes, squares, ellipses, and cyclic quadrilaterals. Formulas are provided for combinations, areas, perimeters, and the sums of sides and diagonals of cyclic quadrilaterals. A variety of mathematical terms and concepts are defined.
The document describes a pipeline for 3D object recognition and 6-DOF pose estimation from an RGB-D image. It involves generating synthetic views for training, extracting global features combining color and geometry, recognizing objects by matching features, and optimizing the estimated pose using ICP. The feature descriptor encodes relationships between point pairs and correlates color and geometry information across segmented regions. The approach achieves 94% recognition rate on online objects and 100% on synthetic views.
4-CONNECTED AND 8-CONNECTED NEIGHBOR SELECTION By Sintiak HaqueSintiak haque
Boundary fill algorithm is used frequently in computer graphics to fill a desired color inside a closed polygon having the same boundary color for all of its sides.Boundary Fill Algorithm starts at a pixel inside the polygon to be filled and paints the interior proceeding outwards towards the boundary. This algorithm works only if the color with which the region has to be filled and the color of the boundary of the region are different.
This document contains exam questions for a Remote Sensing and GIS Applications course. It includes 8 questions related to topics like remote sensing systems, GIS components and data structures, photogrammetry, natural disasters, and watershed drainage patterns. Students are instructed to answer 5 of the 8 questions, which vary in length from short answer to longer explanations and include diagrams/flowcharts. Key remote sensing and GIS concepts covered include raster vs vector data, active vs passive systems, interpolation methods, and using the technologies for applications like flood mapping and groundwater analysis.
User guide of reservoir geological modeling v2.2.0Bo Sun
This is the user guide of DepthInsight™ reservoir geological modeling module. For corresponding video tutorials , please visit and subscribe our Youtube channel: https://www.youtube.com/channel/UCjHyG-mG7NQofUWTZgpBT2w
DepthInsight™ software products include modules as follows:
Structure Interpretation
Well and Data Management
Plan Module
Profile Module
Attribute Modeling
Velocity Modeling
Structural Modeling
Reservoir Geological Modeling
Numerical Simulation Gridding
Rock Modeling
Geo-mechanical Modeling
Paleo-Structural Modeling
Enormous Modeling Platform
For more information about our company, Beijing GridWorld Software Technology Co., Ltd., please visit our website: http://gridworld.com.cn/en/
The document discusses object detection in aerial images using rotated bounding boxes (RBOX). It describes how traditional horizontal bounding boxes (HBOX) are limited for aerial images and introduces RBOX defined by center point, height, angle, and width. It also presents a new "Gliding Vertex" method to calculate RBOX that improves stability over using angle alone. The document outlines the dataset features, preprocessing methods including splitting large images and oversampling rare classes, and an ensemble Faster R-CNN model with RBOX and Feature Pyramid Network that achieved a mAP score of 0.750.
This manual includes very basic features of Remote Sensing like LAYER STACK, BASIC FUNCTION, MULTISPECTRAL BAND COMBINATION& IMAGE ENHANCEMENT, IMAGE PRE-PROCESSING, BASIC IMAGE CORRECTION, NDVI, SUPERVISED AND UNSUPERVISED CLASSIFICATION, MOSAIC, MAP PRODUCTION
This document discusses generalized low rank models, which provide a compressed representation of data tables by approximating them as the product of two smaller numeric tables. This reduces storage space and improves prediction speed while maintaining accuracy. Two examples are described: one where low rank models are used to visualize important stances from walking data, and another where they compress zip code data to predict compliance violations.
The document describes PowerLyra, a system for differentiated graph computation and partitioning on skewed graphs. PowerLyra uses hybrid partitioning and computation strategies to balance locality and parallelism. The hybrid partitioning strategy, called Hybrid-cut, partitions low-degree vertices based on edges (like edge-cut) for locality, and partitions high-degree vertices based on vertices (like vertex-cut) for parallelism. The hybrid computation model processes high-degree vertices in a distributed manner for parallelism, and processes low-degree vertices locally for locality. PowerLyra also includes an optimization called zoning that groups vertices by type to improve data locality during communication.
Generalized low rank models provide a compressed representation of data by identifying important features and representing each data point as a combination of those features. This reduces storage space, speeds up predictions, and helps visualize patterns in the data. Examples show how low rank models can compress walking stance data to identify principal poses and compress zip code data into demographic archetypes to improve compliance predictions across regions.
This document summarizes generalized low rank models (GLRMs), which can find low dimensional structure in large, heterogeneous datasets. GLRMs approximate a data matrix using the product of two lower rank matrices. They generalize techniques like principal component analysis by allowing different loss functions and regularizers. GLRMs can handle a variety of data types, impute missing values, and provide dimensionality reduction. They can be fitted efficiently using alternating minimization or stochastic gradient methods in parallel and distributed implementations.
ILWIS is an acronym for the Integrated Land and Water Information System.
It is a Geographic Information System (GIS) with Image Processing capabilities. ILWIS has been developed by the International Institute for Aerospace Survey and Earth Sciences (ITC), Enschede, The Netherlands.
Hoja informativa sobre el cáncer de cuello UteroPablo Benavides
Hoja informativa sobre la enfermedad de cáncer de cuello de utero, publicada por el Servicio Andaluz de Salud de la Consejería de Salud de la Junta de Andalucía, traducido al árabe por la Mediadora intercultural del Ayuntamiento de Zafarraya Guadalinfo de Zafarraya.
Design for People, Effective Innovation and SustainabilityMusstanser Tinauli
Presentation for the thesis titled, " Designing for people, effective innovation and sustainability: Introducing experiential factors in an observational framework to evaluate technology assisted systems".
The document describes a new GIS tool that classifies lands around selected monuments using texture analysis and machine learning. The tool extracts sub-images around the monument, calculates texture features using GLCM, and classifies the lands using minimum distance classification to identify flat areas for constructing buildings like museums or visitor centers. Key steps include feature extraction using GLCM, calculating metrics like entropy and correlation, and classifying new images based on closest texture feature vectors in the training database.
The document discusses applications of machine learning for robot navigation and control. It describes how surrogate models can be used for predictive modeling in engineering applications like aircraft design. Dimension reduction techniques are used to reduce high-dimensional design parameters to a lower-dimensional space for faster surrogate model evaluation. For robot navigation, regression models on image manifolds are used for visual localization by mapping images to robot positions. Manifold learning is also applied to find low-dimensional representations of valid human hand poses from images to enable easier robot control.
This document discusses using wavelet transforms as a framework for describing inhomogeneity and anisotropy in variational data assimilation. It summarizes some benefits and limitations of using Fourier transforms and discrete wavelet transforms compared to global models of the background error covariance matrix B. The document also provides an overview of work being done to implement wavelet transforms in the ALADIN model, including developing software to estimate the wavelet coefficient error statistics matrix D and designing boundary wavelets.
