This document describes a method for nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares. It introduces radial basis function networks for modeling nonlinear mixing and describes how the network is trained by selecting radial basis function centers using orthogonal least squares to reduce complexity. It then explains how constrained abundance estimation is performed for inversion using the trained network. Simulation results are presented using both synthetic data and a real hyperspectral image to demonstrate the approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Ph.D. Thesis Presentation: A Study of Priors and Algorithms for Signal Recove...Shunsuke Ono
This document summarizes a dissertation on developing new priors and algorithms for signal recovery problems solved via convex optimization. Chapter 4 proposes a blockwise low-rank prior called the Block Nuclear Norm (BNN) to better model texture patterns in images. BNN represents textures as locally low-rank blocks under different shears. Chapter 5 introduces the Local Color Nuclear Norm (LCNN) prior to promote the color-line property and reduce color artifacts in restored images. Chapter 6 develops a hierarchical convex optimization algorithm using primal-dual splitting to solve problems with non-unique solutions and non-strictly convex objectives.
Active Strokes: Coherent Line Stylization for Animated 3D ModelsPierre Bénard
These slides presents a method for creating coherently animated line drawings that include strong abstraction and stylization effects. These effects are achieved with active strokes: 2D contours that approximate and track the lines of an animated 3D scene. Active strokes perform two functions: they connect and smooth unorganized line samples, and they carry coherent parameterization to support stylized rendering. Line samples are approximated and tracked using active contours ("snakes") that automatically update their arrangment and topology to match the animation. Parameterization is maintained by brush paths that follow the snakes but are independent, permitting substantial shape abstraction without compromising fidelity in tracking. This approach renders complex models in a wide range of styles at interactive rates, making it suitable for applications like games and interactive illustrations.
Nonlinear Transformation Based Detection And Directional Mean Filter to Remo...IJMER
In this paper, a novel two stage algorithm for the removal of random valued impulse noise
from the images is presented. In the first stage the noise pixels are detected by using an exponential
nonlinear function. The transformation of the pixels increases the gap between noisy and noise free
candidates which leads to an efficient detection. In the second stage, the directional differences between
the pixels in the four main directions are calculated. The mean values of the pixels which lie in the
direction of minimum difference are calculated and the noisy pixel values are replaced with the mean
value of the pixels lying in the direction of minimum difference. Experimental results show that proposed
method is superior to the conventional methods in peak signal to noise ratio.
Accelarating Optical Quadrature Microscopy Using GPUsPerhaad Mistry
1) Phase unwrapping is used in optical quadrature microscopy to determine viability of embryos by counting cells after unwrapping. It needs to be done at near real-time speeds to analyze sample changes.
2) The paper implements minimum LP norm phase unwrapping and affine transformations on a GPU to improve performance and latency for optical microscopy research.
3) Performance results show a 5.24x speedup for total phase unwrapping time compared to a serial CPU implementation. Further optimizations like multi-GPU support could improve speeds for higher image acquisition rates.
This document presents a non-linear fully-constrained spectral unmixing method with three main steps: 1) Endmember extraction using graph-based manifold learning and N-findR to obtain geodesic distances on the data manifold, 2) Unmixing via a distance-geometry based algorithm using the geodesic distances to find abundances, 3) Testing on Cuprite hyperspectral data shows significant deviations from linear unmixing results but is difficult to fully evaluate without ground truth non-linearly mixed data. Future work includes more testing and evaluation on synthetic non-linearly mixed datasets.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A New Technique of Extraction of Edge Detection Using Digital Image Processing IJMER
Digital image Processing is one of the basic and important tool in the image processing and
computer vision. In this paper we discuss about the extraction of a digital image edge using different
digital image processing techniques. Edge detection is the most common technique for detecting
discontinuities in intensity values. The input image or actual image have some noise that may cause the
of quality of the digital image. Firstly, wavelet transform is used to remove noises from the image
collected. Secondly, some edge detection operators such as Differential edge detection, Log edge
detection, canny edge detection and Binary morphology are analyzed. And then according to the
simulation results, the advantages and disadvantages of these edge detection operators are compared. It
is shown that the Binary morphology operator can obtain better edge feature. Finally, in order to gain
clear and integral image profile, the method of ordering closed is given. After experimentation, edge
detection method proposed in this paper is feasible.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Ph.D. Thesis Presentation: A Study of Priors and Algorithms for Signal Recove...Shunsuke Ono
This document summarizes a dissertation on developing new priors and algorithms for signal recovery problems solved via convex optimization. Chapter 4 proposes a blockwise low-rank prior called the Block Nuclear Norm (BNN) to better model texture patterns in images. BNN represents textures as locally low-rank blocks under different shears. Chapter 5 introduces the Local Color Nuclear Norm (LCNN) prior to promote the color-line property and reduce color artifacts in restored images. Chapter 6 develops a hierarchical convex optimization algorithm using primal-dual splitting to solve problems with non-unique solutions and non-strictly convex objectives.
Active Strokes: Coherent Line Stylization for Animated 3D ModelsPierre Bénard
These slides presents a method for creating coherently animated line drawings that include strong abstraction and stylization effects. These effects are achieved with active strokes: 2D contours that approximate and track the lines of an animated 3D scene. Active strokes perform two functions: they connect and smooth unorganized line samples, and they carry coherent parameterization to support stylized rendering. Line samples are approximated and tracked using active contours ("snakes") that automatically update their arrangment and topology to match the animation. Parameterization is maintained by brush paths that follow the snakes but are independent, permitting substantial shape abstraction without compromising fidelity in tracking. This approach renders complex models in a wide range of styles at interactive rates, making it suitable for applications like games and interactive illustrations.
Nonlinear Transformation Based Detection And Directional Mean Filter to Remo...IJMER
In this paper, a novel two stage algorithm for the removal of random valued impulse noise
from the images is presented. In the first stage the noise pixels are detected by using an exponential
nonlinear function. The transformation of the pixels increases the gap between noisy and noise free
candidates which leads to an efficient detection. In the second stage, the directional differences between
the pixels in the four main directions are calculated. The mean values of the pixels which lie in the
direction of minimum difference are calculated and the noisy pixel values are replaced with the mean
value of the pixels lying in the direction of minimum difference. Experimental results show that proposed
method is superior to the conventional methods in peak signal to noise ratio.
Accelarating Optical Quadrature Microscopy Using GPUsPerhaad Mistry
1) Phase unwrapping is used in optical quadrature microscopy to determine viability of embryos by counting cells after unwrapping. It needs to be done at near real-time speeds to analyze sample changes.
2) The paper implements minimum LP norm phase unwrapping and affine transformations on a GPU to improve performance and latency for optical microscopy research.
