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In this project, an efficient method is proposed to remove the high level of salt and pepper noise in image using median filter. The objective of any noise removal technique is to remove the noise completely from the image, such that the resulting image is better than original image. Salt and pepper noise can be added to image during acquisition process. This noise is removed by the proposed method. This plays a vital role in digital images. The proposed approach is used to improve the quality of the image by removing high tone salt and pepper noise. The time consumption for removing salt and pepper noise in image can be reduced in this approach. The verification is based on filtered images. The resultant image is the converted into binary code. Then the binary code of the resultant image is simulated in Xilinx software. Then it is implemented in FPGA kit using Xilinx software. The resultant image will have good noise removal efficiency.

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map 作者： XU Tao YU Huan QIU Xia KONG Bo XIANG Qing XU Xiaoyu FU Hao 作者单位： 1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; 2. Beijing SuperMap Software Co., Ltd., Beijing 100015, China; 3. Sichuan Real Estate Registration Center, Chengdu 610014, China; 4. Chengdu Institute of Mountain Land and Disasters, Chinese Academy of Sciences, Chengdu 610041, China; 5. School of Earth Systems and Sustainability, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 6. Environmental Resources and Policy, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 7. Sichuan Institute of Land and Space Ecological Restoration and Geohazard Prevention, Chengdu 610081, China 通讯作者： YU Huan Email:[email protected] 提交时间： 2023-03-19 self-organizing map digital image processing morphological characteristics multivariate statistical method environmental monitoring 期刊： Journal of Arid Land 分类： 地球科学 >> 地理学 引用： ChinaXiv:202303.00188 (或此版本 ChinaXiv:202303.00188V1 ) DOI:10.1007/s40333-023-0010-y CSTR:32003.36.ChinaXiv.202303.00188.V1 推荐引用方式： XU Tao,YU Huan,QIU Xia,KONG Bo,XIANG Qing,XU Xiaoyu,FU Hao.(2023).Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map.Journal of Arid Land.doi:10.1007/s40333-023-0010-y 版本历史 [V1] 2023-03-19 22:30:43 ChinaXiv:202303.00188V1 下载全文

The use of good quality seed is important for the satisfactory production of a good quality crop and is essential for export in markets. Quality control is very important in food industry because based on quality of food products are classified and graded into different grades. Quality of grain is evaluated manually but it is very much, time consuming, may be varying results and costly. Digital image processing can be used for quality evaluation and grading of agricultural food in industry. One of the major step in quality analysis of grains is grading of grains, which is normally done either manually or using expensive electronics equipments. We propose a simple method based on image processing and using Artificial Neural Network for grading of rice granules Here grading will be performed by calculating rice seed major axis length ,miner axis length, area ,eccentricity and perimeter. Result will be displayed on LCD and also there is an audio message . The paper presents a solution of grading and analysis of rice grains on the basis of grain size and shape using image processing techniques and Artificial neural network. This method requires minimum time and it is low in cost. Miss Shivpriya Desai | Dr.A.P.Rao"Seed Quality Analysis Using Image Processing and ANN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd137.pdf Article URL: http://www.ijtsrd.com/engineering/computer-engineering/137/seed-quality-analysis-using-image-processing-and-ann/miss-shivpriya-desai

The use of good quality seed is important for the satisfactory production of a good quality crop and is essential for export in markets. Quality control is very important in food industry because based on quality of food products are classified and graded into different grades. Quality of grain is evaluated manually but it is very much, time consuming, may be varying results and costly. Digital image processing can be used for quality evaluation and grading of agricultural food in industry. One of the major step in quality analysis of grains is grading of grains, which is normally done either manually or using expensive electronics equipments. We propose a simple method based on image processing and using Artificial Neural Network for grading of rice granules Here grading will be performed by calculating rice seed major axis length ,miner axis length, area ,eccentricity and perimeter. Result will be displayed on LCD and also there is an audio message . The paper presents a solution of grading and analysis of rice grains on the basis of grain size and shape using image processing techniques and Artificial neural network. This method requires minimum time and it is low in cost. Miss Shivpriya Desai | Dr.A.P.Rao"Seed Quality Analysis Using Image Processing and ANN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd137.pdf Article URL: http://www.ijtsrd.com/engineering/computer-engineering/137/seed-quality-analysis-using-image-processing-and-ann/miss-shivpriya-desai

