This document proposes a general approach for simplifying remote sensing images based on morphological connected filters. It involves two main modules:
1. Selection of filter parameters and operators based on available prior knowledge about the image scene and application. This allows defining operative scenarios.
2. Application of the selected connected filters from a bank of options like attribute thinning and thickening. Filters are chosen to simplify images for specific applications like object extraction.
The approach aims to automate image simplification by modeling filter parameter ranges with fuzzy functions. This allows adapting simplification to different image types and applications in an automated way. Case studies on building enhancement demonstrate how filters can be tailored to specific tasks.
Use of Discrete Sine Transform for A Novel Image Denoising TechniqueCSCJournals
In this paper, we propose a new multiresolution image denoising technique using Discrete Sine Transform. Wavelet techniques have been in use for multiresolution image processing. Discrete Cosine Transform is also extensively used for image compression. Similar to the Discrete Wavelet and Discrete Cosine Transform it is now found that Discrete Sine Transform also possess some good qualities for image processing; specifically for image denoising. Algorithm for image denoising using Discrete Sine Transform is proposed with simulation works for experimental verification. The method is computationally efficient and simple in theory and application.
This document discusses a proposed approach for multi-focus image fusion using a discrete cosine wavelet sharpness criterion. Multi-focus image fusion combines information from multiple images of the same scene to produce an "all-in-focus" image. The proposed approach uses a discrete cosine transform to calculate sharpness values for sub-blocks of the input images and selects the sharpest sub-blocks to include in the fused image. Experimental results on images of a clock, bottle, and book show the discrete cosine wavelet criterion produces fused images with higher quality than a bilateral gradient-based sharpness criterion, as measured by mutual information metrics.
Developing a classification framework for landcover landuse change analysis i...Carolina
The document discusses developing a land cover/land use classification framework for Chile using remote sensing data. It describes using mathematical morphology techniques like morphological attribute profiles to integrate spatial context into spectral classifications, which significantly improved classification accuracy in three test subsets of a Landsat image - forests (61.3% to 80.8%), urban (75.5% to 92.2%) and agriculture (62.2% to 89.2%). However, the high-dimensional feature space requires specialized machine learning methods and high-performance computing.
International Journal of Computational Engineering Research(IJCER)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.
Digital image classification involves:
1) Sorting pixels into classes based on their spectral values using algorithms like supervised maximum likelihood classification or unsupervised isodata clustering.
2) Analyzing spectral patterns by examining pixels in feature space rather than image space. Distances between pixel vectors in feature spaces define class boundaries.
3) Validating classification results to determine accuracy by comparing to reference data. Problems can occur and techniques continue improving.
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
Maximizing Strength of Digital Watermarks Using Fuzzy Logicsipij
In this paper, we propose a novel digital watermarking scheme in DCT domain based fuzzy inference system and the human visual system to adapt the embedding strength of different blocks. Firstly, the original image is divided into some 8×8 blocks, and then fuzzy inference system according to different textural features and luminance of each block decide adaptively different embedding strengths. The watermark detection adopts correlation technology. Experimental results show that the proposed scheme has good imperceptibility and high robustness to common image processing operators.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
Use of Discrete Sine Transform for A Novel Image Denoising TechniqueCSCJournals
In this paper, we propose a new multiresolution image denoising technique using Discrete Sine Transform. Wavelet techniques have been in use for multiresolution image processing. Discrete Cosine Transform is also extensively used for image compression. Similar to the Discrete Wavelet and Discrete Cosine Transform it is now found that Discrete Sine Transform also possess some good qualities for image processing; specifically for image denoising. Algorithm for image denoising using Discrete Sine Transform is proposed with simulation works for experimental verification. The method is computationally efficient and simple in theory and application.
This document discusses a proposed approach for multi-focus image fusion using a discrete cosine wavelet sharpness criterion. Multi-focus image fusion combines information from multiple images of the same scene to produce an "all-in-focus" image. The proposed approach uses a discrete cosine transform to calculate sharpness values for sub-blocks of the input images and selects the sharpest sub-blocks to include in the fused image. Experimental results on images of a clock, bottle, and book show the discrete cosine wavelet criterion produces fused images with higher quality than a bilateral gradient-based sharpness criterion, as measured by mutual information metrics.
Developing a classification framework for landcover landuse change analysis i...Carolina
The document discusses developing a land cover/land use classification framework for Chile using remote sensing data. It describes using mathematical morphology techniques like morphological attribute profiles to integrate spatial context into spectral classifications, which significantly improved classification accuracy in three test subsets of a Landsat image - forests (61.3% to 80.8%), urban (75.5% to 92.2%) and agriculture (62.2% to 89.2%). However, the high-dimensional feature space requires specialized machine learning methods and high-performance computing.
International Journal of Computational Engineering Research(IJCER)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.
Digital image classification involves:
1) Sorting pixels into classes based on their spectral values using algorithms like supervised maximum likelihood classification or unsupervised isodata clustering.
2) Analyzing spectral patterns by examining pixels in feature space rather than image space. Distances between pixel vectors in feature spaces define class boundaries.
3) Validating classification results to determine accuracy by comparing to reference data. Problems can occur and techniques continue improving.