3) Performance results show a 5.24x speedup for total phase unwrapping time compared to a serial CPU implementation. Further optimizations like multi-GPU support could improve speeds for higher image acquisition rates.
This document presents a non-linear fully-constrained spectral unmixing method with three main steps: 1) Endmember extraction using graph-based manifold learning and N-findR to obtain geodesic distances on the data manifold, 2) Unmixing via a distance-geometry based algorithm using the geodesic distances to find abundances, 3) Testing on Cuprite hyperspectral data shows significant deviations from linear unmixing results but is difficult to fully evaluate without ground truth non-linearly mixed data. Future work includes more testing and evaluation on synthetic non-linearly mixed datasets.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A New Technique of Extraction of Edge Detection Using Digital Image Processing IJMER
Digital image Processing is one of the basic and important tool in the image processing and
computer vision. In this paper we discuss about the extraction of a digital image edge using different
digital image processing techniques. Edge detection is the most common technique for detecting
discontinuities in intensity values. The input image or actual image have some noise that may cause the
of quality of the digital image. Firstly, wavelet transform is used to remove noises from the image
collected. Secondly, some edge detection operators such as Differential edge detection, Log edge
detection, canny edge detection and Binary morphology are analyzed. And then according to the
simulation results, the advantages and disadvantages of these edge detection operators are compared. It
is shown that the Binary morphology operator can obtain better edge feature. Finally, in order to gain
clear and integral image profile, the method of ordering closed is given. After experimentation, edge
detection method proposed in this paper is feasible.
Computationally Efficient NMRF model based Texture SynthesisArnab Sinha
The document outlines Arnab Sinha's Ph.D. research work on fast texture synthesis algorithms based on non-parametric Markov random field (NMRF) models. The research aims to address problems with existing NMRF texture synthesis algorithms related to high computational complexity. Key contributions include developing new methods for order estimation from the Fourier domain, reducing dimensionality to lower computational complexity, revisiting order estimation, and inverse texture synthesis. Future work may explore additional dimensionality reduction techniques and faster neighborhood search methods.
Chebyshev Functional Link Artificial Neural Networks for Denoising of Image C...IDES Editor
Here we have presented an alternate ANN
structure called functional link ANN (FLANN) for image
denoising. In contrast to a feed forward ANN structure i.e.
a multilayer perceptron (MLP), the FLANN is basically a
single layer structure in which non-linearity is introduced
by enhancing the input pattern with nonlinear function
expansion. In this work three different expansions is
applied. With the proper choice of functional expansion in
a FLANN , this network performs as good as and in some
case even better than the MLP structure for the problem
of denoising of an image corrupted with Salt and Pepper
noise. In the single layer functional link ANN (FLANN)
the need of hidden layer is eliminated. The novelty of this
structure is that it requires much less computation than
that of MLP. In the presence of additive white Gaussian
noise in the image, the performance of the proposed
network is found superior to that of a MLP .In particular
FLANN structure with Chebyshev functional expansion
works best for Salt and Pepper noise suppression from an
image.
The Birefringent Property of an Optical Resinfor the Study of a Stress Field ...ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Optical sensing techniques and signal processing 5ali alavi
This document discusses frequency analysis of optical imaging systems. It begins with a generalized treatment of imaging systems and defines the amplitude transfer function and impulse response. For coherent systems, the image is a convolution of the ideal image and amplitude impulse response. For incoherent systems, the image intensity is a convolution with the intensity impulse response, which is the squared amplitude impulse response. It then examines the frequency responses of coherent and incoherent systems, defining the amplitude transfer function and optical transfer function respectively.
SHADOW DETECTION USING TRICOLOR ATTENUATION MODEL ENHANCED WITH ADAPTIVE HIST...ijcsit
Shadows create significant problems in many computer vision and image analysis tasks such as object
recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish
between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a
shadow region as an object. So the detection of shadows in images will enhance the performance of many
machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor
Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of
intensity attenuation of pixels in the shadow region which is different for the three color channels. It
originates from the idea that if the minimum attenuated color channel is subtracted from the maximum
attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will
be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the
contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the
quality of the detected shadow region.
Modeling and optimization of high index contrast gratings with aperiodic topo...Milan Maksimovic
"Modeling and optimization of high index contrast gratings with aperiodic topologies", in Modeling Aspects in Optical Metrology IV, Bernd Bodermann, Editors, Proceedings of SPIE Vol. 8789 (SPIE, Bellingham, WA 2013), 87890L
Deferred Pixel Shading on the PLAYSTATION®3Slide_N
This document summarizes a deferred pixel shading algorithm implemented on the PlayStation 3 system. The algorithm runs pixel shaders on the Synergistic Processing Elements of the Cell processor concurrently with the GPU for rendering images. Experimental results found that running the pixel shading on 5 SPEs achieved a performance of up to 85Hz at 720p resolution, comparable to running on a high-end GPU. This indicates that the Cell processor can effectively enhance GPU performance by offloading pixel shading work.
Design and optimization of compact freeform lens array for laser beam splitti...Milan Maksimovic
"Design and optimization of compact freeform lens array for laser beam splitting: a case study in optimal surface representation", in Optical Modelling and Design III, Frank Wyrowski; John T. Sheridan; Jani Tervo; Youri Meuret, Editors, Proceedings of SPIE Vol. 9131 (SPIE, Bellingham, WA 2014), 913107.
Moving Cast Shadow Detection Using Physics-based Features (CVPR 2009)Jia-Bin Huang
This document summarizes a research paper about detecting moving cast shadows in videos. It presents an approach that uses physics-based color features and Gaussian mixture models (GMMs) to model shadows. First, it derives normalized spectral ratios as global color features under assumptions of constant ambient lighting and common spectral power distributions of direct light sources. A single GMM then models these global features to characterize shadows. Additionally, pixel-based GMMs describe local gradient intensity distortions to further differentiate shadows similar to backgrounds. The pixel GMMs are updated through confidence-rated learning to accelerate convergence without needing many foreground activities.
This document compares geometry-based Doppler ambiguity resolution methods for squint synthetic aperture radar (SAR) and presents an indirect scheme for estimating Doppler rate in low-contrast scenes. It discusses squint SAR geometry and challenges of estimating Doppler parameters. It then describes conventional, iterative, and improved Radon transform geometry-based methods for resolving Doppler ambiguity, noting the improved schemes are faster. Finally, it presents a method to indirectly estimate Doppler rate in low-contrast scenes by first estimating it in high-contrast areas and using the inverse relationship between Doppler rate and range.
1. The document examines wind patterns around Rishiri Island, Japan using SAR and weather data. It finds evidence of low-level jets forming in the lee of the island under stable atmospheric conditions.