This document contains exam questions for a Remote Sensing and GIS Applications course. It includes questions about rainfall-runoff relationships and models, scanner systems for remote sensing, key aspects of making effective maps from geospatial data, GIS workflow processes and cognitive models, disadvantages of remotely sensed data and physics concepts related to electromagnetic radiation, and questions about photogrammetry applications, raster data models, and comparing aerial photographs to topographic maps.
This document describes an efficient method for segmenting organized point cloud data using connected components analysis. The method works by assigning integer labels to points in the cloud that are similar according to a comparison function. It can be used for tasks like planar segmentation and tabletop object detection. Planar segmentation works by first computing surface normals and plane equations for each point, then comparing points and merging those that are part of the same plane segment. The method enables real-time segmentation of RGB-D point clouds.
Petrel course Module_1: Import data and management, make simple surfacesMarc Diviu Franco
This document outlines an introduction course to Petrel software. It covers 5 modules: 1) Loading and editing data, 2) Digital mapping, 3) Surface reconstruction and editing, 4) Fault modeling, and 5) Facies modeling. The course will teach important Petrel functions like surface reconstruction, property modeling between horizons, and making grids and horizons. It provides examples of specific tasks like importing elevation data, draping maps, digitizing polygons for mapping, and modeling zones between reconstructed surfaces.
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
The document discusses different mathematical concepts including operations, formulas, shapes, and calculations. It covers topics such as permutations, combinations, fractions, areas, volumes, squares, ellipses, and cyclic quadrilaterals. Formulas are provided for combinations, areas, perimeters, and the sums of sides and diagonals of cyclic quadrilaterals. A variety of mathematical terms and concepts are defined.
The document describes a pipeline for 3D object recognition and 6-DOF pose estimation from an RGB-D image. It involves generating synthetic views for training, extracting global features combining color and geometry, recognizing objects by matching features, and optimizing the estimated pose using ICP. The feature descriptor encodes relationships between point pairs and correlates color and geometry information across segmented regions. The approach achieves 94% recognition rate on online objects and 100% on synthetic views.
4-CONNECTED AND 8-CONNECTED NEIGHBOR SELECTION By Sintiak HaqueSintiak haque
Boundary fill algorithm is used frequently in computer graphics to fill a desired color inside a closed polygon having the same boundary color for all of its sides.Boundary Fill Algorithm starts at a pixel inside the polygon to be filled and paints the interior proceeding outwards towards the boundary. This algorithm works only if the color with which the region has to be filled and the color of the boundary of the region are different.
This document contains exam questions for a Remote Sensing and GIS Applications course. It includes 8 questions related to topics like remote sensing systems, GIS components and data structures, photogrammetry, natural disasters, and watershed drainage patterns. Students are instructed to answer 5 of the 8 questions, which vary in length from short answer to longer explanations and include diagrams/flowcharts. Key remote sensing and GIS concepts covered include raster vs vector data, active vs passive systems, interpolation methods, and using the technologies for applications like flood mapping and groundwater analysis.
User guide of reservoir geological modeling v2.2.0Bo Sun
This is the user guide of DepthInsight™ reservoir geological modeling module. For corresponding video tutorials , please visit and subscribe our Youtube channel: https://www.youtube.com/channel/UCjHyG-mG7NQofUWTZgpBT2w
DepthInsight™ software products include modules as follows:
Structure Interpretation
Well and Data Management
Plan Module
Profile Module
Attribute Modeling
Velocity Modeling
Structural Modeling
Reservoir Geological Modeling
Numerical Simulation Gridding
Rock Modeling
Geo-mechanical Modeling
Paleo-Structural Modeling
Enormous Modeling Platform
For more information about our company, Beijing GridWorld Software Technology Co., Ltd., please visit our website: http://gridworld.com.cn/en/
The document discusses object detection in aerial images using rotated bounding boxes (RBOX). It describes how traditional horizontal bounding boxes (HBOX) are limited for aerial images and introduces RBOX defined by center point, height, angle, and width. It also presents a new "Gliding Vertex" method to calculate RBOX that improves stability over using angle alone. The document outlines the dataset features, preprocessing methods including splitting large images and oversampling rare classes, and an ensemble Faster R-CNN model with RBOX and Feature Pyramid Network that achieved a mAP score of 0.750.
This manual includes very basic features of Remote Sensing like LAYER STACK, BASIC FUNCTION, MULTISPECTRAL BAND COMBINATION& IMAGE ENHANCEMENT, IMAGE PRE-PROCESSING, BASIC IMAGE CORRECTION, NDVI, SUPERVISED AND UNSUPERVISED CLASSIFICATION, MOSAIC, MAP PRODUCTION
This document discusses generalized low rank models, which provide a compressed representation of data tables by approximating them as the product of two smaller numeric tables. This reduces storage space and improves prediction speed while maintaining accuracy. Two examples are described: one where low rank models are used to visualize important stances from walking data, and another where they compress zip code data to predict compliance violations.
The document describes PowerLyra, a system for differentiated graph computation and partitioning on skewed graphs. PowerLyra uses hybrid partitioning and computation strategies to balance locality and parallelism. The hybrid partitioning strategy, called Hybrid-cut, partitions low-degree vertices based on edges (like edge-cut) for locality, and partitions high-degree vertices based on vertices (like vertex-cut) for parallelism. The hybrid computation model processes high-degree vertices in a distributed manner for parallelism, and processes low-degree vertices locally for locality. PowerLyra also includes an optimization called zoning that groups vertices by type to improve data locality during communication.
Generalized low rank models provide a compressed representation of data by identifying important features and representing each data point as a combination of those features. This reduces storage space, speeds up predictions, and helps visualize patterns in the data. Examples show how low rank models can compress walking stance data to identify principal poses and compress zip code data into demographic archetypes to improve compliance predictions across regions.
This document summarizes generalized low rank models (GLRMs), which can find low dimensional structure in large, heterogeneous datasets. GLRMs approximate a data matrix using the product of two lower rank matrices. They generalize techniques like principal component analysis by allowing different loss functions and regularizers. GLRMs can handle a variety of data types, impute missing values, and provide dimensionality reduction. They can be fitted efficiently using alternating minimization or stochastic gradient methods in parallel and distributed implementations.
ILWIS is an acronym for the Integrated Land and Water Information System.
It is a Geographic Information System (GIS) with Image Processing capabilities. ILWIS has been developed by the International Institute for Aerospace Survey and Earth Sciences (ITC), Enschede, The Netherlands.
Hoja informativa sobre el cáncer de cuello UteroPablo Benavides
Hoja informativa sobre la enfermedad de cáncer de cuello de utero, publicada por el Servicio Andaluz de Salud de la Consejería de Salud de la Junta de Andalucía, traducido al árabe por la Mediadora intercultural del Ayuntamiento de Zafarraya Guadalinfo de Zafarraya.
Design for People, Effective Innovation and SustainabilityMusstanser Tinauli
Presentation for the thesis titled, " Designing for people, effective innovation and sustainability: Introducing experiential factors in an observational framework to evaluate technology assisted systems".