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
Maximizing Strength of Digital Watermarks Using Fuzzy Logicsipij
In this paper, we propose a novel digital watermarking scheme in DCT domain based fuzzy inference system and the human visual system to adapt the embedding strength of different blocks. Firstly, the original image is divided into some 8×8 blocks, and then fuzzy inference system according to different textural features and luminance of each block decide adaptively different embedding strengths. The watermark detection adopts correlation technology. Experimental results show that the proposed scheme has good imperceptibility and high robustness to common image processing operators.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
Review on Optimal image fusion techniques and Hybrid techniqueIRJET Journal
This document reviews various image fusion techniques and proposes a hybrid technique. It discusses pixel-level, feature-level, and decision-level image fusion. Spatial domain methods like average fusion and temporal domain methods like discrete wavelet transform are described. The limitations of existing techniques like ringing artifacts and shift-variance are covered. A hybrid technique using set partitioning in hierarchical trees (SPIHT) and self-organizing migrating algorithm (SOMA) is proposed to improve fusion quality and efficiency over existing methods. This technique is presented as easier to implement and suitable for real-time applications.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
Image fusion is the process of combining two or more images with specific objects with more precision. It is very common that when one object is focused remaining objects will be less highlighted. To get an image highlighted in all areas, a different means is necessary. This is done by the Image Fusion. In remote sensing, the increasing availability of Space borne images and synthetic aperture radar images gives a motivation to different kinds of image fusion algorithms. In the literature a number of time domain image fusion techniques are available. Few transform domain fusion techniques are proposed. In transform domain fusion techniques, the source images will be decomposed, then integrated into a single data and will be reconstructed back into time domain. In this paper, singular value decomposition as a tool to have transform domain data will be utilized for image fusion. In the literature, the quality assessment of fusion techniques is mainly by subjective tests. In this paper, objective quality assessment metrics are calculated for existing and proposed techniques. It has been found that the new image fusion technique outperformed the existing ones.
Automatic Relative Radiometric Normalization for Change Detection of Satellit...IDES Editor
Several relative radiometric normalization (RRN)
techniques have been proposed till date most of which involve
selection of pseudo invariant features whose reflectance are
nearly invariant from image to image and are independent of
seasonal cycles. Extraction of such points is quiet tedious and
human operator has to provide mutual correspondence by
choosing easily recognizable and time invariant points. In
this paper, we intend to propose a new automatic radiometric
normalization technique to select PIFs in panchromatic
images known as Bin-Division Method. For multispectral
images, MAD (Multivariate Alteration Detection) has been
employed for selecting PIFs based on the assumption that
MAD components are invariant to affine transformation. This,
followed by robust linear regression constitutes the whole
automatic radiometric normalization procedure.
RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques
IRJET- Image Registration in GIS: A SurveyIRJET Journal
This document provides an overview of image registration techniques in geographic information systems (GIS) and remote sensing. It discusses key concepts such as image registration, which aligns images taken at different times or from different sensors. The document also summarizes common digital image processing techniques used for image registration, including image restoration, enhancement, classification, and transformation. Principal component analysis is described as one example of an image transformation technique.
Inflammatory Conditions Mimicking Tumours In Calabar: A 30 Year Study (1978-2...IOSR Journals
1. The document discusses different image fusion techniques, specifically wavelet transform and curvelet transform based fusion.
2. Wavelet transform is commonly used for image fusion due to its simplicity and ability to preserve time-frequency details. Curvelet transform is better for fusing images with curved edges.
3. The paper compares fusion results of medical images like MR and CT using wavelet and curvelet transforms, finding that curvelet transform provides superior results in metrics like entropy and peak signal-to-noise ratio.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
An Unsupervised Change Detection in Satellite IMAGES Using MRFFCM ClusteringEditor IJCATR
This document summarizes an unsupervised change detection method for satellite images using Markov random field fuzzy c-means (MRFFCM) clustering. The method first generates a difference image from multitemporal satellite images using image fusion techniques. It then applies MRFFCM clustering to the difference image to segment it into changed and unchanged regions. Experimental results on real synthetic aperture radar images show that MRFFCM clustering produces more accurate change detection results with less error than previous approaches, while also having lower time complexity. The method is evaluated on datasets from Bern, Ottawa, and the Yellow River region, demonstrating its effectiveness.
Uniform and non uniform single image deblurring based on sparse representatio...ijma
Considering the sparseness property of images, a sparse representation based iterative deblurring method
is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken
is based on sparse and redundant representations over adaptively training dictionaries from single
blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary that describes the
image contents effectively. Comprehensive experimental evaluation demonstrate that the proposed
framework integrating the sparseness property of images, adaptive dictionary training and iterative
deblurring scheme together significantly improves the deblurring performance and is comparable with the
state-of-the art deblurring algorithms and seeks a powerful solution to an ill-conditioned inverse problem.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
Comparative study on image fusion methods in spatial domainIAEME Publication
This document provides a comparative study of various image fusion methods in the spatial domain. It begins by introducing image fusion and its applications. Section 2 then describes several common fusion algorithms in the spatial domain, including average, select maximum/minimum, Brovey transform, intensity hue saturation (IHS), and principal component analysis (PCA). Section 3 defines image fusion quality measures like entropy, mean squared error, and normalized cross correlation. Section 4 provides a comparative analysis of the spatial domain fusion techniques based on parameters like simplicity, type of resources, and disadvantages. It finds that spatial domain methods provide high spatial resolution but have issues like image blurring and producing less informative outputs. The document concludes that while the best algorithm depends on the problem, spatial
This document discusses image fusion techniques at different levels of abstraction: pixel level, feature level, and decision level. It describes various fusion methods including numerical (e.g. multiplicative, Brovey), color related (e.g. IHS), statistical (e.g. PCA, Gram Schmidt), and feature level (e.g. Ehlers) techniques. Both qualitative (visual) and quantitative (statistical measures like RMSE, correlation coefficient, entropy) methods to assess fusion quality are outlined. Image fusion has applications in improving classification and displaying sharper resolution images.