2. The winds are classified into four types based on 115 SAR images. The classification depends on a non-dimensional mountain height parameter and stratification.
3. Strong wind streaks are sometimes observed extending from headlands in Hokkaido under stratified southerly flows. Layer-dependent orographic forcing from the headlands may explain the coexistence of gap winds and wind streaks.
The document discusses Radial Basis Function (RBF) networks. It describes the architecture of an RBF network which has three layers - an input layer, a hidden layer of radial basis functions, and a linear output layer. It also discusses types of radial basis functions like Gaussian, training algorithms for determining hidden unit centers and radii, and provides an example of how an RBF network can learn the XOR problem.
This document discusses combining space-based active microwave (CloudSat) and passive microwave (PMW) observations to improve global snowfall estimates. CloudSat uses radar to detect snowfall but has limitations without additional data. PMW provides constraints by relating brightness temperature depression to ice water path. The authors aim to use optimal estimation to relate CloudSat radar reflectivity measurements to snowfall rate, incorporating prior particle properties from ground observations. This combined approach could produce improved snowfall retrievals by leveraging the strengths of both active and passive microwave sensors.
This document describes using radial basis function networks (RBF) for well log data inversion. It proposes modifying a conventional two-layer RBF and introducing a three-layer RBF. Simulation experiments show the three-layer 10-27-9-10 RBF model achieves the smallest error in testing simulated well log data. When applied to real well log data, the 10-27-9-10 RBF model provides an acceptable inversion of true formation conductivity from apparent conductivity measurements.
The document summarizes the technical program for IGARSS 2011, an international conference on remote sensing. It provides details on the conference topics and sessions, numbers of submitted and accepted papers, and outreach activities. The conference covered various remote sensing topics including analysis techniques, applications to land, atmosphere, oceans and environment. Special sessions focused on the 2011 Japan earthquake and Canadian remote sensing. Over 2200 papers were submitted with around 1500 being presented orally or as posters.
The document summarizes a seven-week experiment that looked at the differential phase behavior of a sandy soil in response to varying moisture content using C-band SAR. Large phase changes over 180 degrees were observed in the absence of physical surface movement as the soil dried from 40% to 10% moisture content. A linear relationship was found between phase and soil moisture over a range from 0.16 to 0.38 within a spatially averaged 1.5m x 1.5m region. The entire phase history could be explained by approximately 1cm changes in the depth of the reflecting layer, though the phase signal is likely an interference effect between surface and volume returns.
Dokumen tersebut membahas tentang pengertian Internet, layanan yang tersedia di Internet seperti email dan World Wide Web, budaya Internet, dan cara mengakses Internet baik secara komersial maupun di tempat umum seperti warnet dan perpustakaan. Dokumen ini juga membahas dampak positif Internet terhadap pemerintahan dan ekonomi di Indonesia.
An ensemble classification algorithm for hyperspectral imagessipij
Hyperspectral image analysis has been used for many purposes in environmental monitoring, remote
sensing, vegetation research and also for land cover classification. A hyperspectral image consists of many
layers in which each layer represents a specific wavelength. The layers stack on top of one another making
a cube-like image for entire spectrum. This work aims to classify the hyperspectral images and to produce
a thematic map accurately. Spatial information of hyperspectral images is collected by applying
morphological profile and local binary pattern. Support vector machine is an efficient classification
algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature
subjected for classification. Selected features are classified for obtaining the classes and to produce a
thematic map. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. Proposed
method produces accuracy as 93% for Indian Pines and 92% for Pavia University.
Spectral unmixing is used to analyze mixed pixels in hyperspectral satellite data. It identifies the individual materials present in each pixel (endmembers) and their proportional abundances. The linear mixing model assumes endmembers combine linearly based on their fractional area coverage. Nonlinear mixing may also occur if materials are intimately mixed. Identifying endmember spectra, extracting mixed pixel spectra, and computing mixing fractions using least squares fitting allows estimation of the proportional abundances of each endmember in each pixel. Key assumptions include having identified all relevant endmembers and their contributions being proportional. Spectral unmixing is most effective when endmembers are spectrally distinct.
This document discusses the use of neural networks to model potential energy surfaces for applications in catalysis. It provides a brief history of artificial neural networks and describes how they can be used to mimic the brain and model nonlinear relationships through networks of neurons. The document specifically discusses how Behler-Parrinello applied neural networks to model potential energy surfaces by using symmetry functions as inputs to element-specific neural networks to characterize the chemical environment of each atom. This approach allows neural networks to model potential energy surfaces across different supercell sizes and shapes.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document discusses techniques for interactive hair rendering under environment lighting. It proposes representing the environment lighting as a sum of spherical radial basis functions (SRBFs). SRBFs can approximate spherical functions and represent individual lights. The transmittance of hair is computed using a deep opacity map technique. Multiple scattering is modeled by convolving SRBF lights with a scattering function. This allows interactive rendering of hair under complex environment lighting.
Computationally Efficient NMRF model based Texture SynthesisArnab Sinha
The document outlines Arnab Sinha's Ph.D. research work on fast texture synthesis algorithms based on non-parametric Markov random field (NMRF) models. The research aims to address problems with existing NMRF texture synthesis algorithms related to high computational complexity. Key contributions include developing new methods for order estimation from the Fourier domain, reducing dimensionality to lower computational complexity, revisiting order estimation, and inverse texture synthesis. Future work may explore additional dimensionality reduction techniques and faster neighborhood search methods.
Chebyshev Functional Link Artificial Neural Networks for Denoising of Image C...IDES Editor
Here we have presented an alternate ANN
structure called functional link ANN (FLANN) for image
denoising. In contrast to a feed forward ANN structure i.e.
a multilayer perceptron (MLP), the FLANN is basically a
single layer structure in which non-linearity is introduced
by enhancing the input pattern with nonlinear function
expansion. In this work three different expansions is
applied. With the proper choice of functional expansion in
a FLANN , this network performs as good as and in some
case even better than the MLP structure for the problem
of denoising of an image corrupted with Salt and Pepper
noise. In the single layer functional link ANN (FLANN)
the need of hidden layer is eliminated. The novelty of this
structure is that it requires much less computation than
that of MLP. In the presence of additive white Gaussian
noise in the image, the performance of the proposed
network is found superior to that of a MLP .In particular
FLANN structure with Chebyshev functional expansion
works best for Salt and Pepper noise suppression from an
image.