Although many equipments and techniques are available for plantar pressure analysis to study foot pressure distributions, there is still a need for mathematical modelling references to compare the acquired measurements. In order to derive formulas in this concern, this research proposes a measurement-based method which adopts the reference measured parameters such as; the weight of a subject, contact-area size, age, and the pressure level distribution over a plantar image captured by the EMED plantar pressure system. The proposed analysis and algorithm were verified by a group 79 volunteers through data collection with four various measurement conditions. Three mathematical modelling equations have been proposed that describe the relationships between the foot plantar pressure levels and the subject’s body mass, foot size, and age. The modelling of foot plantar pressure could be useful for various applications such as gait analysis, hospitals, clinics, custom shoe making, and early detection of ulceration in the case of diabetic patients.

Although many equipments and techniques are available for plantar pressure analysis to study foot pressure distributions, there is still a need for mathematical modelling references to compare the acquired measurements. In order to derive formulas in this concern, this research proposes a measurement-based method which adopts the reference measured parameters such as; the weight of a subject, contact-area size, age, and the pressure level distribution over a plantar image captured by the EMED plantar pressure system. The proposed analysis and algorithm were verified by a group 79 volunteers through data collection with four various measurement conditions. Three mathematical modelling equations have been proposed that describe the relationships between the foot plantar pressure levels and the subject’s body mass, foot size, and age. The modelling of foot plantar pressure could be useful for various applications such as gait analysis, hospitals, clinics, custom shoe making, and early detection of ulceration in the case of diabetic patients.

Although many equipments and techniques are available for plantar pressure analysis to study foot pressure distributions, there is still a need for mathematical modelling references to compare the acquired measurements. In order to derive formulas in this concern, this research proposes a measurement-based method which adopts the reference measured parameters such as; the weight of a subject, contact-area size, age, and the pressure level distribution over a plantar image captured by the EMED plantar pressure system. The proposed analysis and algorithm were verified by a group 79 volunteers through data collection with four various measurement conditions. Three mathematical modelling equations have been proposed that describe the relationships between the foot plantar pressure levels and the subject’s body mass, foot size, and age. The modelling of foot plantar pressure could be useful for various applications such as gait analysis, hospitals, clinics, custom shoe making, and early detection of ulceration in the case of diabetic patients.

Although many equipments and techniques are available for plantar pressure analysis to study foot pressure distributions, there is still a need for mathematical modelling references to compare the acquired measurements. In order to derive formulas in this concern, this research proposes a measurement-based method which adopts the reference measured parameters such as; the weight of a subject, contact-area size, age, and the pressure level distribution over a plantar image captured by the EMED plantar pressure system. The proposed analysis and algorithm were verified by a group 79 volunteers through data collection with four various measurement conditions. Three mathematical modelling equations have been proposed that describe the relationships between the foot plantar pressure levels and the subject’s body mass, foot size, and age. The modelling of foot plantar pressure could be useful for various applications such as gait analysis, hospitals, clinics, custom shoe making, and early detection of ulceration in the case of diabetic patients.

Although many equipments and techniques are available for plantar pressure analysis to study foot pressure distributions, there is still a need for mathematical modelling references to compare the acquired measurements. In order to derive formulas in this concern, this research proposes a measurement-based method which adopts the reference measured parameters such as; the weight of a subject, contact-area size, age, and the pressure level distribution over a plantar image captured by the EMED plantar pressure system. The proposed analysis and algorithm were verified by a group 79 volunteers through data collection with four various measurement conditions. Three mathematical modelling equations have been proposed that describe the relationships between the foot plantar pressure levels and the subject’s body mass, foot size, and age. The modelling of foot plantar pressure could be useful for various applications such as gait analysis, hospitals, clinics, custom shoe making, and early detection of ulceration in the case of diabetic patients.