A New Approach of Medical Image Fusion using Discrete Wavelet TransformIDES Editor
MRI-PET medical image fusion has important
clinical significance. Medical image fusion is the important
step after registration, which is an integrative display method
of two images. The PET image shows the brain function with
a low spatial resolution, MRI image shows the brain tissue
anatomy and contains no functional information. Hence, a
perfect fused image should contains both functional
information and more spatial characteristics with no spatial
& color distortion. The DWT coefficients of MRI-PET
intensity values are fused based on the even degree method
and cross correlation method The performance of proposed
image fusion scheme is evaluated with PSNR and RMSE and
its also compared with the existing techniques.
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
Survey on Image Integration of Misaligned ImagesIRJET Journal
The document discusses methods for integrating misaligned images to improve image quality under low lighting conditions. It reviews previous works that combine images like flash/no-flash pairs to transfer details and color, but have limitations when images are misaligned. The paper proposes a new method using a long-exposure image and flash image that introduces a local linear model to transfer color while maintaining natural colors and high contrast, without deteriorating contrast for misaligned pairs. It concludes that handling misaligned images remains a challenge with existing methods and further work is needed.
Image restoration model with wavelet based fusionAlexander Decker
1. The document discusses various techniques for image restoration, which aims to recover a sharp original image from a degraded one using mathematical models of degradation and restoration.
2. It analyzes techniques like deconvolution using Lucy Richardson algorithm, Wiener filter, regularized filter, and blind image deconvolution on different image formats based on metrics like PSNR, MSE, and RMSE.
3. Previous studies have applied techniques like Wiener filtering, wavelet-based fusion, and iterative blind deconvolution for motion blur restoration and compared their performance.
This document summarizes a research paper that proposes a method for denoising remote sensing images using a combination of second order and fourth order partial differential equations (PDEs). It begins by explaining how noise is introduced in images and why denoising is important. It then discusses existing denoising methods using second order and fourth order PDEs individually and their limitations. The proposed method combines the two approaches to reduce both the blocky effect of second order PDEs and the speckle effect of fourth order PDEs. Simulation results show the combined method achieves better peak signal-to-noise and signal-to-noise ratios compared to the individual methods.
The Prince's Foundation works to address the challenges of rapid urbanization by championing sustainable development principles and enabling community projects around the world. It runs education programs on topics like sustainable urbanism and heritage skills. Current projects exemplify the Foundation's principles of creating places that are prudent, local, adaptable, coherent, and equitable by engaging communities in the planning process.
Review on Optimal image fusion techniques and Hybrid techniqueIRJET Journal
This document reviews various image fusion techniques and proposes a hybrid technique. It discusses pixel-level, feature-level, and decision-level image fusion. Spatial domain methods like average fusion and temporal domain methods like discrete wavelet transform are described. The limitations of existing techniques like ringing artifacts and shift-variance are covered. A hybrid technique using set partitioning in hierarchical trees (SPIHT) and self-organizing migrating algorithm (SOMA) is proposed to improve fusion quality and efficiency over existing methods. This technique is presented as easier to implement and suitable for real-time applications.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
Image fusion is the process of combining two or more images with specific objects with more precision. It is very common that when one object is focused remaining objects will be less highlighted. To get an image highlighted in all areas, a different means is necessary. This is done by the Image Fusion. In remote sensing, the increasing availability of Space borne images and synthetic aperture radar images gives a motivation to different kinds of image fusion algorithms. In the literature a number of time domain image fusion techniques are available. Few transform domain fusion techniques are proposed. In transform domain fusion techniques, the source images will be decomposed, then integrated into a single data and will be reconstructed back into time domain. In this paper, singular value decomposition as a tool to have transform domain data will be utilized for image fusion. In the literature, the quality assessment of fusion techniques is mainly by subjective tests. In this paper, objective quality assessment metrics are calculated for existing and proposed techniques. It has been found that the new image fusion technique outperformed the existing ones.
Automatic Relative Radiometric Normalization for Change Detection of Satellit...IDES Editor
Several relative radiometric normalization (RRN)
techniques have been proposed till date most of which involve
selection of pseudo invariant features whose reflectance are
nearly invariant from image to image and are independent of
seasonal cycles. Extraction of such points is quiet tedious and
human operator has to provide mutual correspondence by
choosing easily recognizable and time invariant points. In
this paper, we intend to propose a new automatic radiometric
normalization technique to select PIFs in panchromatic
images known as Bin-Division Method. For multispectral
images, MAD (Multivariate Alteration Detection) has been
employed for selecting PIFs based on the assumption that
MAD components are invariant to affine transformation. This,
followed by robust linear regression constitutes the whole
automatic radiometric normalization procedure.
RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques
IRJET- Image Registration in GIS: A SurveyIRJET Journal
This document provides an overview of image registration techniques in geographic information systems (GIS) and remote sensing. It discusses key concepts such as image registration, which aligns images taken at different times or from different sensors. The document also summarizes common digital image processing techniques used for image registration, including image restoration, enhancement, classification, and transformation. Principal component analysis is described as one example of an image transformation technique.
Inflammatory Conditions Mimicking Tumours In Calabar: A 30 Year Study (1978-2...IOSR Journals
1. The document discusses different image fusion techniques, specifically wavelet transform and curvelet transform based fusion.
2. Wavelet transform is commonly used for image fusion due to its simplicity and ability to preserve time-frequency details. Curvelet transform is better for fusing images with curved edges.