The Birefringent Property of an Optical Resinfor the Study of a Stress Field ...ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Optical sensing techniques and signal processing 5ali alavi
This document discusses frequency analysis of optical imaging systems. It begins with a generalized treatment of imaging systems and defines the amplitude transfer function and impulse response. For coherent systems, the image is a convolution of the ideal image and amplitude impulse response. For incoherent systems, the image intensity is a convolution with the intensity impulse response, which is the squared amplitude impulse response. It then examines the frequency responses of coherent and incoherent systems, defining the amplitude transfer function and optical transfer function respectively.
SHADOW DETECTION USING TRICOLOR ATTENUATION MODEL ENHANCED WITH ADAPTIVE HIST...ijcsit
Shadows create significant problems in many computer vision and image analysis tasks such as object
recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish
between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a
shadow region as an object. So the detection of shadows in images will enhance the performance of many
machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor
Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of
intensity attenuation of pixels in the shadow region which is different for the three color channels. It
originates from the idea that if the minimum attenuated color channel is subtracted from the maximum
attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will
be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the
contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the
quality of the detected shadow region.
Modeling and optimization of high index contrast gratings with aperiodic topo...Milan Maksimovic
"Modeling and optimization of high index contrast gratings with aperiodic topologies", in Modeling Aspects in Optical Metrology IV, Bernd Bodermann, Editors, Proceedings of SPIE Vol. 8789 (SPIE, Bellingham, WA 2013), 87890L
Deferred Pixel Shading on the PLAYSTATION®3Slide_N
This document summarizes a deferred pixel shading algorithm implemented on the PlayStation 3 system. The algorithm runs pixel shaders on the Synergistic Processing Elements of the Cell processor concurrently with the GPU for rendering images. Experimental results found that running the pixel shading on 5 SPEs achieved a performance of up to 85Hz at 720p resolution, comparable to running on a high-end GPU. This indicates that the Cell processor can effectively enhance GPU performance by offloading pixel shading work.
Design and optimization of compact freeform lens array for laser beam splitti...Milan Maksimovic
"Design and optimization of compact freeform lens array for laser beam splitting: a case study in optimal surface representation", in Optical Modelling and Design III, Frank Wyrowski; John T. Sheridan; Jani Tervo; Youri Meuret, Editors, Proceedings of SPIE Vol. 9131 (SPIE, Bellingham, WA 2014), 913107.
Moving Cast Shadow Detection Using Physics-based Features (CVPR 2009)Jia-Bin Huang
This document summarizes a research paper about detecting moving cast shadows in videos. It presents an approach that uses physics-based color features and Gaussian mixture models (GMMs) to model shadows. First, it derives normalized spectral ratios as global color features under assumptions of constant ambient lighting and common spectral power distributions of direct light sources. A single GMM then models these global features to characterize shadows. Additionally, pixel-based GMMs describe local gradient intensity distortions to further differentiate shadows similar to backgrounds. The pixel GMMs are updated through confidence-rated learning to accelerate convergence without needing many foreground activities.
This document compares geometry-based Doppler ambiguity resolution methods for squint synthetic aperture radar (SAR) and presents an indirect scheme for estimating Doppler rate in low-contrast scenes. It discusses squint SAR geometry and challenges of estimating Doppler parameters. It then describes conventional, iterative, and improved Radon transform geometry-based methods for resolving Doppler ambiguity, noting the improved schemes are faster. Finally, it presents a method to indirectly estimate Doppler rate in low-contrast scenes by first estimating it in high-contrast areas and using the inverse relationship between Doppler rate and range.
1. The document examines wind patterns around Rishiri Island, Japan using SAR and weather data. It finds evidence of low-level jets forming in the lee of the island under stable atmospheric conditions.
2. The winds are classified into four types based on 115 SAR images. The classification depends on a non-dimensional mountain height parameter and stratification.
3. Strong wind streaks are sometimes observed extending from headlands in Hokkaido under stratified southerly flows. Layer-dependent orographic forcing from the headlands may explain the coexistence of gap winds and wind streaks.
The document discusses Radial Basis Function (RBF) networks. It describes the architecture of an RBF network which has three layers - an input layer, a hidden layer of radial basis functions, and a linear output layer. It also discusses types of radial basis functions like Gaussian, training algorithms for determining hidden unit centers and radii, and provides an example of how an RBF network can learn the XOR problem.
This document discusses combining space-based active microwave (CloudSat) and passive microwave (PMW) observations to improve global snowfall estimates. CloudSat uses radar to detect snowfall but has limitations without additional data. PMW provides constraints by relating brightness temperature depression to ice water path. The authors aim to use optimal estimation to relate CloudSat radar reflectivity measurements to snowfall rate, incorporating prior particle properties from ground observations. This combined approach could produce improved snowfall retrievals by leveraging the strengths of both active and passive microwave sensors.
This document describes using radial basis function networks (RBF) for well log data inversion. It proposes modifying a conventional two-layer RBF and introducing a three-layer RBF. Simulation experiments show the three-layer 10-27-9-10 RBF model achieves the smallest error in testing simulated well log data. When applied to real well log data, the 10-27-9-10 RBF model provides an acceptable inversion of true formation conductivity from apparent conductivity measurements.
The document summarizes the technical program for IGARSS 2011, an international conference on remote sensing. It provides details on the conference topics and sessions, numbers of submitted and accepted papers, and outreach activities. The conference covered various remote sensing topics including analysis techniques, applications to land, atmosphere, oceans and environment. Special sessions focused on the 2011 Japan earthquake and Canadian remote sensing. Over 2200 papers were submitted with around 1500 being presented orally or as posters.
The document summarizes a seven-week experiment that looked at the differential phase behavior of a sandy soil in response to varying moisture content using C-band SAR. Large phase changes over 180 degrees were observed in the absence of physical surface movement as the soil dried from 40% to 10% moisture content. A linear relationship was found between phase and soil moisture over a range from 0.16 to 0.38 within a spatially averaged 1.5m x 1.5m region. The entire phase history could be explained by approximately 1cm changes in the depth of the reflecting layer, though the phase signal is likely an interference effect between surface and volume returns.
Dokumen tersebut membahas tentang pengertian Internet, layanan yang tersedia di Internet seperti email dan World Wide Web, budaya Internet, dan cara mengakses Internet baik secara komersial maupun di tempat umum seperti warnet dan perpustakaan. Dokumen ini juga membahas dampak positif Internet terhadap pemerintahan dan ekonomi di Indonesia.
An ensemble classification algorithm for hyperspectral imagessipij
Hyperspectral image analysis has been used for many purposes in environmental monitoring, remote
sensing, vegetation research and also for land cover classification. A hyperspectral image consists of many
layers in which each layer represents a specific wavelength. The layers stack on top of one another making
a cube-like image for entire spectrum. This work aims to classify the hyperspectral images and to produce
a thematic map accurately. Spatial information of hyperspectral images is collected by applying
morphological profile and local binary pattern. Support vector machine is an efficient classification
algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature
subjected for classification. Selected features are classified for obtaining the classes and to produce a
thematic map. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. Proposed
method produces accuracy as 93% for Indian Pines and 92% for Pavia University.