Software calibration data and an optimum sequence for image processing technique application are developed for use in processing of S-056 telescope images.

Software calibration data and an optimum sequence for image processing technique application are developed for use in processing of S-056 telescope images.

Software calibration data and an optimum sequence for image processing technique application are developed for use in processing of S-056 telescope images.

The use of good quality seed is important for the satisfactory production of a good quality crop and is essential for export in markets. Quality control is very important in food industry because based on quality of food products are classified and graded into different grades. Quality of grain is evaluated manually but it is very much, time consuming, may be varying results and costly. Digital image processing can be used for quality evaluation and grading of agricultural food in industry. One of the major step in quality analysis of grains is grading of grains, which is normally done either manually or using expensive electronics equipments. We propose a simple method based on image processing and using Artificial Neural Network for grading of rice granules Here grading will be performed by calculating rice seed major axis length ,miner axis length, area ,eccentricity and perimeter. Result will be displayed on LCD and also there is an audio message . The paper presents a solution of grading and analysis of rice grains on the basis of grain size and shape using image processing techniques and Artificial neural network. This method requires minimum time and it is low in cost. Miss Shivpriya Desai | Dr.A.P.Rao"Seed Quality Analysis Using Image Processing and ANN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd137.pdf  http://www.ijtsrd.com/engineering/computer-engineering/137/seed-quality-analysis-using-image-processing-and-ann/miss-shivpriya-desai

The use of good quality seed is important for the satisfactory production of a good quality crop and is essential for export in markets. Quality control is very important in food industry because based on quality of food products are classified and graded into different grades. Quality of grain is evaluated manually but it is very much, time consuming, may be varying results and costly. Digital image processing can be used for quality evaluation and grading of agricultural food in industry. One of the major step in quality analysis of grains is grading of grains, which is normally done either manually or using expensive electronics equipments. We propose a simple method based on image processing and using Artificial Neural Network for grading of rice granules Here grading will be performed by calculating rice seed major axis length ,miner axis length, area ,eccentricity and perimeter. Result will be displayed on LCD and also there is an audio message . The paper presents a solution of grading and analysis of rice grains on the basis of grain size and shape using image processing techniques and Artificial neural network. This method requires minimum time and it is low in cost. Miss Shivpriya Desai | Dr.A.P.Rao"Seed Quality Analysis Using Image Processing and ANN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd137.pdf  http://www.ijtsrd.com/engineering/computer-engineering/137/seed-quality-analysis-using-image-processing-and-ann/miss-shivpriya-desai

The characteristic of low probability of intercept (LPI) radar makes it difficult to intercept with conventional signal intelligence methods so new interception methods need to be devel-oped. This thesis initially describes a simulation of a polytime phase-coded LPI signal. The thesis then introduces a method for classification of LPI radar signals. The method utilizes a parallel tree structure with three separate \"branches\" to exploit the image representation formed by three separate detection methods. Each detection method output is pre-processed and fea-tures are extracted using image processing. After processing the images, they are each fed into three separate neural networks to be classified. The classification output of each neural network is then combined and fed into a fourth neural network performing the final classification. The outcome of testing shows only 53%, which might be the result of the image representation of the detection methods not being distinct enough, the pre -processing/feature extraction not be-ing able to extract relevant information or the neural networks not being properly trained. The thesis concludes with a brief discussion about a suitable method for image processing to extract significant parameters from a LPI signal.