3. The paper compares fusion results of medical images like MR and CT using wavelet and curvelet transforms, finding that curvelet transform provides superior results in metrics like entropy and peak signal-to-noise ratio.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
An Unsupervised Change Detection in Satellite IMAGES Using MRFFCM ClusteringEditor IJCATR
This document summarizes an unsupervised change detection method for satellite images using Markov random field fuzzy c-means (MRFFCM) clustering. The method first generates a difference image from multitemporal satellite images using image fusion techniques. It then applies MRFFCM clustering to the difference image to segment it into changed and unchanged regions. Experimental results on real synthetic aperture radar images show that MRFFCM clustering produces more accurate change detection results with less error than previous approaches, while also having lower time complexity. The method is evaluated on datasets from Bern, Ottawa, and the Yellow River region, demonstrating its effectiveness.
Uniform and non uniform single image deblurring based on sparse representatio...ijma
Considering the sparseness property of images, a sparse representation based iterative deblurring method
is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken
is based on sparse and redundant representations over adaptively training dictionaries from single
blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary that describes the
image contents effectively. Comprehensive experimental evaluation demonstrate that the proposed
framework integrating the sparseness property of images, adaptive dictionary training and iterative
deblurring scheme together significantly improves the deblurring performance and is comparable with the
state-of-the art deblurring algorithms and seeks a powerful solution to an ill-conditioned inverse problem.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
Comparative study on image fusion methods in spatial domainIAEME Publication
This document provides a comparative study of various image fusion methods in the spatial domain. It begins by introducing image fusion and its applications. Section 2 then describes several common fusion algorithms in the spatial domain, including average, select maximum/minimum, Brovey transform, intensity hue saturation (IHS), and principal component analysis (PCA). Section 3 defines image fusion quality measures like entropy, mean squared error, and normalized cross correlation. Section 4 provides a comparative analysis of the spatial domain fusion techniques based on parameters like simplicity, type of resources, and disadvantages. It finds that spatial domain methods provide high spatial resolution but have issues like image blurring and producing less informative outputs. The document concludes that while the best algorithm depends on the problem, spatial
This document discusses image fusion techniques at different levels of abstraction: pixel level, feature level, and decision level. It describes various fusion methods including numerical (e.g. multiplicative, Brovey), color related (e.g. IHS), statistical (e.g. PCA, Gram Schmidt), and feature level (e.g. Ehlers) techniques. Both qualitative (visual) and quantitative (statistical measures like RMSE, correlation coefficient, entropy) methods to assess fusion quality are outlined. Image fusion has applications in improving classification and displaying sharper resolution images.
A New Approach of Medical Image Fusion using Discrete Wavelet TransformIDES Editor
MRI-PET medical image fusion has important
clinical significance. Medical image fusion is the important
step after registration, which is an integrative display method
of two images. The PET image shows the brain function with
a low spatial resolution, MRI image shows the brain tissue
anatomy and contains no functional information. Hence, a
perfect fused image should contains both functional
information and more spatial characteristics with no spatial
& color distortion. The DWT coefficients of MRI-PET
intensity values are fused based on the even degree method
and cross correlation method The performance of proposed
image fusion scheme is evaluated with PSNR and RMSE and
its also compared with the existing techniques.
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
Survey on Image Integration of Misaligned ImagesIRJET Journal
The document discusses methods for integrating misaligned images to improve image quality under low lighting conditions. It reviews previous works that combine images like flash/no-flash pairs to transfer details and color, but have limitations when images are misaligned. The paper proposes a new method using a long-exposure image and flash image that introduces a local linear model to transfer color while maintaining natural colors and high contrast, without deteriorating contrast for misaligned pairs. It concludes that handling misaligned images remains a challenge with existing methods and further work is needed.
Image restoration model with wavelet based fusionAlexander Decker
1. The document discusses various techniques for image restoration, which aims to recover a sharp original image from a degraded one using mathematical models of degradation and restoration.
2. It analyzes techniques like deconvolution using Lucy Richardson algorithm, Wiener filter, regularized filter, and blind image deconvolution on different image formats based on metrics like PSNR, MSE, and RMSE.
3. Previous studies have applied techniques like Wiener filtering, wavelet-based fusion, and iterative blind deconvolution for motion blur restoration and compared their performance.
This document summarizes a research paper that proposes a method for denoising remote sensing images using a combination of second order and fourth order partial differential equations (PDEs). It begins by explaining how noise is introduced in images and why denoising is important. It then discusses existing denoising methods using second order and fourth order PDEs individually and their limitations. The proposed method combines the two approaches to reduce both the blocky effect of second order PDEs and the speckle effect of fourth order PDEs. Simulation results show the combined method achieves better peak signal-to-noise and signal-to-noise ratios compared to the individual methods.
The Prince's Foundation works to address the challenges of rapid urbanization by championing sustainable development principles and enabling community projects around the world. It runs education programs on topics like sustainable urbanism and heritage skills. Current projects exemplify the Foundation's principles of creating places that are prudent, local, adaptable, coherent, and equitable by engaging communities in the planning process.
This document describes a measurement setup used to extract the reflectivity of a microwave blackbody target using free-space measurements. Theoretical background is provided on modeling the non-ideal behavior of calibration targets. Measurements were made in an anechoic chamber on a Rexolite sample and a 13-inch GSFC target from 400 MHz to 40 GHz. Results showed the GSFC target had reflectivity lower than 40 dB in K-band, consistent with specifications, and was unaffected by temperature. The technique was verified and uncertainties were estimated.