Design thiking e gestão de projetos - INFOBRAL 2013Eduardo Freire
O documento apresenta uma palestra sobre Design Thinking e Gestão de Projetos. Resume os principais conceitos de Design Thinking, como focar nas pessoas e empatia. Também discute como aplicar Design Thinking em projetos, com abordagens centradas nas pessoas, negócios e sociedade. Por fim, explica como o Design Thinking pode evoluir a Gestão de Projetos com foco na desejabilidade, viabilidade e praticabilidade dos projetos.
PhD Thesis Defense Presentation - Estudo da viabilidade de fabricação de disp...Alessandro Oliveira
1) O documento descreve estudos sobre a viabilidade de fabricação de dispositivos semicondutores baseados em filmes de carbeto de silício obtidos por PECVD.
2) Foram realizados experimentos de cristalização, corrosão e dopagem dos filmes de carbeto de silício amorfo, bem como a fabricação de estruturas básicas como capacitores e heterojunções.
3) Os resultados indicaram a formação de nanocristais de carbeto de silício cúbico após tratamento térmico e taxas de corrosão
This document discusses the implications of immigration on educational management in Libya. Libya has a small population of around 6 million people, but this number includes non-citizens who migrated to the country for work opportunities following the discovery of oil. Factors like seeking better opportunities and lifestyles can force people to migrate. The education system in Libya follows a 6-3-3 pattern from primary to university level. Universities have increased to accommodate growing student enrollment in higher education. Libya encouraged skilled migration to its education institutions. The research aims to better understand the implications of "learners of immigration" - immigrants who study in Libya - on educational management, and to clarify the advantages and disadvantages.
PALESTRA - Inovação em Gerenciamento de Projetos - Eduardo FreirePapo de Consultor
O documento apresenta produtos e serviços relacionados a gestão de projetos oferecidos por Eduardo Freire, incluindo: (1) soluções customizadas de gerenciamento de projetos com foco em pessoas, métodos e tecnologia; (2) workshops sobre diversos temas como carreira em gestão de projetos e metodologias como PMBOK e Design Thinking; (3) frameworks para gestão de projetos públicos. Além disso, o documento discute conceitos como inovação, projetos, Design Thinking e sua relação com a gestão de projetos.
This document discusses the application of geographic information systems (GIS) techniques to exploration and production (E&P) data management and subsurface interpretation. It covers how GIS provides tools for data organization, visualization, querying, editing, spatial analysis, geoprocessing, and prediction. These capabilities allow GIS to be used across various stages of the E&P lifecycle including exploration, drilling, production, refining, transmission, and data management. The document concludes that using GIS in the oil and gas industry enables better decision making, cost savings and efficiency gains, and improved communication.
Some people are feel uncomfortable to express their views to other. It is also a type of hesitation. Here are the best tips for reduce the fear of public speaking and get bold.So take a look at these ideas of public speaking
A PhD in nursing allows one to advance the field of nursing science through original research. A typical PhD nursing program takes 4-6 years and includes coursework, a research practicum, and a dissertation involving collecting or analyzing original data to answer a new research question. Obtaining a PhD allows nurses to develop the scientific foundation of the discipline, educate future nurses, and improve patient care through applying research findings to clinical practice.
Este documento discute a auto-eficácia dos professores na utilização educativa das tecnologias. Analisa fatores como motivação, competências percebidas e variáveis atitudinais que influenciam a integração tecnológica. Tem como objetivo identificar os principais fatores preditores e mediadores do investimento profissional dos professores nesta área.
This document discusses the threat of radical Islam in Europe and calls for a new Christian reformation and spiritual revival to counter this threat. It argues that secularism and hedonism have weakened Europe's Christian foundations and left it vulnerable to Islamic influence. Statistics are presented showing growing Muslim populations in several European countries. The document warns that if current trends continue, Europe risks losing its culture and freedoms and falling under Islamic law. It calls Christians to wake up to the crisis, engage in evangelism, prayer and biblical teaching to conquer this threat rather than being fearful or complacent.
The Islamisation of Europe - What can be Done to Stop and Reverse ItPeter Hammond
- The document discusses the growing Muslim population in cities in Belgium and the Netherlands, noting that Muslims will likely comprise the majority of Brussels' population by 2030. It describes ways in which Belgian society has become more Islamic through changes to school calendars, holidays, and political policies that accommodate Islamic practices. It expresses concern that some Muslim politicians have vowed to implement Islamic sharia law in Belgium and that proposed legislation could criminalize criticism of Islam.
The document summarizes the reserve estimation of the Titas gas field in Bangladesh using different methods. It describes the location and geology of the field and outlines the objectives to estimate gas initially in place, recoverable reserves, and recovery factor using volumetric, conventional material balance, and flowing gas material balance methods. The results show that the gas initially in place ranges from 6.2 to 11.3 trillion cubic feet depending on the method. The recoverable reserves and recovery factors are also estimated and compared for the different reservoir sands.
Islamization of knowledge: Special Reference to the Discipline of Fiqh and Us...Abu Talib Mohammad Monawer
This document outlines a discussion on Islamizing the disciplines of Fiqh (Islamic jurisprudence) and Usul al-Fiqh (principles of Islamic jurisprudence). It notes that these fields need to be updated and made more relevant to current issues and knowledge. It discusses problems in the classical approaches like neglecting social sciences and political dimensions. It suggests expanding these fields to include new topics like family law, human rights, environment and development. It proposes applying collective ijtihad/consultation to cover new aspects of life. Overall, the document argues that Fiqh and Usul al-Fiqh must be reformed and renewed to solve contemporary challenges.
Different Tools to Detect and Monitor Oil Spills Aerial Observation Tech.A.Tuğsan İşiaçık Çolak
Remote sensing techniques can be used to monitor oil pollution from ships. Aerial observation and satellite imagery are effective tools to detect and monitor oil spills. Aerial observation uses tools like side-looking airborne radar, laser fluorosensors, infrared and ultraviolet sensors to locate oil slicks and map pollution from the air. Satellite synthetic aperture radar and optical sensors can also detect and monitor oil spills from space over large ocean areas. These remote sensing methods are useful for responding to accidents and illegal pollution from ships.
This document discusses the ePortfolio and its role as a tool for individuals. An ePortfolio is a collection of an individual's work and achievements that can be used to demonstrate skills and competencies. ePortfolios provide tools and spaces for people to develop their work over time by overcoming obstacles, articulating their experiences, and engaging in dialogue. They also allow for collaboration and connection with others to support lifelong learning.
International Journal of Engineering Research and DevelopmentIJERD Editor
This document presents a technique for estimating parameters of a deployable mesh reflector antenna using 3D coordinate data and least squares fitting. It involves determining the unknown coefficients of the general quadratic surface equation that best fits the 3D points. The shape of the surface is then estimated as an elliptic paraboloid based on its invariants. Key parameters of the elliptic paraboloid like the focal length are then determined by reconstructing the surface in its standard form based on the estimated coefficients and orientations. Estimating these parameters at different stages of deployment testing can help validate the stability of the antenna surface and placement of its feed.