The characteristic of low probability of intercept (LPI) radar makes it difficult to intercept with conventional signal intelligence methods so new interception methods need to be devel-oped. This thesis initially describes a simulation of a polytime phase-coded LPI signal. The thesis then introduces a method for classification of LPI radar signals. The method utilizes a parallel tree structure with three separate \"branches\" to exploit the image representation formed by three separate detection methods. Each detection method output is pre-processed and fea-tures are extracted using image processing. After processing the images, they are each fed into three separate neural networks to be classified. The classification output of each neural network is then combined and fed into a fourth neural network performing the final classification. The outcome of testing shows only 53%, which might be the result of the image representation of the detection methods not being distinct enough, the pre -processing/feature extraction not be-ing able to extract relevant information or the neural networks not being properly trained. The thesis concludes with a brief discussion about a suitable method for image processing to extract significant parameters from a LPI signal.

The design and operation of a microprocessor based data acquisition system is described. The system is capable of digitising and storing a frame of pictorial information from an AGA Thermovision and provides facilities for the analysis of the stored information. Transfer of the stored information to the IBM 370/168 can be achieved at high speed and sophisticated processing applied to the stored data. The use of a microprocessor as the controlling unit has allowed the implementation of some analysis and processing facilities at the experimental site.

The design and operation of a microprocessor based data acquisition system is described. The system is capable of digitising and storing a frame of pictorial information from an AGA Thermovision and provides facilities for the analysis of the stored information. Transfer of the stored information to the IBM 370/168 can be achieved at high speed and sophisticated processing applied to the stored data. The use of a microprocessor as the controlling unit has allowed the implementation of some analysis and processing facilities at the experimental site.

Powerful techniques have been developed in recent years for the analysis of digital data, especially the manipulation of images. This book provides an in-depth introduction to a range of these innovative, avant-garde data-processing techniques. It develops the reader's understanding of each technique and then shows with practical examples how they can be applied to improve the skills of graduate students and researchers in astronomy, electrical engineering, physics, geophysics and medical imaging. What sets this book apart from others on the subject is the complementary blend of theory and practical application. Throughout, it is copiously illustrated with real-world examples from astronomy, electrical engineering, remote sensing and medicine. It also shows how many, more traditional, methods can be enhanced by incorporating the new wavelet and multiscale methods into the processing. For graduate students and researchers already experienced in image processing and data analysis, this book provides an indispensable guide to a wide range of exciting and original data-analysis techniques. Lecture Notes Collection FreeScience.info ID2239 Obtained from http://www.multiresolution.com/cupbook.pdf http://www.freescience.info/go.php?pagename=books&id=2239

The Archimedes Palimpsest is a 10th-century parchment manuscript that was erased in the 13th century and overwritten with a Christian prayer book. The Walters Art Museum in Baltimore, MD, USA has supervised a ten-year collaboration by conservators, imaging scientists, and scholars to image and transcribe the original writings. This paper reports on the variety of optical imaging and image processing techniques used in the project. Image analysis tools to clarify texts included supervised segmentation via a spectral pseudoinverse calculation, deterministic renderings in pseudocolor, and statistical analysis with dynamical pseudocolor rendering.

The Archimedes Palimpsest is a 10th-century parchment manuscript that was erased in the 13th century and overwritten with a Christian prayer book. The Walters Art Museum in Baltimore, MD, USA has supervised a ten-year collaboration by conservators, imaging scientists, and scholars to image and transcribe the original writings. This paper reports on the variety of optical imaging and image processing techniques used in the project. Image analysis tools to clarify texts included supervised segmentation via a spectral pseudoinverse calculation, deterministic renderings in pseudocolor, and statistical analysis with dynamical pseudocolor rendering.

The Archimedes Palimpsest is a 10th-century parchment manuscript that was erased in the 13th century and overwritten with a Christian prayer book. The Walters Art Museum in Baltimore, MD, USA has supervised a ten-year collaboration by conservators, imaging scientists, and scholars to image and transcribe the original writings. This paper reports on the variety of optical imaging and image processing techniques used in the project. Image analysis tools to clarify texts included supervised segmentation via a spectral pseudoinverse calculation, deterministic renderings in pseudocolor, and statistical analysis with dynamical pseudocolor rendering.