The document discusses several topics:
1) Giving processes which involves fairly simple stages from starting to finishing;
2) Making future plans which involves a group interested in investing in a new refinery and inviting to Moscow and for drinks;
3) Getting someone's attention respectfully by asking for a minute of their time.
The document outlines the requirements for a group project and individual assignment. For the group project, students must give a 10-15 minute oral presentation on an imaginary invention, describing its purpose, appearance, function, and benefits/limitations. Presentations will be graded based on flow, demonstrated understanding, speech/slides synchronization, and proper grammar/pronunciation. For the individual assignment, students must write a 100-word paragraph reflecting on and evaluating a classmate's invention by answering three questions and following paragraph structure guidelines. Both assignments are due by specified dates and criteria like plagiarism, word count, and formatting must be followed.
Leaders Study Program , London 2014-
Leaders Pecha Kucha (Lightening Talks) What makes a Visionary Development ? - Learning from innovations in Design, Strategy & Development in London.About: The Urban Vision- Leader Network will connect the most influential urban leaders to each other and to revolutionary city building & design concepts. Our annual leaders retreat explores innovations in design and policy in some of the world's great cities. Apply Now : http://bit.ly/tuvleaders
Reimagine Mumbai's Public Spaces : Plaza in Powai
The uptight formal fabric of Powai needs a punctuation of a personalized space - a pause space- a place to connect - a place to drop in with no agenda - a place created by the people - a place where a tree house can be planted - a place where one can paint the floor !
The document describes a community toilet project in Delwara village, India. Most villagers lacked access to toilets, so women used fields and men used the village pond, contaminating the water supply. A site was selected near the highway, clinic, and bus stop. Dry composting toilets were designed using local materials like stone and tin roofs. The toilets' waste will fertilize an amla grove and fodder beds. The design aims to provide sanitation while conserving water and embedding local skills and traditions.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
1. The document describes a new morphological image cleaning (MIC) algorithm for reducing noise in grayscale images while preserving thin features.
2. MIC works by calculating image residuals on different scales using morphological size distributions, then discards regions judged to contain noise. It creates a cleaned image by recombining the processed residuals with a smoothed version.
3. Previous morphological noise filters like openings and closings tend to remove important thin features along with noise. MIC aims to overcome this limitation by manipulating image residuals in a way that preserves thin features.
Medical Images are regularly of low contrast and boisterous/Noisy (absence of clarity) because of
the circumstances they are being taken. De-noising these pictures is a troublesome undertaking as they
ought to exclude any antiquities or obscuring of edges in the pictures. The Bayesian shrinkage strategy has
been chosen for thresholding in light of its sub band reliance property. The spatial space and Wavelet
based de-noising systems utilizing delicate thresholding strategy are contrasted and the proposed technique
utilizing GA (Genetic Algorithm) is used. The GA procedure is proposed in view of PSNR and results are
contrasted and existing spatial space and wavelet based de-noising separating strategies. The proposed
calculation gives improved visual clarity to diagnosing the restorative pictures. The proposed strategy in
view of GA surveys the better execution on the premise of the quantitative metric i.e PSNR (Peak Signal
to Noise-Ratio) and visual impacts. Reenactment results demonstrate that the GA based proposed
technique beats the current de-noising separating strategies.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
The document evaluates and compares the performance of three background subtraction algorithms: frame difference, statistical approach for real-time robust background subtraction and shadow detection, and adaptive background mixture models for real-time tracking. The frame difference method is the simplest but depends on threshold selection, while the statistical approach provides average accuracy and speed and the adaptive mixture models method provides the best accuracy but highest computational cost. Experimental results on video data demonstrate the tradeoffs between accuracy, computational requirements, and ability to handle challenges like lighting changes for each algorithm.
Computationally Efficient Methods for Sonar Image Denoising using Fractional ...CSCJournals
Sonar images produced due to the coherent nature of scattering phenomenon inherit a multiplicative component called speckle and contain almost homogeneous as well as textured regions with relatively rare edges. Speckle removal is a pre-processing step required in applications like the detection and classification of objects in the sonar image. In this paper computationally efficient Fractional Integral Mask algorithms to remove the speckle noise from sonar images is proposed. Riemann- Liouville definition of fractional calculus is used to create Fractional integral masks in eight directions. The use of a mask incorporated with the significant coefficients from the eight directional masks and a single convolution operation required in such case helps in obtaining the computational efficiency. The sonar image heterogeneous patch classification is based on a new proposed naive homogeneity index which depends on the texture strength of the patches and despeckling filters can be adjusted to these patches. The application of the mask convolution only to the selected patches again reduce the computational complexity. The non-homomorphic approach used in the proposed method avoids the undesired bias occurring in the traditional homomorphic approach. Experiments show that the mask size required directly depends on the fractional order. Mask size can be reduced for lower fractional orders thus ensuring the computation complexity reduction for lower orders. Experimental results substantiate the effectiveness of the despeckling method. The different non reference image performance evaluation criterion are used to evaluate the proposed method.
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...sipij
The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding.