Special Plenary Lecture at the International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY (VETOMAC), Lisbon, Portugal, September 10 - 13, 2018
http://www.conf.pt/index.php/v-speakers
Propagation of uncertainties in complex engineering dynamical systems is receiving increasing attention. When uncertainties are taken into account, the equations of motion of discretised dynamical systems can be expressed by coupled ordinary differential equations with stochastic coefficients. The computational cost for the solution of such a system mainly depends on the number of degrees of freedom and number of random variables. Among various numerical methods developed for such systems, the polynomial chaos based Galerkin projection approach shows significant promise because it is more accurate compared to the classical perturbation based methods and computationally more efficient compared to the Monte Carlo simulation based methods. However, the computational cost increases significantly with the number of random variables and the results tend to become less accurate for a longer length of time. In this talk novel approaches will be discussed to address these issues. Reduced-order Galerkin projection schemes in the frequency domain will be discussed to address the problem of a large number of random variables. Practical examples will be given to illustrate the application of the proposed Galerkin projection techniques.
Surveillance refers to the task of observing a scene, often for lengthy periods in search of particular objects or particular behaviour. This task has many applications, foremost among them is security (monitoring for undesirable behaviour such as theft or vandalism), but increasing numbers of others in areas such as agriculture also exist. Historically, closed circuit TV (CCTV) surveillance has been mundane and labour Intensive, involving personnel scanning multiple screens, but the advent of reasonably priced fast hardware means that automatic surveillance is becoming a realistic task to attempt in real time. Several attempts at this are underway.
This document describes a code structure for calculating and visualizing electric potential and field from point charges. It discusses:
1) Calculating the potential and electric field at grid points due to multiple point charges using superposition principles.
2) Interpolating sparse potential data to generate smooth 2D potential maps.
3) Representing the electric field as vectors showing position, magnitude, and direction originating from point charges.
The code reads charge and position inputs, calculates potentials and fields on a grid, interpolates the potential data, and outputs files to generate vector maps visualizing the electric potential and field.
A new kind of quantum gates, higher braiding gates, as matrix solutions of the polyadic braid equations (different from the generalized Yang–Baxter equations) is introduced. Such gates lead to another special multiqubit entanglement that can speed up key distribution and accelerate algorithms. Ternary braiding gates acting on three qubit states are studied in detail. We also consider exotic non-invertible gates, which can be related with qubit loss, and define partial identities (which can be orthogonal), partial unitarity, and partially bounded operators (which can be non-invertible). We define two classes of matrices, star and circle ones, such that the magic matrices (connected with the Cartan decomposition) belong to the star class. The general algebraic structure of the introduced classes is described in terms of semigroups, ternary and 5-ary groups and modules. The higher braid group and its representation by the higher braid operators are given. Finally, we show, that for each multiqubit state, there exist higher braiding gates that are not entangling, and the concrete conditions to be non-entangling are given for the obtained binary and ternary gates.
Video surveillance is becoming more and more important forsocial security, law enforcement, social order,military, and other social problems. In order to manage parking information effectively, this vehicle
detection method is presented. In general, motion detection plays an important role in video surveillance
systems. In this paper, firstly this system uses ViBe method to extract the foreground object, then extracts
HOG features on the performance of the ROI of images. At last this paper presents Support vector machine for vehicle recognition. The results of this test show that, the recognition rate of vehicle’s model in this recognition system is up the industrial application standard.
Video surveillance is becoming more and more important forsocial security, law enforcement, social order,military, and other social problems. In order to manage parking information effectively, this vehicle detection method is presented. In general, motion detection plays an important role in video surveillance systems. In this paper, firstly this system uses ViBe method to extract the foreground object, then extracts HOG features on the performance of the ROI of images. At last this paper presents Support vector machine for vehicle recognition. The results of this test show that, the recognition rate of vehicle’s model in this recognition system is up the industrial application standard.
This document summarizes a research paper that presents a method for vehicle recognition using background subtraction and support vector machines (SVM). It first uses the ViBe background subtraction algorithm to extract foreground objects from video surveillance footage of a parking lot. Histogram of oriented gradients (HOG) features are then extracted from regions of interest and fed into an SVM classifier to determine if the objects are vehicles. The system was able to accurately recognize vehicles in testing with a recognition rate meeting industrial standards.
Vehicle Recognition Using VIBE and SVMCSEIJJournal
This document summarizes a research paper on vehicle recognition using the ViBe background subtraction algorithm and support vector machines (SVM). It first describes using ViBe to extract foreground objects from video surveillance footage of a parking lot. Histogram of oriented gradients (HOG) features are then extracted from regions of interest and used to train an SVM classifier to recognize vehicles. The system was able to accurately detect and recognize multiple vehicles simultaneously with results meeting industrial standards.
Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL
Mask R-CNN extends Faster R-CNN by adding a branch for predicting segmentation masks in parallel with bounding box recognition and classification. It introduces a new layer called RoIAlign to address misalignment issues in the RoIPool layer of Faster R-CNN. RoIAlign improves mask accuracy by 10-50% by removing quantization and properly aligning extracted features. Mask R-CNN runs at 5fps with only a small overhead compared to Faster R-CNN.
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Rec...Saif Mahmud
The document proposes a hierarchical self-attention based autoencoder model for open-set human activity recognition using wearable sensor data. The model encodes sensor signals with hierarchical attention at both the window and session level to capture spatial and temporal dependencies. An autoencoder is then used to detect unknown activities based on reconstruction loss thresholds. The model is evaluated on several datasets, demonstrating state-of-the-art performance for window-level and open-set activity recognition. Attention maps also allow interpretability of important sensor locations and time periods.
Convolutional networks and graph networks through kernelstuxette
This presentation discusses how convolutional kernel networks (CKNs) can be used to model sequential and graph-structured data through kernels defined over sequences and graphs. CKNs define feature maps from substructures like n-mers in sequences and paths in graphs into high-dimensional spaces, which are then approximated to obtain low-dimensional representations that can be used for prediction tasks like classification. This approach is analogous to convolutional neural networks and can be extended to multiple layers. The presentation provides examples showing CKNs achieve good performance on problems involving protein sequences and social networks.
This document describes a new machine learning algorithm called the Balancing Board Machine (BBM). BBM approximates the centroid of the polyhedral cone that represents the version space in kernel machines. It does this by computing the centroid of the intersection between the version space cone and a hyperplane, and then iteratively "balancing" the hyperplane towards the centroid. This process improves the generalization performance over support vector machines. The document provides mathematical details on exactly computing polytope centroids and deriving efficient approximation algorithms for implementing BBM.
This document discusses probabilistic error bounds for order reduction of smooth nonlinear models. It begins with motivation for using reduced order models (ROM) in computationally intensive applications and the need for error metrics. It then provides background on Dixon's theory for probabilistic error bounds, which has mostly been used for linear models. The document outlines snapshot and gradient-based reduction algorithms to reduce the response and parameter interfaces of a model. It defines different types of errors that can occur from reducing these interfaces and discusses propagating the errors across interfaces using Dixon's theory. Numerical tests and results are briefly mentioned along with conclusions.