The Archimedes Palimpsest is a 10th-century parchment manuscript that was erased in the 13th century and overwritten with a Christian prayer book. The Walters Art Museum in Baltimore, MD, USA has supervised a ten-year collaboration by conservators, imaging scientists, and scholars to image and transcribe the original writings. This paper reports on the variety of optical imaging and image processing techniques used in the project. Image analysis tools to clarify texts included supervised segmentation via a spectral pseudoinverse calculation, deterministic renderings in pseudocolor, and statistical analysis with dynamical pseudocolor rendering.

The use of good quality seed is important for the satisfactory production of a good quality crop and is essential for export in markets. Quality control is very important in food industry because based on quality of food products are classified and graded into different grades. Quality of grain is evaluated manually but it is very much, time consuming, may be varying results and costly. Digital image processing can be used for quality evaluation and grading of agricultural food in industry. One of the major step in quality analysis of grains is grading of grains, which is normally done either manually or using expensive electronics equipments. We propose a simple method based on image processing and using Artificial Neural Network for grading of rice granules Here grading will be performed by calculating rice seed major axis length ,miner axis length, area ,eccentricity and perimeter. Result will be displayed on LCD and also there is an audio message . The paper presents a solution of grading and analysis of rice grains on the basis of grain size and shape using image processing techniques and Artificial neural network. This method requires minimum time and it is low in cost. Miss Shivpriya Desai | Dr.A.P.Rao"Seed Quality Analysis Using Image Processing and ANN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd137.pdf Article URL: http://www.ijtsrd.com/engineering/computer-engineering/137/seed-quality-analysis-using-image-processing-and-ann/miss-shivpriya-desai

We first describe results on shape-base image contrast enhancement. This work shows how to perform local contrast enhancement while preserving the shapes in the image. We have transferred this software to Dr. Szymczak from Physical Acoustics at the Naval Research Laboratory for testing on underwater laser images (LIDAR). We then show a novel approach to anisotropic diffusion. This approach is based on robust statistics, and in particular, in the theory of influence functions. This technique is, for example, of particular significance for image denoising prior to segmentation. We conclude this document with a description of a novel technique of incorporating prior information in anisotropic diffusion. The idea is to use Bayes rule to compute posterior distributions, and then, regularize those distributions via partial differential equations before the MAP is computed. Although a number of results have already been obtained in these areas, the work described in this document is still in progress. As was mentioned above, we want to extend the work on shape-preserving contrast enhancement to include additional definitions of shape and adapt it to specific applications. We are also planing to extend the robust framework for anisotropic diffusion to vector-valued data, and investigate fast implementations. We plan to further investigate the underlying theory of the posterior diffusion work, and to apply it to additional problems. The work described in this document opens a number of theoretical questions that we plan to address as well.

We first describe results on shape-base image contrast enhancement. This work shows how to perform local contrast enhancement while preserving the shapes in the image. We have transferred this software to Dr. Szymczak from Physical Acoustics at the Naval Research Laboratory for testing on underwater laser images (LIDAR). We then show a novel approach to anisotropic diffusion. This approach is based on robust statistics, and in particular, in the theory of influence functions. This technique is, for example, of particular significance for image denoising prior to segmentation. We conclude this document with a description of a novel technique of incorporating prior information in anisotropic diffusion. The idea is to use Bayes rule to compute posterior distributions, and then, regularize those distributions via partial differential equations before the MAP is computed. Although a number of results have already been obtained in these areas, the work described in this document is still in progress. As was mentioned above, we want to extend the work on shape-preserving contrast enhancement to include additional definitions of shape and adapt it to specific applications. We are also planing to extend the robust framework for anisotropic diffusion to vector-valued data, and investigate fast implementations. We plan to further investigate the underlying theory of the posterior diffusion work, and to apply it to additional problems. The work described in this document opens a number of theoretical questions that we plan to address as well.