Spectral unmixing is used to analyze mixed pixels in hyperspectral satellite data. It identifies the individual materials present in each pixel (endmembers) and their proportional abundances. The linear mixing model assumes endmembers combine linearly based on their fractional area coverage. Nonlinear mixing may also occur if materials are intimately mixed. Identifying endmember spectra, extracting mixed pixel spectra, and computing mixing fractions using least squares fitting allows estimation of the proportional abundances of each endmember in each pixel. Key assumptions include having identified all relevant endmembers and their contributions being proportional. Spectral unmixing is most effective when endmembers are spectrally distinct.
This document discusses the use of neural networks to model potential energy surfaces for applications in catalysis. It provides a brief history of artificial neural networks and describes how they can be used to mimic the brain and model nonlinear relationships through networks of neurons. The document specifically discusses how Behler-Parrinello applied neural networks to model potential energy surfaces by using symmetry functions as inputs to element-specific neural networks to characterize the chemical environment of each atom. This approach allows neural networks to model potential energy surfaces across different supercell sizes and shapes.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document discusses techniques for interactive hair rendering under environment lighting. It proposes representing the environment lighting as a sum of spherical radial basis functions (SRBFs). SRBFs can approximate spherical functions and represent individual lights. The transmittance of hair is computed using a deep opacity map technique. Multiple scattering is modeled by convolving SRBF lights with a scattering function. This allows interactive rendering of hair under complex environment lighting.
Divide the examined window into cells (e.g. 16x16 pixels for each cell).
2- For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, leftmiddle,
left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counterclockwise.
3- Where the center pixel's value is greater than the neighbor's value, write "1". Otherwise,
write "0". This gives an 8-digit binary number (which is usually converted to decimal for
convenience).
4- Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e.,
each combination of which pixels are smaller and which are greater than the center).
This document describes six MATLAB projects available from VenSoft Technologies for the 2014-2015 academic year. It provides the titles, abstracts, publishing details, and index terms for each project. The projects involve topics such as sparse unmixing of hyperspectral data, mixed noise removal, subspace matching pursuit, exploiting spectral a priori information, gradient histogram preservation for image denoising, and image set-based collaborative representation for face recognition.
SIGGRAPH 2014 Course on Computational Cameras and Displays (part 4)Matthew O'Toole
Recent advances in both computational photography and displays have given rise to a new generation of computational devices. Computational cameras and displays provide a visual experience that goes beyond the capabilities of traditional systems by adding computational power to optics, lights, and sensors. These devices are breaking new ground in the consumer market, including lightfield cameras that redefine our understanding of pictures (Lytro), displays for visualizing 3D/4D content without special eyewear (Nintendo 3DS), motion-sensing devices that use light coded in space or time to detect motion and position (Kinect, Leap Motion), and a movement toward ubiquitous computing with wearable cameras and displays (Google Glass).
This short (1.5 hour) course serves as an introduction to the key ideas and an overview of the latest work in computational cameras, displays, and light transport.
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
In the near future, there is an eminent demand for High Resolution images. In order to fulfil this
demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more
Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR
image in that set and combine the information into a single HR image. Conventional interpolation methods can
produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome
the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically
verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily,
outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable
for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim.
Image fusion technology is also used to fuse two processed images obtained through the algorithm
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
Abstract: In the near future, there is an eminent demand for High Resolution images. In order to fulfil this demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR image in that set and combine the information into a single HR image. Conventional interpolation methods can produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily, outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim. Image fusion technology is also used to fuse two processed images obtained through the algorithm. Keywords: Super Resolution, Interpolation, EESM, Image Fusion
The information and mutual information ration for counting image features and...alikhajegili
This document discusses information ratio (IR) and mutual information ratio (MIR) as methods for estimating the number of image features and feature matches.
IR is defined based on image channel histograms and self-information. MIR similarly uses joint histograms and mutual information. Lower bounds on IR (LIR) and MIR (LMIR) are also proposed based on entropy.
Numerical experiments evaluate IR and MIR on standard datasets using SURF, KAZE and ORB features. Results show these features follow the IR curve and extract fewer features than estimated by IR and LIR. Optimization of feature extraction based on IR is also shown to outperform standard algorithms. Future work is proposed to further evaluate M
A broad ranging open access journal Fast and efficient online submission Expe...ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Neural Inverse Rendering for General Reflectance Photometric Stereo (ICML 2018)Tatsunori Taniai
Our physics-embedded neural network approach to photometric stereo:
1) Uses an autoencoder with two streams - one to estimate surface normals from images and another to reconstruct images using the estimated normals.
2) Implements an unsupervised learning approach using a reconstruction loss function without needing ground truth surface normal data.
3) Incorporates a weak supervision prior in early training to stabilize learning, which is removed later.
4) Outperforms other methods on real-world scenes, achieving state-of-the-art results for general reflectance photometric stereo.
Presentación de la Universidad de Granada sobre cambios de color en escenarios naturales debidos a la interacción entre luz y atmósfera, realizada durante las jornadas HOIP 2010 organizadas por la Unidad de Sistemas de Información e Interacción TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
The document summarizes a new algorithm for noise reduction and quality improvement in SAR interferograms using inpainting and diffusion. It presents an inpainting-diffusion algorithm to improve interferogram quality and DEM accuracy. It then describes applying the Complex Ginzburg-Landau equation to the inpainting scheme for SAR interferogram restoration. The algorithm uses inpainting to fill in discarded phase values below a threshold of coherence. It evaluates the algorithm's performance using Signal-to-Noise Ratio on an interferogram of Ariano Irpino, Italy.
DIP ch 11 part 2 Feature Extraction By Mohammad Pooya MalekMohammadPooya Malek
The document discusses several methods for extracting features from images for purposes like pattern recognition and image matching. It describes spectral approaches using Fourier analysis to identify periodic textures. It covers moment invariants which are features that remain unchanged with translations, rotations, or scaling. It also explains principal component analysis which transforms correlated variables into linearly uncorrelated variables called principal components. The scale-invariant feature transform (SIFT) algorithm and Harris corner detector are also summarized.
Neural Radiance Fields (NeRF) represents scenes as neural radiance fields that can be used for novel view synthesis. NeRF learns a continuous radiance field from a sparse set of input views using a multi-layer perceptron that maps 5D coordinates to RGB color and density values. It uses volumetric rendering to integrate these values along camera rays and optimizes the network via differentiable rendering and a reconstruction loss. NeRF produces high-fidelity novel views and has inspired extensions like handling dynamic scenes and reconstructing scenes from unstructured internet photos.