Prpagation of Error Bounds Across reduction interfacesMohammad
This document summarizes the motivation, background, algorithms, and theory behind developing probabilistic error bounds for order reduction of smooth nonlinear models. It discusses how reduced order models (ROM) play an important role in computationally intensive applications and the need to provide error metrics with ROM predictions. It then describes snapshot and gradient-based reduction algorithms used at the response and parameter interfaces, respectively. It introduces different types of errors that can occur from reducing the response space only, parameter space only, or both spaces simultaneously, and how Dixon's theory can be used to estimate these relative errors.
안녕하세요 딥러닝 논문읽기 모임 입니다! 오늘 소개할 논문은 3D관련 업무를 진행 하시는/ 희망하시는 분들의 필수 논문인 VoxelNET 입니다.
발표자료:https://www.slideshare.net/taeseonryu/mcsemultimodal-contrastive-learning-of-sentence-embeddings
안녕하세요! 딥러닝 논문읽기 모임입니다.
오늘은 자율 주행, 가정용 로봇, 증강/가상 현실과 같은 다양한 응용 분야에서 중요한 문제인 3D 포인트 클라우드에서의 객체 탐지에 대한 획기적인 진전을 소개하고자 합니다. 이를 위해 'VoxelNet'이라는 새로운 3D 탐지 네트워크에 대해 알아보겠습니다.
1. 기존 방법의 한계
기존의 많은 노력은 수동으로 만들어진 특징 표현, 예를 들어 새의 눈 시점 투영 등에 집중해 왔습니다. 하지만 이러한 방법들은 LiDAR 포인트 클라우드와 영역 제안 네트워크(RPN) 사이의 연결을 효과적으로 수행하기 어렵습니다.
2. VoxelNet의 혁신적 접근법
VoxelNet은 3D 포인트 클라우드를 위한 수동 특징 공학의 필요성을 없애고, 특징 추출과 바운딩 박스 예측을 단일 단계, end-to-end 학습 가능한 깊은 네트워크로 통합합니다. VoxelNet은 포인트 클라우드를 균일하게 배치된 3D 복셀로 나누고, 새롭게 도입된 복셀 특징 인코딩(VFE) 레이어를 통해 각 복셀 내의 포인트 그룹을 통합된 특징 표현으로 변환합니다.
3. 효과적인 기하학적 표현 학습
이 방식을 통해 포인트 클라우드는 서술적인 체적 표현으로 인코딩되며, 이는 RPN에 연결되어 탐지를 생성합니다. VoxelNet은 다양한 기하학적 구조를 가진 객체의 효과적인 구별 가능한 표현을 학습합니다.
4. 성능 평가
KITTI 자동차 탐지 벤치마크에서의 실험 결과, VoxelNet은 기존의 LiDAR 기반 3D 탐지 방법들을 큰 차이로 능가했습니다. 또한, LiDAR만을 기반으로 한 보행자와 자전거 탐지에서도 희망적인 결과를 보였습니다.
VoxelNet의 도입은 3D 포인트 클라우드에서의 객체 탐지를 혁신적으로 개선하고 있으며, 이 분야에서의 미래 발전에 중요한 영향을 미칠 것으로 기대됩니다.
오늘 논문 리뷰를 위해 이미지처리 허정원님이 자세한 리뷰를 도와주셨습니다 많은 관심 미리 감사드립니다!
https://youtu.be/yCgsCyoJoMg
This document discusses computing canonical labelings of digraphs. It begins by reviewing key concepts like digraphs, adjacency matrices, and isomorphisms. It notes that while many algorithms exist for undirected graphs, computing canonical labelings of digraphs remains challenging. The document then presents several new theoretical concepts for digraph canonical labeling, including mix diffusion degree sequences. It proposes using these concepts to systematically compute canonical labelings and proves several theorems to guide the algorithm. It describes four algorithms for calculating the canonical labeling of a digraph and notes the algorithms have been preliminarily verified through software testing.
Background Estimation Using Principal Component Analysis Based on Limited Mem...IJECEIAES
Given a video of 푀 frames of size ℎ × 푤. Background components of a video are the elements matrix which relative constant over 푀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 푤 × 푀) into 2 dimensions one (푁 × 푀), where 푁 is a linear array of size ℎ × 푤 . The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
AU QP Answer key NOv/Dec 2015 Computer Graphics 5 sem CSEThiyagarajan G
This document contains a summary of a computer graphics exam with 10 multiple choice questions in Part A and 4 long answer questions in Part B. Some of the key topics covered include: image resolution, scaling matrices, color conversion between RGB and CMY color modes, Bezier curves, projection planes, dithering, animation principles, turtle attributes in graphics, Bresenham's circle algorithm, Liang-Barsky line clipping algorithm, viewing transformations, cubic Bezier curves, and backface detection. Part B also includes questions on orthographic vs axonometric vs oblique projections, ambient lighting models, raster vs keyframe animation, ray tracing, and morphing.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Compound Structure Detection
1. AUTOMATIC DETECTION OF
COMPOUND STRUCTURES
FROM MULTIPLE HIERARCHICAL
SEGMENTATIONS
H¨useyin G¨okhan Akc¸ay
Department of Computer Engineering, Bilkent University, Bilkent, 06800, Ankara
akcay@cs.bilkent.edu.tr
21 Sept. 2016
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 1 / 78
2. Motivation
Large scale global content about the Earth.
Small local details (upto 30 cm resolution).
1.6 terabytes of data by the ESA’s multispectral
high-resolution imaging satellite.
The WorldView-2 satellite collects 975,000 square
kilometers of imagery per day.
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 2 / 78
3. Motivation
A challenging problem in remote sensing image mining is
the detection of heterogeneous compound structures such
as different types of residential, industrial, and agricultural
areas.
Compound structures are comprised of spatial
arrangements of simple primitive objects such as buildings,
trees and road segments.
Detection of compound structures is a challenging problem
because
They contain thousands of primitive objects.
They mostly do not have distinctive features.
Primitives can arrange in many different combinations in the
overhead view.
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 3 / 78
4. Motivation
Figure: 75 × 75m2 compound structures in WorldView-2 images.
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 4 / 78
5. Literature Review
Primitive object detection.
Residential-factory buildings, local roads, vehicles, airplanes
and boats.
Window-based approaches.
Bag-of-words representation.
Enforces artificial boundaries on the image.
Assumes the whole window corresponds to a compound
structure.
Segmentation-based approaches.
The grouping criteria do not involve spatial arrangements.
Graph-based approaches.
Specific arrangements such as alignment and parallelism.
Structural graph matching.
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 5 / 78
6. Problem Definition
We propose a generic method for the modeling and
detection of compound structures.
Target structures can involve arrangements of an unknown
number of different types of primitive objects.
The detection task is formulated as the selection of multiple
coherent subsets of candidate regions obtained from
multiple hierarchical segmentations.
To avoid over- or under-segmentation of candidate regions,
we search for the most meaningful regions at different
scales.
We propose a constrained region selection framework
which allows to specify global constraints on the selected
regions.