We first describe results on shape-base image contrast enhancement. This work shows how to perform local contrast enhancement while preserving the shapes in the image. We have transferred this software to Dr. Szymczak from Physical Acoustics at the Naval Research Laboratory for testing on underwater laser images (LIDAR). We then show a novel approach to anisotropic diffusion. This approach is based on robust statistics, and in particular, in the theory of influence functions. This technique is, for example, of particular significance for image denoising prior to segmentation. We conclude this document with a description of a novel technique of incorporating prior information in anisotropic diffusion. The idea is to use Bayes rule to compute posterior distributions, and then, regularize those distributions via partial differential equations before the MAP is computed. Although a number of results have already been obtained in these areas, the work described in this document is still in progress. As was mentioned above, we want to extend the work on shape-preserving contrast enhancement to include additional definitions of shape and adapt it to specific applications. We are also planing to extend the robust framework for anisotropic diffusion to vector-valued data, and investigate fast implementations. We plan to further investigate the underlying theory of the posterior diffusion work, and to apply it to additional problems. The work described in this document opens a number of theoretical questions that we plan to address as well.

We first describe results on shape-base image contrast enhancement. This work shows how to perform local contrast enhancement while preserving the shapes in the image. We have transferred this software to Dr. Szymczak from Physical Acoustics at the Naval Research Laboratory for testing on underwater laser images (LIDAR). We then show a novel approach to anisotropic diffusion. This approach is based on robust statistics, and in particular, in the theory of influence functions. This technique is, for example, of particular significance for image denoising prior to segmentation. We conclude this document with a description of a novel technique of incorporating prior information in anisotropic diffusion. The idea is to use Bayes rule to compute posterior distributions, and then, regularize those distributions via partial differential equations before the MAP is computed. Although a number of results have already been obtained in these areas, the work described in this document is still in progress. As was mentioned above, we want to extend the work on shape-preserving contrast enhancement to include additional definitions of shape and adapt it to specific applications. We are also planing to extend the robust framework for anisotropic diffusion to vector-valued data, and investigate fast implementations. We plan to further investigate the underlying theory of the posterior diffusion work, and to apply it to additional problems. The work described in this document opens a number of theoretical questions that we plan to address as well.

We first describe results on shape-base image contrast enhancement. This work shows how to perform local contrast enhancement while preserving the shapes in the image. We have transferred this software to Dr. Szymczak from Physical Acoustics at the Naval Research Laboratory for testing on underwater laser images (LIDAR). We then show a novel approach to anisotropic diffusion. This approach is based on robust statistics, and in particular, in the theory of influence functions. This technique is, for example, of particular significance for image denoising prior to segmentation. We conclude this document with a description of a novel technique of incorporating prior information in anisotropic diffusion. The idea is to use Bayes rule to compute posterior distributions, and then, regularize those distributions via partial differential equations before the MAP is computed. Although a number of results have already been obtained in these areas, the work described in this document is still in progress. As was mentioned above, we want to extend the work on shape-preserving contrast enhancement to include additional definitions of shape and adapt it to specific applications. We are also planing to extend the robust framework for anisotropic diffusion to vector-valued data, and investigate fast implementations. We plan to further investigate the underlying theory of the posterior diffusion work, and to apply it to additional problems. The work described in this document opens a number of theoretical questions that we plan to address as well.