This document proposes using a deep belief network (DBN) to learn depth perception from optical flow information. It describes:
1) Using motion parallax and optical flow cues to perceive depth in humans and insects.
2) Generating labeled training data from 3D graphics scenes to teach the DBN the mapping from motion to depth.
3) The DBN architecture, which takes motion energy maps as input and uses multiple hidden layers and backpropagation to predict depth maps.
4) Test results showing the DBN achieves a higher R^2 score for depth prediction than other models like linear regression.
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODELgrssieee
1) The document describes a segmentation algorithm for polarimetric SAR (PolSAR) data that can model both scalar-texture and multi-texture scattering.
2) The algorithm uses log-cumulants and hypothesis testing to determine whether a scalar-texture or dual-texture model best fits the data within each segment.
3) The algorithm is tested on simulated multi-texture PolSAR data and is shown to accurately segment the classes and estimate their texture parameters. However, when applied to real data sets, the algorithm only finds the simpler scalar-texture case.
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...grssieee
This document discusses using wavelet transforms to analyze two-point statistics of polarimetric synthetic aperture radar (PolSAR) data. It introduces wavelet variance and kurtosis as metrics that can be applied to PolSAR data transformed using a wavelet frame. It then provides an example of applying this analysis to ALOS PALSAR data over Hawaii's Papau Seamount to characterize sea surface features.
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIESgrssieee
The Sentinel-1 mission is part of the GMES program and consists of two satellites to provide C-band SAR data for emergency response, marine and land monitoring, and other applications. The satellites operate in a near-polar orbit with a 12 day repeat cycle. The main acquisition mode is an interferometric wide swath mode with 5m range and 20m azimuth resolution over a 250km swath. Sentinel-1 will support operational services and create a long-term SAR data archive.
The document summarizes the status of the GMES Space Component program. It describes the Sentinel satellite missions for monitoring land, ocean, atmosphere and emergency situations. The Sentinels will provide long-term data continuity as well as improved coverage compared to existing missions. Sentinel data will be freely and openly available to both operational users and the science community. The program is on track, with the first Sentinel launches beginning in 2013.
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETERgrssieee
The document describes the progress of the development of CFOSAT SCAT, a Ku-band scatterometer onboard the Chinese-French Oceanography Satellite (CFOSAT). CFOSAT will measure global ocean surface winds and waves to improve weather forecasting, ocean dynamics modeling, climate research, and understanding of surface processes. The SCAT instrument is a rotating fan-beam radar scatterometer that will retrieve wind vectors using measurements of backscatter at incidence angles from 26 to 46 degrees. It has a wide swath of over 1000km and specifications are designed to achieve high-precision wind measurements globally. System details including parameters and the operation mode are provided.
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...grssieee
The document describes the SAP4PRISMA project which aims to develop algorithms and products to support the Italian hyperspectral PRISMA Earth observation mission. The project will focus on data processing, quality assessment, classification methods, and generating level 3 and 4 products for applications like land monitoring, agriculture, and hazard monitoring. It will include the generation of "PRISMA-like" synthetic test data to support algorithm development and validation. The research will be carried out across multiple work packages focusing on topics like data quality, classification methods, calibration/validation, and developing applicative products.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different sites, including a Zambian woodland and North Carolina forest sites.
3) Time series of Hyperion data at flux tower sites show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different forest, grassland, and woodland sites globally.
3) Time series of Hyperion data at sites in Zambia, North Carolina, and Kansas show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release measured by flux towers.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
EO-1/Hyperion has been collecting hyperspectral imagery for over 12 years, acquiring over 65,000 scenes. Researchers have been using these data to develop and validate algorithms for estimating vegetation properties like fraction of absorbed photosynthetically active radiation (fAPAR) and photochemical reflectance index (PRI). Comparisons of Hyperion data to field measurements at flux tower sites show these algorithms can accurately track vegetation changes over time and relate spectral properties to productivity metrics like light use efficiency and gross ecosystem productivity. This work is helping prototype data products for the upcoming HyspIRI mission.
This document is a return and exchange form for a wetsuit company. It provides instructions for customers to fill out when returning an undamaged item for a refund, exchange, or size change. The form requests information like the customer's order details, contact information, the suit being returned and its size, the reason for return, and if applicable, the new desired size. It also provides the return shipping address and notifies customers that the company is not responsible for lost or damaged return packages.
This document provides instructions for clients of Fox Tax Planning and Preparation for preparing to have their taxes filed. It lists important income and deduction documentation to bring to an appointment, such as W-2s, 1099s, receipts for donations. It also includes an engagement letter detailing the services to be provided, responsibilities of both parties, fees, and electronic filing and signature procedures. Clients are asked to sign the letter agreeing to the terms and return it along with their tax information.
The document discusses mapping wetlands in North America using MODIS 500m imagery. It describes wetlands and existing global wetland databases. The methodology uses MODIS data from 2008, digital elevation models, and reference data to classify wetlands into three types - forest/shrub dominant wetlands, herbaceous dominant wetlands, and sea grass dominant wetlands. Training data is collected from existing land cover maps and Landsat imagery. A decision tree model and maximum likelihood classification are applied to extract wetlands from other land covers.
The document summarizes research using SBAS-DInSAR (Small BAseline Subset differential interferometric synthetic aperture radar) techniques to analyze ground deformation at Mt. Etna volcano in Italy over the last 18 years using ERS and ENVISAT satellite data. The analysis revealed three main deformation processes: inflation of the volcanic edifice, subsidence of sectors on the eastern flank due to gravitational spreading, and deflation-inflation cycles associated with eruptive and post-eruptive activity. More recent analysis using higher resolution COSMO-SkyMed data from 2009-2010 detected deformation related to faults and a 2010 earthquake more precisely than lower resolution ENVISAT data.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...GlobalLogic Ukraine
Під час доповіді відповімо на питання, навіщо потрібно підвищувати продуктивність аплікації і які є найефективніші способи для цього. А також поговоримо про те, що таке кеш, які його види бувають та, основне — як знайти performance bottleneck?
Відео та деталі заходу: https://bit.ly/45tILxj
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
The Microsoft 365 Migration Tutorial For Beginner.pptx
Altmann_IGARSS_2011a_talk.pdf
1. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Nonlinear unmixing of hyperspectral images
using radial basis functions
and orthogonal least squares
Yoann Altmann1 , Nicolas Dobigeon1 ,
Steve McLaughlin2 and Jean-Yves Tourneret1
1
University of Toulouse - IRIT/INP-ENSEEIHT Toulouse, FRANCE
2
School of Engineering and Electronics - University of Edinburgh, U.K.