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 6 / 78
8. Compound Structure Model
Primitive Representation
Compound structures are composed of spatial
arrangements of multiple, relatively homogeneous, and
compact primitive objects.
We assume that a compound structure V consists of R
layers of primitive object maps, V = r=1,...,R Vr
.
Figure: Primitive object layers.
Each primitive object vi is represented by an ellipse
vi = (li, si, θi).
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 8 / 78
9. Compound Structure Model
Spatial Arrangement Model
For a given compound structure consisting of N primitive
objects, we construct a neighborhood graph G = (V, E).
V = {v1, . . . , vN} correspond to the individual primitive
objects,
E = r1,r2=1,...,R Er1r2 where Er1r2 denotes the edges
between the vertices at layers Vr1 and Vr2 .
Figure: Neighborhood graph construction for multiple primitive layers.
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 9 / 78
10. Compound Structure Model
Spatial Arrangement Model
For each (vi, vj) ∈ E, we compute the following five features:
Distance between the
closest pixels, φ1(vi, vj),
Relative orientation,
φ2(vi, vj),
φ2
φ3
φ1 φ4
Angle between the line joining the centroids of the two
objects and the major axis of vi as the reference object,
φ3(vi, vj),
Distance between the closest antipodal pixels that lie on the
major axes, φ4(vi, vj),
Relative size, φ5(vi, vj).
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 10 / 78
11. Compound Structure Model
Spatial Arrangement Model
We also compute the following four individual features for
each primitive object vi:
Area, φ6(vi),
Eccentricity, φ7(vi),
Solidity, φ8(vi),
Regularity, φ9(vi).
H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 11 / 78
12. Compound Structure Model
Spatial Arrangement Model
A one-dimensional marginal histogram Hr1r2
k (Er1r2 ) is
constructed for each pairwise feature φk , k = 1, . . . , 5,
computed over all edges for each pair of layers Vr1 and Vr2 .
Also, a one-dimensional marginal histogram Hr
k (Vr
) is
constructed for each individual feature φk , k = 6, . . . , 8,
computed over all vertices at each layer Vr
.
The concatenation H(V) of all marginal histograms is used
as a non-parametric approximation to the distribution of the
primitive objects in the compound structure.
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13. Compound Structure Model
Spatial Arrangement Model
Figure: Example histograms for the building layers of four different
types of compound structures.
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14. Compound Structure Model
Probabilistic Region Processes
Each primitive object vi (i.e., the ellipse parameters) is
considered a vector-valued random variable.
A compound structure is represented by a set of random
variables that leads to a region process.
The region process is governed by the Gibbs distribution
p(V|β) =
1
Zv
exp βT
H(V) (1)
where β is the parameter vector controlling each histogram
bin, and Zv is the partition function.
A region process is equivalent to a Markov random field
(MRF).
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15. Learning
Maximum Likelihood Estimation
Suppose that we observe a set of i.i.d. region processes
V = {V1, . . . , VM}.
We can estimate a compound structure model via
maximum likelihood estimation (MLE) of β by maximizing
(β|V) =
M
m=1
log p(Vm|β). (2)
The gradient of the log-likelihood is given by
d (β|V)
dβ
= Ep[H(V)] −
1
M
N
m=1
H(Vm). (3)
We use the stochastic gradient ascent algorithm where the
expectation Ep[H(V)] is approximated by a finite sum of
histograms of samples V(s), s = 1, . . . , S.
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16. Learning
Maximum Likelihood Estimation
Figure: An example iteration for updating β corresponding to the
relative orientation histogram bins.
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17. Learning
Sampling Region Processes
(a) (b) t = 0 (c) t = 50
(d) t = 200 (e) t = 600 (f) t = 1000
Figure: Illustration of the Gibbs sampler for two primitive layers.
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18. Inference and Region Selection
Hierarchical Region Extraction
Given a compound structure model with learned parameter
vector β, we would like to automatically detect all of its
instances in an input image.
The detection problem is posed as the selection of multiple
subgroups of candidate regions coming from multiple
hierarchical segmentations.
Figure: Hierarchical segmentation trees for two primitive layers.
Each selected group of regions constitutes an instance of
the example compound structure in the large image.
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19. Inference and Region Selection
Hierarchical Region Extraction
Given a compound structure model with learned parameter
vector β, we would like to automatically detect all of its
instances in an input image.
The detection problem is posed as the selection of multiple
subgroups of candidate regions coming from multiple
hierarchical segmentations.
Figure: Hierarchical segmentation trees for two primitive layers.
Each selected group of regions constitutes an instance of
the example compound structure in the large image.
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20. Inference and Region Selection
Hierarchical Region Extraction
The first step is the identification of candidate regions for
each layer Vr
by using a hierarchical segmentation
algorithm.
The next step is to connect the potentially related vertices
at all levels to represent the neighbor relationships.
Within-level edges (⊆ Er1r2 , r1 = r2): Voronoi tessellations.
Between-level edges (⊆ Er1r2 , r1 = r2): Ancestor-descendant
relations.
Between-layer edges (⊆ Er1r2 , r1 = r2): Proximity-based
neighbors.
Figure: Hierarchical segmentation trees for two primitive layers.
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21. Inference and Region Selection
Hierarchical Region Extraction
The first step is the identification of candidate regions for
each layer Vr
by using a hierarchical segmentation
algorithm.
The next step is to connect the potentially related vertices
at all levels to represent the neighbor relationships.
Within-level edges (⊆ Er1r2 , r1 = r2): Voronoi tessellations.
Between-level edges (⊆ Er1r2 , r1 = r2): Ancestor-descendant
relations.
Between-layer edges (⊆ Er1r2 , r1 = r2): Proximity-based
neighbors.
Figure: Hierarchical segmentation trees for two primitive layers.
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22. Inference and Region Selection
Hierarchical Region Extraction
Figure: Graph construction for two primitive layers (i.e., building and
pool). The hierarchical candidate regions at three and two levels for
these layers are shown in red and light blue, respectively. The edges
that represent parent-child relationship for both layers are shown.
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23. Inference and Region Selection
Hierarchical Region Extraction
Figure: Graph construction for two primitive layers (i.e., building and
pool). The edges that represent the within- and between-level
neighbor relationship within the same layer are shown.
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24. Inference and Region Selection
Hierarchical Region Extraction
Figure: Graph construction for two primitive layers (i.e., building and
pool). The edges that represent the within- and between-level
neighbor relationship between the layers are shown. For better
visualization of edges, only 20 percent of all between-layer edges are
shown.
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25. Inference and Region Selection Inference without Constraints
Bayesian Formulation
Given a graph G = (V, E), the problem can be formulated
as the selection of a subset V∗
among all regions V as
V∗
= arg max
V ⊆V
p(V |I) = arg max
V ⊆V
p(I|V )p(V ) (4)
where p(I|V ) is the observed spectral data likelihood for
the compound structure in the image, and p(V ) acts as the
spatial prior according to the learned appearance and
arrangement model.
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26. Inference and Region Selection Inference without Constraints
CRF Formulation
We formulate the selection problem in (4) using a
conditional random field (CRF).