DIGITAL RESTORATION OF TORN FILMS USING FILTERING T ECHNIQUESAM Publications
The acceptance of digital imaging is motivating many photography enthusiasts to transfer their
photographic archive to digital form. Scans of negatives and positives are preferred to be scanned at high resolution
which makes small cracks and scratches very apparent. These unsightly defects have become an important issue
for consumers. Filtering techniques are used for the restoration process which is fully automatic whereas the existing
systems were semi-automatic or completely manual. The method used for the detection of tear is dilation process and
top-hat transform. Top-hat transform might misinterpret dark brush strokes as cracks. In order to avoid these
unwanted alterations to the original image, brush strokes are separated from the actual cracks using clustering
technique. Tear removal includes order statistics filtering which deals with the reconstruction of missing or
damaged image areas.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Design and Development of Forest Fire Management Systemsipij
Forest fire is one of those natural disasters that have been causing huge destruction in terms of loss of vegetation, animals and hence affects the economy. Image segmentation techniques have been applied on satellite images of forest fire to extract fire object and some data mining techniques have been used for predicting the spread of forest fire. This paper proposes a novel approach to isolation of fire region using time-sequenced images, classifying fire images from non-fire images, predicting its movement and estimating the area burnt. Once the images are enhanced, the fire region is segmented out. Feature extraction provides the necessary inputs for classification of images as fire and non-fire images. Linear regression is used to predict the movement of forest fire to facilitate better evacuation strategy. Burnt area is calculated from the difference image. This work is helpful in drafting evacuation strategies quickly by predicting the movement of forest fire and facilitates the kick-off of rehabilitation activities by identifying and assessing the burnt area.
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
REMOVING RAIN STREAKS FROM SINGLE IMAGES USING TOTAL VARIATIONijma
ABSTRACT
Rainy image restoration is considered asone of the most important image restorations aspects to improve the outdoor vision. Many fields have used this kind of restorations such as driving assistant, environment monitoring, animals monitoring, computer vision, face recognition, object recognition and personal photos. Image restoration simply means how to remove the noise from the images. Most of the images have some noises from the environment. Moreover, image quality assessment plays an important role in the valuation of image enhancement algorithms. In this research, we will use a total variation to remove rain streaks from a single image. It shows a good performance compared to other methods, using some measurements MSE, PSNR, and VIF for an image with references and BRISQUE for an image without references.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document discusses various techniques for edge detection in digital images, including differential operators, log operators, Canny operators, and binary morphology.
2) It first performs wavelet-based denoising on input images to remove noise before edge detection.
3) It then applies different edge detection operators and compares their advantages and disadvantages through simulations. Binary morphology is shown to obtain better edge features compared to other operators.
4) The overall goal is to extract clear and complete edge profiles from images to aid in tasks like image segmentation.
International 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
This document summarizes a research paper on tracking moving objects and determining their distance and velocity using background subtraction algorithms. It first describes background subtraction as a process to extract foreground objects from video by comparing each frame to a background model. It then discusses several algorithms used in the research, including median filtering for noise removal, morphological operations to smooth object regions, and connected component analysis to detect large foreground regions representing objects. The document evaluates these techniques on video to track a single object, determine the distance and velocity of that object between frames, and identify multiple moving objects.
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.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
5 igarss2011_mdm.pdf
1. A General Approach to the Spatial Simplication of Remote Sensing
Images Based on Morphological Connected Filters
Mauro Dalla Mura
†, , Jón Atli Benediktsson , Lorenzo Bruzzone†
† Department of Information Engineering and Computer Science
University of Trento.
Faculty of Electrical and Computer Engineering
University of Iceland.
IGARSS 2011
24-29 July
2. Outline
1 Introduction
2 General Approach for Image Simplication
Connected Operators
Methodology
3 Conclusion and Future Developments
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 2 / 19
3. Introduction
Remote Sensing VHR Images
QuickBird 60cm, Panchromatic image, Bam (Iraq) Geoeye 50cm, Pansharpened images, Vancouver (Canada)
c Google
ROSIS-03 1.3m, Hyperspectral image, Pavia (Italy) TerraSAR-X 1.1m, Spotlight SAR, Dorsten (Germany)
The information extraction in remote sensing images is becoming increasingly
complex due to the progressively higher spatial resolution of the current
sensors.
How to extract the informative components dealing with the huge amount
of details?
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 3 / 19
4. Introduction
Remote Sensing VHR Images
QuickBird 60cm, Panchromatic image, Bam (Iraq) Geoeye 50cm, Pansharpened images, Vancouver (Canada)
c Google
ROSIS-03 1.3m, Hyperspectral image, Pavia (Italy) TerraSAR-X 1.1m, Spotlight SAR, Dorsten (Germany)
The information extraction in remote sensing images is becoming increasingly
complex due to the progressively higher spatial resolution of the current
sensors.
How to extract the informative components dealing with the huge amount
of details?
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 3 / 19
5. Introduction
Image Simplication
Spatial simplication of the image.
Pre-processing operation for many remote sensing applications:
Image segmentation;
Supervised/unsupervised thematic classication;
Land cover change analysis;
Object recognition and extraction;
Denoising SAR images;
Analysis of multiangular images.
Image simplication leads to:
noise reduction;
exploiting the contextual relations;
modeling spatial relations;
removing or attenuating undesired details.
Simplication of the image performed by spatial ltering, a 2-step procedure
composed of:
1 selection of the lters parameters;
2 application of the operator on the image.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 4 / 19
6. Introduction
Image Simplication (examples)
VHR optical image - dierent simplications
Which details should be removed?
Which operator should be applied? And which lter parameters should be
selected?
It depends on the application and on the type of image.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 5 / 19
7. Introduction
Image Simplication (examples)
VHR optical image - dierent simplications
Which details should be removed?
Which operator should be applied? And which lter parameters should be
selected?
It depends on the application and on the type of image.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 5 / 19
8. Introduction
Image Simplication (examples)
VHR optical image - dierent simplications
Which details should be removed?