IEEE IGARSS 2011, Vancouver, Canada
1 / 34
2. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Hyperspectral Imagery
Hyperspectral Images
same scene observed at different wavelengths
2 / 34
3. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Hyperspectral Imagery
Hyperspectral Images
same scene observed at different wavelengths
Hyperspectral Cube
2 / 34
4. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Hyperspectral Imagery
Hyperspectral Images
same scene observed at different wavelengths
pixel represented by a vector of hundreds of measurements
3 / 34
5. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Hyperspectral Imagery
Hyperspectral Images
same scene observed at different wavelengths
pixel represented by a vector of hundreds of measurements
Hyperspectral Cube
3 / 34
6. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Unmixing problem
Unmixing: decomposing a measured pixel into a mixture of pure components
Presence of mixed pixels in hyperspectral data...
...even at high spatial resolution!
Unmixing steps
1. Endmember extraction: estimating the spectral signatures, or
endmembers, (PPI1 , N-FINDR2 , VCA3 , MVES4 ...)
2. Inversion: estimating the abundances (FCLS5 , Bayesian algo.6 ...)
(1+2). Joint estimation of endmembers and abundances (DECA7 , BLU8 ...)
1 Boardman et al., in Sum. JPL Airborne Earth Science Workshop, 1995.
2 Winter, in Proc. SPIE, 1999.
3 Nascimento et al., IEEE Trans. Geosci. and Remote Sensing, 2005.
4 Chan et al., IEEE Trans. Signal Process., 2009.
5 Heinz et al., IEEE Trans. Geosci. and Remote Sensing, 2001.
6 Dobigeon et al., IEEE Trans. Signal Process., 2008.
7 Nascimento et al., in Proc IGARSS., 2007.
8 Dobigeon et al., IEEE Trans. Signal Process., 2009.
4 / 34
7. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Unmixing problem
Two strategies: linear vs non-linear unmixing
Linear model
→ pure materials sitting side-by-side in the scene
→ 1st-order approximation
→ most of the research works over the last 2 decades
∼ 1000 entries in IEEEXplore
Nonlinear model
→ to describe intimate mixtures (e.g., sands)
→ to handle multiple scattering effects
5 / 34
8. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Unmixing problem
Two strategies: linear vs non-linear unmixing
Linear model
→ pure materials sitting side-by-side in the scene
→ 1st-order approximation
→ most of the research works over the last 2 decades
∼ 1000 entries in IEEEXplore
Nonlinear model
→ to describe intimate mixtures (e.g., sands)
→ to handle multiple scattering effects
Contribution of this paper
Nonlinear unmixing using radial basis functions to solve the inversion step.
5 / 34
9. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Outline
Radial basis function network
Mixing models
Network structure
Training: selecting RBF centers using OLS
Inversion: constrained abundance estimation
Simulation results
Synthetic data
Real Image data
Conclusions
6 / 34
10. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Radial basis function network
Outline
Radial basis function network
Mixing models
Network structure
Training: selecting RBF centers using OLS
Inversion: constrained abundance estimation
Simulation results
Synthetic data
Real Image data
Conclusions
7 / 34
11. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Radial basis function network
Mixing models
Linear mixing model
Reference: IEEE Signal Proc. Magazine, Jan. 2002.
Single-path of the detected photons
Linear combinations of the contributions of the endmembers
8 / 34
12. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Radial basis function network
Mixing models
Nonlinear mixing model
Reference: IEEE Signal Proc. Magazine, Jan. 2002.
Possible interactions between the components of the scene
Nonlinear terms included in the mixing model
9 / 34
13. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Radial basis function network
Mixing models
General (linear/nonlinear) mixing model
Definition
y = fM (a)
a = [a1 , . . . , aR ]T abundance vector
R number of endmembers
M endmember matrix
fM (linear/nonlinear) function from RR to RL parameterized by M
Remark : linear mixing defined by fM : a → Ma
Constraints
R
positivity : ar ≥ 0, ∀r ∈ 1, ..., R, sum-to-one : ar = 1 (1)
r=1
Supervised unmixing
The inversion problem can be formulated as
−1
ˆ
a = fM (y)
−1
→ knowledge of fM (·) required!
10 / 34
14. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Radial basis function network
Network structure
Radial basis function network (RBFN)9
Proposed inversion procedure
Given y, approximating a by the following linear expansion
N
φ1 (y)
−1
a = fM (y) ≈
ˆ φn (y)wn = W . .
.
n=1
φN (y)
ˆ
a estimated abundance vector
wn = [wn,1 , . . . , wn,R ]T weight vector
φn (y) projection of the data vector y onto the nth basis function
Gaussian kernels
2
y − cn
φn (y) = exp −
2σ 2
cn nth center of the network
unique fixed dispersion parameter σ 2
N number of basis functions
9 Guilfoyle et al., IEEE Trans. Geosci. and Remote Sensing, 2001.
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15. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Radial basis function network
Network structure
Radial basis function network (RBFN)
Radial basis function network for nonlinear unmixing.
Estimation of the nonlinear relation relating a to y: training step
Estimation of abundance vectors: inversion step
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16. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Radial basis function network
Network structure
Radial basis function network (RBFN)
Radial basis function network for nonlinear unmixing.
Estimation of the nonlinear relation relating a to y: training step
Estimation of abundance vectors: inversion step
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17. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Outline
Radial basis function network
Mixing models
Network structure
Training: selecting RBF centers using OLS
Inversion: constrained abundance estimation
Simulation results
Synthetic data
Real Image data
Conclusions
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18. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Training step
Inputs:
training pixels y1 , . . . , yN
associated abundance vectors a1 , . . . , aN
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19. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Training step
Inputs:
training pixels y1 , . . . , yN
associated abundance vectors a1 , . . . , aN
Outputs:
weights w1 , . . . , wN
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20. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Training step
Learning procedure
Given N training pixels y1 , . . . , yN and associated training abundance
vectors a1 , . . . , aN , estimating w1 , . . . , wN such that
T T T
a1 φ (y1 ) w1
. . .
A= . = . .
. . + E = ΦW + E
.
T T T
aN φ (yN ) wN
φ(y) = [φ1 (y), . . . , φN (y)]T projections of y on the N RBFs
Φ N × N matrix of projections
W N × R matrix of weight vectors related to the N centers
E projection error matrix
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21. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Training step
Learning procedure
Estimating W of size N × R using least-squares method
2
min A − ΦW F
W
where Φ is of size N × N .
Problem
Numerical issues for large values of N (Φ ill-conditioned)
Solution
Selecting a reduced number of centers out of the N training pixels.