Let X = {x1, . . . , xM} where xi ∈ {0, 1}, i = 1, . . . , M, be the
set of indicator variables associated with the vertices V of G
so that xi = 1 implies region vi being selected.
Our CRF formulation defines a posterior distribution as
p(X|I, V) ∝ p(I|X, V)p(X, V)
=
1
Zx
vi ∈V
exp ψc
i + ψs
i xi
(vi ,vj )∈E
exp ψa
ij xixj . (5)
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27. Inference and Region Selection Inference without Constraints
CRF Formulation
The vertex bias terms ψc
and ψs
representing color and
shape, respectively, and edge weights ψa
representing
arrangement are defined as
ψc
i =
−1
2
(yi − µr
)T
(Σr
)−1
(yi − µr ), ∀vi ∈ Vr
, r = 1, . . . , R
(6)
ψs
i =
9
k=6
βr
k,Ir
k
φk (vi )
, ∀vi ∈ Vr
, r = 1, . . . , R
(7)
ψa
ij =
5
k=1
βr1r2
k,I
r1r2
k
φk (vi ,vj )
, ∀(vi, vj) ∈ E, r1, r2 = 1, . . .
(8)
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28. Inference and Region Selection Inference without Constraints
CRF Inference
Selecting V∗
in (4) is equivalent to estimating the joint MAP
labels given by
X∗
= arg max
X
p(X|I, V). (9)
Exact inference of the CRF formulation is intractable in
general graphs.
An approximate solution can be obtained by a Markov chain
Monte Carlo sampler.
We developed a sampling algorithm that samples the labels
of many variables at once.
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29. Inference and Region Selection Inference without Constraints
CRF Inference
Figure: Illustration of the primitive sampling procedure.
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30. Inference with Constraints
Our objective is to obtain the maximum probability
estimates of the indicator variables, xi, i = 1, . . . , N
satisfying convex inequality and equality constraints.
We reformulate the problem as quadratic programming
under convex constraints.
The problem in Equation (4) can be rewritten as
V∗
= arg max
V ⊆V
V ⊆Ω
p(V |I) = arg max
V ⊆V
V ⊆Ω
p(I|V )p(V ). (10)
where Ω ∈ RN
is a nonempty polyhedral convex set
determined by a set of constraints.
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31. Inference with Constraints
Quadratic Programming Formulation
For a V ⊆ V, log p(V |β∗
) can be written as follows
log p(V |β) =
5
k=1
R
r1=1
R
r2=1 (vi ,vj )∈Er1r2
βr1r2
k,I
r1r2
k
φk (vi ,vj )
xixj
+
9
k=6
R
r=1 vi ∈Vr
βr
k,Ir
k
φk (vi )
xi − log ZX .
(11)
where ZX is the partition function.
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32. Inference with Constraints
Quadratic Programming Formulation
Let W = 5
k=1
R
r1=1
R
r2=1 Wr1r2
k where each Wr1r2
k is an
N × N affinity matrix.
Each element of this matrix is calculated as
Wr1r2
k (i, j) = −βr1r2
k,I
r1r2
k
φk (vi ,vj )
.
Also, let q = 9
k=6
R
r=1 qr
k where each qr
k is an N × 1
potential vector.
Each element of this vector is calculated as
qr
k (i) = βr
k,Ir
k
φk (vi )
.
The problem can be formulated as
minimize
x
− log p(V |β) =
1
2
XT
WX + qT
X + log ZX
subject to X ∈ Ω,
X ∈ {0, 1}.
(12)
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33. Inference with Constraints
DC Programming Inference
The problem can be formulated as
minimize
x
− log p(V |β) =
1
2
XT
WX + qT
X + log ZX
subject to X ∈ Ω,
X ∈ {0, 1}.
(13)
First, a linear programming relaxation is applied to the 0 − 1
integer program so that 0 ≤ x ≤ 1.
Since W is not assumed positive semidefinite, the resulting
linearly constrained quadratic problem is not convex.
The objective function can be reformulated as a difference
of two convex functions.
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34. Inference with Constraints
Difference of Convex Programming
Formulation
A Difference of Convex (DC) problem is defined as
P = min f(X) = g(X) − h(X) : X ∈ RN
(14)
where g : RN
→ R and h : RN
→ R are convex functions.
Consider the dual program
D = min f∗
(Y) = h∗
(Y) − g∗
(Y) : Y ∈ RN
(15)
where g∗
is the conjugate function of g.
An iterative primal-dual algorithm constructs two alternating
sequences {X(t)
} and {Y(t)
} such that
g(X(t)) − h(X(t)) and g∗(Y(t)) − h∗(Y(t)) are decreasing,
converging to the optimal solutions, X∗ and Y∗, to the primal
and dual problems, respectively.
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35. Inference with Constraints
DC Programming Inference
Let W = QΛQT
be the eigenvalue decomposition of W, and
Λ+
= diag(Λ+
1 , . . . , Λ+
N ) (respectively, Λ−
= diag(Λ−
1 , . . . , Λ−
N ))
be the positive semidefinite diagonal matrix (respectively,
negative semidefinite diagonal matrix) of Λ.
We rewrite the nonconvex symmetric quadratic objective
function as
1
2
XT
WX + qT
X = g(X) − h(X)
g(X) =
1
2
XT
W+
X + qT
X + χΩ(X)
h(X) = −
1
2
XT
W−
X
(16)
where W+
= QΛ+
QT
, W−
= QΛ−
QT
, and χΩ(X) is the
space enclosed by the constraints X ∈ {0, 1} and Ω.
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36. Inference with Constraints
Experiments
We present detailed results of four different kinds of
experiments:
1 Using a single layer without imposing any constraint.
2 Using multiple layers without imposing any constraint.
3 Using a single layer by imposing constraints.
4 Using multiple layers by imposing constraints.
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37. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Figure: 2500 × 4000 pixels Ankara data set.
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38. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Table: Detection scenarios for the experiments. Example primitives
used for learning the compound structure model for each scenario are
shown in a different color. The number of polygons and buildings in
the validation data are also given.
Scenario 1 2 3 4 5 6
Example
primitives
# polygons 162 98 48 195 60 16
# buildings 1519 870 1117 1796 771 219
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39. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Figure: Candidate regions hierarchy.
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40. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Figure: Marginal probabilities for the first scenario.
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41. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Figure: Marginal probabilities for the second scenario.
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42. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Figure: Marginal probabilities for the third scenario.
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43. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Figure: Marginal probabilities for the fourth scenario.
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44. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
Figure: Marginal probabilities for the fifth scenario.
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45. Experiments Unconstrained Single-layer Experiments
Results-Urban Structures
*
Figure: Marginal probabilities for the sixth scenario.
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52. Experiments Unconstrained Single-layer Experiments
Results-Orchards
Figure: Example results for the detection of orchards in the subimage
on the left column. The right column shows the corresponding
marginal probabilities of the selected regions (the copper colormap) as
well as the discarded input candidate regions (white).