Which operator should be applied? And which lter parameters should be
selected?
It depends on the application and on the type of image.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 5 / 19
9. Introduction
Motivation
Issues related to image simplication
The selection of the parameters of the lters is application dependent.
Proper operators should be used.
Manual operation.
Aims of the work
Dene a novel general approach to image simplication
based on morphological connected operators;
suitable for the processing of dierent types of images and dierent
applications;
suitable to be performed in an automated way.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 6 / 19
10. General Approach for Image Simplication Connected Operators
Connected Operators - Operators by Reconstruction
Connected operators are morphological lters that process an image by only
merging its at zones.
Either completely remove or entirely preserve a region in the image.
They do not distort the geometrical characteristics (e.g., shape, edges) of the
structures in the image.
Operators by Reconstruction
Closing Closing by rec. Original image Opening by rec. Opening
φB (f ) φB (f ) = Rf [δB (f )]
R
ε f B δ
γR (f ) = Rf [εB (f )] γB (f )
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 7 / 19
11. General Approach for Image Simplication Connected Operators
Connected Operators - Operators by Reconstruction
Connected operators are morphological lters that process an image by only
merging its at zones.
Either completely remove or entirely preserve a region in the image.
They do not distort the geometrical characteristics (e.g., shape, edges) of the
structures in the image.
Operators by Reconstruction
Closing Closing by rec. Original image Opening by rec. Opening
φB (f ) φB (f ) = Rf [δB (f )]
R
ε f B δ
γR (f ) = Rf [εB (f )] γB (f )
Connected operators are suitable for the analysis of VHR images.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 7 / 19
12. General Approach for Image Simplication Connected Operators
Connected Operators - Attribute Filters
Attribute lters are connected operators dened in the mathematical
morphology framework and recently used for the analysis of remote sensing1 .
Based on a measure (attribute) computed on the regions of an image.
Filtering performed by removing the regions that do not fulll a condition (T )
which compares an attribute attr against a reference value λ
(e.g., T = attr ≥ λ).
Main operators:
attribute thinning, γT ;
attribute thickening, φT .
Example: Dierent attributes
Original image Area Standard deviation Moment of inertia Solidity
1 M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, Morphological attribute proles for the analysis
of very high resolution images, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 10, pp.
IGARSS 20113762, Oct. 2010.
3747 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 8 / 19
13. General Approach for Image Simplication Methodology
Architecture of the Proposed General Approach
X
Parameters Selection
Bank of
Scenario 1 Y
Connected Filters
e. g., attribute thinning,
attribute thickening, ...
Application
Scenario 2
Knowledge
Scene Performance
Knowledge Scenario 3 Assessment
The proposed approach is composed of two modules that perform the operations
of:
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 9 / 19
14. General Approach for Image Simplication Methodology
Architecture of the Proposed General Approach
X
Parameters Selection
Bank of
Scenario 1 Y
Connected Filters
e. g., attribute thinning,
attribute thickening, ...
Application
Scenario 2
Knowledge
Scene Performance
Knowledge Scenario 3 Assessment
The proposed approach is composed of two modules that perform the operations
of:
1 selection of the parameters and operators;
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 9 / 19
15. General Approach for Image Simplication Methodology
Architecture of the Proposed General Approach
X
Parameters Selection
Bank of
Scenario 1 Y
Connected Filters
e. g., attribute thinning,
attribute thickening, ...
Application
Scenario 2
Knowledge
Scene Performance
Knowledge Scenario 3 Assessment
The proposed approach is composed of two modules that perform the operations
of:
1 selection of the parameters and operators;
2 ltering.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 9 / 19
16. General Approach for Image Simplication Methodology
Filter Selection and Operative Scenarios
Operative Scenarios
Information available as prior
knowledge: X
Parameters Selection
1 Scenario 1
%
Scene Knowledge Scenario 1
Bank of
Y
%
Connected Filters
Application Knowledge e. g., attribute thinning,
2 Scenario 2 Application
attribute thickening, ...
%
Scene Knowledge
Knowledge
Scenario 2
Application Knowledge
3 Scenario 3 Scene
Scenario 3
Performance
Knowledge Assessment
Scene Knowledge
Application Knowledge
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 10 / 19
17. General Approach for Image Simplication Methodology
Filter Selection and Operative Scenarios
Operative Scenarios
Information available as prior
knowledge: X
Parameters Selection
1 Scenario 1
%
Scene Knowledge Scenario 1
Bank of
Y
%
Connected Filters
Application Knowledge e. g., attribute thinning,
2 Scenario 2 Application
attribute thickening, ...
%
Scene Knowledge
Knowledge
Scenario 2
Application Knowledge
3 Scenario 3 Scene
Scenario 3
Performance
Knowledge Assessment
Scene Knowledge
Application Knowledge
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 10 / 19
18. General Approach for Image Simplication Methodology
Filter Selection and Operative Scenarios
Operative Scenarios
Information available as prior
knowledge: X
Parameters Selection
1 Scenario 1
%
Scene Knowledge Scenario 1
Bank of
Y
%
Connected Filters
Application Knowledge e. g., attribute thinning,
2 Scenario 2 Application
attribute thickening, ...
%
Scene Knowledge
Knowledge
Scenario 2
Application Knowledge
3 Scenario 3 Scene
Scenario 3
Performance
Knowledge Assessment
Scene Knowledge
Application Knowledge
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 10 / 19
19. General Approach for Image Simplication Methodology
Data set
Quickbird
panchromatic
image of 995×995
pixels, 0.6 m
resolution.