We first describe results on shape-base image contrast enhancement. This work shows how to perform local contrast enhancement while preserving the shapes in the image. We have transferred this software to Dr. Szymczak from Physical Acoustics at the Naval Research Laboratory for testing on underwater laser images (LIDAR). We then show a novel approach to anisotropic diffusion. This approach is based on robust statistics, and in particular, in the theory of influence functions. This technique is, for example, of particular significance for image denoising prior to segmentation. We conclude this document with a description of a novel technique of incorporating prior information in anisotropic diffusion. The idea is to use Bayes rule to compute posterior distributions, and then, regularize those distributions via partial differential equations before the MAP is computed. Although a number of results have already been obtained in these areas, the work described in this document is still in progress. As was mentioned above, we want to extend the work on shape-preserving contrast enhancement to include additional definitions of shape and adapt it to specific applications. We are also planing to extend the robust framework for anisotropic diffusion to vector-valued data, and investigate fast implementations. We plan to further investigate the underlying theory of the posterior diffusion work, and to apply it to additional problems. The work described in this document opens a number of theoretical questions that we plan to address as well.

This research in support of the Air Force Research Laboratory Materials and Manufacturing Directorate was conducted from 11 June 2010 through 18 April 2013. The objective of this study was to develop a flexible and portable software frame work to manage evolving software for importing, manipulating and archiving microstructural images and data.

This research in support of the Air Force Research Laboratory Materials and Manufacturing Directorate was conducted from 11 June 2010 through 18 April 2013. The objective of this study was to develop a flexible and portable software frame work to manage evolving software for importing, manipulating and archiving microstructural images and data.

This research in support of the Air Force Research Laboratory Materials and Manufacturing Directorate was conducted from 11 June 2010 through 18 April 2013. The objective of this study was to develop a flexible and portable software frame work to manage evolving software for importing, manipulating and archiving microstructural images and data.

This research in support of the Air Force Research Laboratory Materials and Manufacturing Directorate was conducted from 11 June 2010 through 18 April 2013. The objective of this study was to develop a flexible and portable software frame work to manage evolving software for importing, manipulating and archiving microstructural images and data.

This research in support of the Air Force Research Laboratory Materials and Manufacturing Directorate was conducted from 11 June 2010 through 18 April 2013. The objective of this study was to develop a flexible and portable software frame work to manage evolving software for importing, manipulating and archiving microstructural images and data.

The plant requires thirteen different nutrients. The two main types of nutrients are micronutrients and macronutrients. Diseases develop due to deficiency of vital nutrients, resulting in colored spots on the leaves. Plant development is affected by toxicity or lack of one or more of these nutrients, resulting in plant death. As a result, a continuous monitoring system is necessary to know the nutritional status of the plants to enhance production efficiency and output. Optical image recognition-based medical technology can identify indicators of inaccuracy faster than the human eye. Consequently, farmers are prepared to take prompt and effective remedial action. This article investigates the nutrient deficits in plants using image processing techniques.

Overview: Wavelet based methods: Wavelets for image representation and JPEG 2000; Non-separable wavelets; Wavelet Denoising. IFS based methods: IFS fractal basics IFS on images; IFS image compression basics and variations; Fractal denoising; Mixing IFS and wavelets; IFS in Content Based Image Retrieval. Multifractal based methods: Basic introduction; Edge detection; Denoising.

We present several graph-based algorithms for image processing and classification of high-dimensional data. The first (semi-supervised) method uses a graph adaptation of the classical numerical Merriman-Bence-Osher (MBO) scheme, and can be extended to the multiclass case via the Gibbs simplex. We show examples of the application of the algorithm in the areas of image inpainting (both binary and grayscale), image segmentation and classification on benchmark data sets. We have also applied this algorithm to the problem of object detection using hyper-spectral video sequences as a data set. In addition, a second related model is introduced. It uses a diffuse interface model based on the Ginzburg-Landau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex with the functional's double-well potential modified to handle the multiclass case. The version minimizes the functional using a convex splitting numerical scheme. In our computations, we make use of fast numerical solvers for finding the eigenvectors and eigenvalues of the graph Laplacian and take advantage of the sparsity of the matrix. Experiments on benchmark data sets show that our models produce results that are comparable with or outperform the state-of-the-art algorithms. The second (semi-supervised) method develops a global minimization framework for binary classification of high-dimensional data. It combines recent convex optimization methods for image processing with recent graph based variational models for data segmentation. Two convex splitting algorithms are proposed, where graph-based PDE techniques are used to solve some of the subproblems. It is shown that global minimizers can be guaranteed for semi-supervised segmentation with two regions.