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53. Experiments Unconstrained Single-layer Experiments
Results-Orchards
Figure: Example results for the detection of orchards in the subimage
on the left column. The right column shows the corresponding
marginal probabilities of the selected regions (the copper colormap) as
well as the discarded input candidate regions (white).
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54. Experiments Unconstrained Single-layer Experiments
Results-Orchards
Figure: Example results for the detection of orchards. The left column
shows the marginal probabilities at the end of selection. The right
column shows the thresholded detections overlayed as red.
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55. Experiments Unconstrained Single-layer Experiments
Results-Refugee Camps
Figure: 1102 × 971 pixels Darfur data set.
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56. Experiments Unconstrained Single-layer Experiments
Results-Refugee Camps
Figure: Example results for the detection of refugee camps as rural
structures.
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57. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
Figure: 3000 × 8000 pixels Kusadasi data set.
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58. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
Figure: (Up) Examples of local details of red building rooftops. (Down)
An example hierarchy.
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59. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
The selection algorithm that used only the building layer
could not detect several housing estates.
The idea was to add a pool layer that can provide additional
cues for finding the missed buildings.
The initial model was extended by learning the
arrangements of buildings with respect to pools as well.
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60. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
Figure: (Up) Candidate regions. (Down) Selected regions from the
building and pool layers.H. G. Akc¸ay Compound Structure Detection 21 Sept. 2016 58 / 78
61. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
Table: The number of candidate and detected regions for single and
multi-layer selection scenarios.
Single-layer Multi-layer
Candidates Detected Candidates Detected
Building 67,983 11,173 67,983 11,871
Pool - - 16,276 436
Total 67,983 11,173 84,259 12,307
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62. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
Figure: Samples obtained by the selection procedure ran on single
and multiple layers.
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63. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
(a) (b)
Figure: The selected regions using (a) only the building layer. (b)
building and pool layers. Newly detected housing estates that was
missed with single layer selection is enclosed by a red convex hull.
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64. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
(a) (b)
Figure: The selected regions using (a) only the building layer. (b)
building and pool layers. Newly detected housing estates that was
missed with single layer selection is enclosed by a red convex hull.
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65. Experiments Unconstrained Multi-layer Experiments
Results-Housing Estates
Figure: Ground view of a missed housing estate with single layer
selection.
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67. Experiments
Problem Definition
Unconstrained selection involved overlapping regions at
different levels of the hierarchy.
To overcome this problem, we require at most one region
should be selected per path where a path corresponds to
the set of vertices from a leaf to the root.
Formally, we select an optimal subset V∗
⊆ V such that
∀a, b ∈ V∗
, a ∈ descendant(b) and b ∈ descendant(a).
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68. Experiments
Constrained Single-layer Experiments
Let A be a |P| × |V| matrix.
P denotes all the paths from the leaves to the roots of the
input hierarchical forest.
A(i, j) = 1 implies vi ∈ pj ∈ P.
The problem can be reformulated as
minimize
x
1
2
XT
WX + qT
X + log ZX
subject to AX ≤ 1,
0 ≤ X ≤ 1.
(17)
The resulting problem is solved by the DC inference
algorithm.
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69. Experiments
Results-Urban Structures
Table: The number of selected regions for unconstrained and
constrained selection scenarios.
# cand.s 70,644 70,644 70,644 70,644 70,644 22,195
Uncnstr. 3191 1828 3819 3201 2027 1612
Cnstr. 1485 856 2562 1740 811 263
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70. Experiments
Results-Urban Structures
(a) (b)
Figure: Zoomed detection examples. (a) shows the RGB image for a
300 × 300 sub-scene. (b) shows the hierarchy of candidate regions
(two-level hierarchy from bottom to top).
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71. Experiments
Results-Urban Structures
(a)
(b)
Figure: Zoomed detection examples. (a) shows the RGB image for a
300 × 300 sub-scene. (b) shows the hierarchy of candidate regions
(six-level hierarchy from bottom to top).
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72. Experiments
Constrained Multi-layer Experiments
The last set of experiments uses two primitive layers and
enforces geometrical constraints between them.
We search for nearby alike buildings and green areas where
each building group must have a green area in the middle.
We strictly require that the distance between the centroid of
the centroids of a selected group of similar buildings and
the centroid of a selected large green area cannot exceed a
distance threshold δ .
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73. Experiments
Constrained Multi-layer Experiments
Let V1
and V2
represent the building and green area layers.
The desired set of regions can be selected by
minimize
x
1
2
XT
WX + qT
X + log ZX
subject to AX ≤ 1,
1
k1
vi ∈V1
sh
i xi −
1
k2
vj ∈V2
sh
j xj ≤ δ
1
k1
vi ∈V1
sw
i xi −
1
k2
vj ∈V2
sw
j xj ≤ δ
vi ∈V1
xi = k1
vj ∈V2
xj = k2
0 ≤ X ≤ 1.
(18)
where (sh
i , sw
i ) is the centroid of region vi, k1 and k2 denote
the number of buildings and green areas to be selected.
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74. Experiments Constrained Multi-layer Experiments
Results-Buildings & Green Areas
Figure: 2500 × 4000 pixels Ankara data set.
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75. Experiments Constrained Multi-layer Experiments
Results-Buildings & Green Areas
Figure: Selected regions for the green areas surrounded by buildings.
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76. Experiments Constrained Multi-layer Experiments
Results-Buildings & Green Areas
(a) RGB (b) Building candidates
(c) Green candi-
dates
(d) Selection (e) Overlay
Figure: A zoomed detection example for k1 = 4, k2 = 1.
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77. Experiments Constrained Multi-layer Experiments
Results-Buildings & Green Areas
(a) RGB (b) Building candidates
(c) Green candi-
dates
(d) Selection (e) Overlay
Figure: A zoomed detection example for k1 = 6, k2 = 1.
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78. Experiments Constrained Multi-layer Experiments
Results-Buildings & Green Areas
(a) k1 = 4, fVal = −2.95 (b) k1 = 8, fVal = −3.23
(c) k1 = 6, fVal = −3.16 (d) k1 = 4, fVal = −2.97
Figure: Zoomed detection examples for different values of k1 = 4, 6, 8.
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79. Experiments
Summary & Conclusions
We described a generic method for the modeling and
detection of compound structures that consisted of
arrangements of mostly unknown number of primitives.
The modeling process built an MRF-based contextual
model for the compound structure of interest.
The detection task involved a combinatorial selection
problem where multiple subsets of candidate regions from
multiple hierarchical segmentations were selected.
We also handled hard constraints on the candidate regions.
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80. Experiments
Summary & Conclusions
Experiments using urban, industrial, agricultural and rural
structures showed that the proposed method can provide
good localization of instances of compound structures.
The multi-layered experiments showed that selection of
some objects required the selection of objects in other
layers that had spatial relation with them.
One of the most important bottlenecks in terms of accuracy
was the errors in the input hierarchical segmentations.
Future work includes
Using the detection results for adjusting wrong segmentation
results.
Inferring the primitive objects inside a compound structure.
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