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22. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Network complexity reduction
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23. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Network complexity reduction
Proposed approach for complexity reduction
Selection of the M << N most relevant network centers
→ How to determine the relevant subspace of span{φ1 , . . . , φN }?
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24. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Orthogonal least squares10
Orthogonalization
A = [φ1 , . . . , φN ] W + E
= [q1 , . . . , qN ] Θ + E
Q = [q1 , . . . , qN ] N × N orthogonal matrix such that
span{φ1 , . . . , φN } = span{q1 , . . . , qN }
Θ = [θ1 , . . . , θN ]T new regression coefficients.
Decomposition into relevant/unrelevant subspace
Sub-matrix decomposition Q = Q1:M QM +1:N
Relevant decomposition obtained when the output energy matrix
AT A is approximated by
N M
AT A = T T
θm qm qm θm ≈ T T
θm qm qm θm
m=1 m=1
⇒ Quantification of the contribution of the M first orthogonal regressors!
10 Chen et al. IEEE Trans. Neural Network, 1991.
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25. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Training: selecting RBF centers using OLS
Orthogonal least squares
Choosing the orthogonal basis
Q depends on the orthogonalization of {φ1 , . . . , φN }
testing all the orthogonal basis derived from {φ1 , . . . , φN } using
permutations and Gram-Schmidt processes
⇒ prohibitive computational cost
Proposed alternative
sequential and iterative construction of the orthogonal basis in a
forward regression manner (i.e., try M = 1, then M = 2, etc...).
(approach similar to the orthogonal matching pursuit)
stopping rule based on the error reduction ratio
M T T
m=1 θm qm qm θm
F
M =
AT A F
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26. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Inversion: constrained abundance estimation
Outline
Radial basis function network
Mixing models
Network structure
Training: selecting RBF centers using OLS
Inversion: constrained abundance estimation
Simulation results
Synthetic data
Real Image data
Conclusions
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27. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Inversion: constrained abundance estimation
Constrained abundance estimation
Network output
T
ˆ
a = WM φ(y)
Network fixed after the training step
WM estimated using M relevant regressors
Problem
Constraints not ensured when unmixing a new pixel!
Solution: constrained least-squares method
2
a = arg min φ(y) − WM† a
˜ T
a 2
subject to the positivity and sum-to-one constraints for a
WM† pseudo-inverse of WM
T T
problem solved using the FCLS algorithm11
11 Heinz et al., IEEE Trans. Geosci. and Remote Sensing, 2001.
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28. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Outline
Radial basis function network
Mixing models
Network structure
Training: selecting RBF centers using OLS
Inversion: constrained abundance estimation
Simulation results
Synthetic data
Real Image data
Conclusions
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29. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Synthetic data
Synthetic data
Simulation parameters
3 synthetic images (10 × 10 pixels) composed of R = 3 endmembers
(green grass, olive green paint, galvanized steel metal) corrupted by an
additive Gaussian noise with SNR 15dB
I1 = Linear Mixing Model (LMM)
3 mixing models I2 = Fan’s model (FM)12
I3 = Nascimento’s model (NM)13
Abundances uniformly drawn over the simplex defined by the
constraints.
Training data
3 training images (50 × 50 = 2500 pixels) composed of the same R = 3
endmembers (SNR 15dB)
T1 = LMM
3 mixing models T2 = FM
T3 = NM
12 Fan and al., Remote Sensing of Environment, 2009.
13 Nascimento et al., Proc. SPIE, 2009.
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30. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Synthetic data
Synthetic data
Training step: center selection
Initial 2500 centers (left) and selected centers for the images T1 (middle) and T2
(right) with the OLS procedure.
M = 11 centers selected for T1 (LMM)
M = 13 centers selected for T2 (FM)
M = 17 centers selected for T3 (NM) (not displayed here)
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31. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Synthetic data
Synthetic data
Quality of unmixing
Root mean square error
N
1 2
RMSE = an − an
ˆ
NR n=1
ˆ
an estimate of the nth abundance vector an
RMSE (×10−1 )
without OLS with OLS
Model-based algo.
RBFN CRBFN RBFN CRBFN
I1 (LMM) 0.409 0.407 0.411 0.403 0.395 (FCLS)
I2 (FM) 0.391 0.378 0.376 0.393 0.42014
I3 (NM) 0.541 0.532 0.547 0.544 0.689 (FCLS)
14 Fan and al., Remote Sensing of Environment, 2009.
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32. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Real Image data
Real data
Simulation parameters
Real hyperspectral image of 50 × 50 pixels extracted from a larger
image acquired in 1997 by AVIRIS (Moffett Field, CA, USA) data set
reduced from 224 to 189 bands (water absorption bands removed)
VCA used to extract the R = 3 approximated endmembers associated
to water, soil and vegetation.
Real hyperspectral data: Moffett field acquired by AVIRIS in 1997 (left) and the
region of interest shown in true colors (right).
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33. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Real Image data
Real data
Estimated endmembers
Training step
Estimated spectra used to generate training data sets of 2500 pixels
according to the LMM and FM (SNR ≈ 15 dB)
OLS procedure performed to reduce the number of centers (LMM: 21
centers, FM: 23 centers)
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34. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Real Image data
Real data
Estimated abundance maps (LMM)
Top: Constrained RBFN. Bottom: FCLS.
Maps similar to maps obtained using dedicated algorithms
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35. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Simulation results
Real Image data
Real data
Estimated abundance maps (FM)
Top: Constrained RBFN. Bottom: Fan-LS.
Maps similar to maps obtained using dedicated algorithms
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36. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Conclusions
Outline
Radial basis function network
Mixing models
Network structure
Training: selecting RBF centers using OLS
Inversion: constrained abundance estimation
Simulation results
Synthetic data
Real Image data
Conclusions
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37. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Conclusions
Conclusions
Nonlinear unmixing of hyperspectral images using radial basis functions and
orthogonal least squares
Radial basis functions introduced to invert the nonlinear relationship
between the observation vector and the associated abundance vector.
Selection of a reduced number of RBFs centers from training data to
reduce the network complexity using an OLS procedure.
Modification of the algorithm to satisfy positivity and sum-to-one
constraints.
Perspectives
Adaptive update of the weights and centers for unsupervised unmixing.
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38. Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares
Conclusions
Nonlinear unmixing of hyperspectral images
using radial basis functions
and orthogonal least squares
Yoann Altmann1 , Nicolas Dobigeon1 ,
Steve McLaughlin2 and Jean-Yves Tourneret1
1
University of Toulouse - IRIT/INP-ENSEEIHT Toulouse, FRANCE
2
School of Engineering and Electronics - University of Edinburgh, U.K.
IEEE IGARSS 2011, Vancouver, Canada
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