Acquired over a
residential urban
area of Bam, Iran.
Panchromatic image
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 11 / 19
20. General Approach for Image Simplication Methodology
Scenario 1 - Application Knowledge %, Scene Knowledge %
Aim: Generic reduction of the complexity of the image by reducing non
informative components.
Filtering aiming at reducing:
1 Noisy components ⇒ Removing small regions with values signicantly dierent
from their surroundings;
2 Inter-object variability ⇒ Flattening small values dierences in homogeneous
regions.
Suitable to cope with most of the applications.
Eases the interpretation of the scene.
Exploits the contextual relations of the pixels.
Fully automatic suitable for batch processing.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 12 / 19
21. General Approach for Image Simplication Methodology
Scenario 1 - Example
Generic simplication performed by:
γ T φT with area attribute (remove small bright and dark regions);
γ T with T based on relations between the regions1 (merge nested regions).
VHR image
Building rooftop (80×60 pixels). 2789 at regions. Simplied image. 1059 regions.
1 V. Caselles and P. Monasse, Geometric Description of Images as Topographic Maps. Springer, 2010.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 13 / 19
22. General Approach for Image Simplication Methodology
Scenario 2 - Application Knowledge , Scene Knowledge %
Filters parameters selected according to the application.
The translation of the characteristics of the objects of interest from the
concept to the lter parameters.
Example:
Application: building extraction.
Aim of the simplication: enhance rectangular regions
Concept: keep rectangular regions.
Filtering: attribute lter with criterion: {rectangularity 0.5});
Automation
Modeling the range of values of the features that drive the lters with fuzzy
possibilistic functions.
Defuzzify in order to get the values for the lters parameters.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 14 / 19
23. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Buildings (Particulars)
Panchromatic image (f )
Filter by rec.
B
f − γR (f ) (B : disk radius 7 pixels)
Attribute lter γ T (f ) with T = (R 0.3) ∧ (I 0.5) ∧ (50 A 5000)
R: rectangularity; I: moment of inertia; A: area
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
24. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Buildings (Particulars)
Panchromatic image (f )
Filter by rec.
B
f − γR (f ) (B : disk radius 11 pixels)
Attribute lter γ T (f ) with T = (R 0.3) ∧ (I 0.5) ∧ (50 A 5000)
R: rectangularity; I: moment of inertia; A: area
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
25. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Buildings (Particulars)
Panchromatic image (f )
Filter by rec.
B
f − γR (f ) (B : disk radius 15 pixels)
Attribute lter γ T (f ) with T = (R 0.3) ∧ (I 0.5) ∧ (50 A 5000)
R: rectangularity; I: moment of inertia; A: area
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
26. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Buildings (Particulars)
Panchromatic image (f )
Filter by rec.
B
f − γR (f ) (B : disk radius 19 pixels)
Attribute lter γ T (f ) with T = (R 0.3) ∧ (I 0.5) ∧ (50 A 5000)
R: rectangularity; I: moment of inertia; A: area
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
27. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Dark Elongated Structures (Particulars)
Panchromatic image (f )
Filter by rec. C[φB (f ) − f ] (B :
R
disk radius 3 pixels)
Attribute lter φT (f ) with T = (H 10000) ∧ (I 1.0)
H: height; I: moment of inertia
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
28. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Dark Elongated Structures (Particulars)
Panchromatic image (f )
Filter by rec. C[φB (f ) − f ] (B :
R
disk radius 7 pixels)
Attribute lter φT (f ) with T = (H 10000) ∧ (I 1.0)
H: height; I: moment of inertia
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
29. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Dark Elongated Structures (Particulars)
Panchromatic image (f )
Filter by rec. C[φB (f ) − f ] (B :
R
disk radius 11 pixels)
Attribute lter φT (f ) with T = (H 10000) ∧ (I 1.0)
H: height; I: moment of inertia
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
30. General Approach for Image Simplication Methodology
Scenario 2 - Enhancement of Dark Elongated Structures (Particulars)
Panchromatic image (f )
Filter by rec. C[φB (f ) − f ] (B :
R
disk radius 15 pixels)
Attribute lter φT (f ) with T = (H 10000) ∧ (I 1.0)
H: height; I: moment of inertia
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
31. General Approach for Image Simplication Methodology
Scenario 3 - Application Knowledge , Scene Knowledge
Ad hoc parameters selection. If the available information is a set of labeled
samples (i.e., a training set), the reduction of the image complexity
generated by the ltering aims at increasing the separability of the classes.
Performance assessment. The quality of the simplication obtained can be
evaluated on the known samples according to a given criterion.
Automation
Dene a cost function to minimize, representing the tness of the generated
result with the input requirements;
Dene a optimization procedure and a stopping condition.
See on Thurstday: S. Peeters, P. R. Marpu, J. A. Benediktsson, M. Dalla Mura Classication using extended
morphological attribute proles based on dierent feature extraction techniques.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 17 / 19
32. Conclusion and Future Developments
Conclusion and Future Developments
Conclusion
Denition of a novel general approach for image simplication based on
connected lters (in particular attribute lters).
Suitable for
processing many dierent image types;
dierent applications involving image analysis.
Contributions:
identication of three scenarios modeling common dierent operative
conditions;
giving guidelines for the automation of the process;
qualitative evaluation of the proposed approach on a real data set in dierent
scenarios.
Future Developments
Extensively test the approach on dierent type of images and applications.
Improve the automation of the process.
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 18 / 19
33. Conclusion and Future Developments
Thanks for your attention!
IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 19 / 19