In this project, an efficient method is proposed to remove the high level of salt and pepper noise in image using median filter. The objective of any noise removal technique is to remove the noise completely from the image, such that the resulting image is better than original image. Salt and pepper noise can be added to image during acquisition process. This noise is removed by the proposed method. This plays a vital role in digital images. The proposed approach is used to improve the quality of the image by removing high tone salt and pepper noise. The time consumption for removing salt and pepper noise in image can be reduced in this approach. The verification is based on filtered images. The resultant image is the converted into binary code. Then the binary code of the resultant image is simulated in Xilinx software. Then it is implemented in FPGA kit using Xilinx software. The resultant image will have good noise removal efficiency.

This Report describes a series of techniques for processing and analysing images. Although developed for a specific purpose, namely the automatic reading of angular information of Askania kinetheodolite film, most of the techniques are quite general, and potentially applicable to a wide variety of problems. The processes described include real time binarisation of a television signal, production and analysis of projections to determine the positions of features of interest, and character recognition. (Author)

The characteristic of low probability of intercept (LPI) radar makes it difficult to intercept with conventional signal intelligence methods so new interception methods need to be developed. This thesis initially describes a simulation of a polytime phase-coded LPI signal. The thesis then introduces a method for classification of LPI radar signals. The method utilizes a parallel tree structure with three separate 'branches' to exploit the image representation formed by three separate detection methods. Each detection method output is pre-processed and features are extracted using image processing. After processing the images, they are each fed into three separate neural networks to be classified. The classification output of each neural network is then combined and fed into a fourth neural network performing the final classification. The outcome of testing shows only 53%, which might be the result of the image representation of the detection methods not being distinct enough, the pre -processing/feature extraction not being able to extract relevant information or the neural networks not being properly trained. The thesis concludes with a brief discussion about a suitable method for image processing to extract significant parameters from a LPI signal.

The characteristic of low probability of intercept (LPI) radar makes it difficult to intercept with conventional signal intelligence methods so new interception methods need to be developed. This thesis initially describes a simulation of a polytime phase-coded LPI signal. The thesis then introduces a method for classification of LPI radar signals. The method utilizes a parallel tree structure with three separate 'branches' to exploit the image representation formed by three separate detection methods. Each detection method output is pre-processed and features are extracted using image processing. After processing the images, they are each fed into three separate neural networks to be classified. The classification output of each neural network is then combined and fed into a fourth neural network performing the final classification. The outcome of testing shows only 53%, which might be the result of the image representation of the detection methods not being distinct enough, the pre -processing/feature extraction not being able to extract relevant information or the neural networks not being properly trained. The thesis concludes with a brief discussion about a suitable method for image processing to extract significant parameters from a LPI signal.

B. Mandelbrot gave a new birth to the notions of scale invariance, selfsimilarity and non-integer dimensions, gathering them as the founding corner-stones used to build up fractal geometry. The first purpose of the present contribution is to review and relate together these key notions, explore their interplay and show that they are different facets of a same intuition. Second, it will explain how these notions lead to the derivation of the mathematical tools underlying multifractal analysis. Third, it will reformulate these theoretical tools into a wavelet framework, hence enabling their better theoretical understanding as well as their efficient practical implementation. B. Mandelbrot used his concept of fractal geometry to analyze real-world applications of very different natures. As a tribute to his work, applications of various origins, and where multifractal analysis proved fruitful, are revisited to illustrate the theoretical developments proposed here.

The use of NOAA Advanced Very High Resolution Radar/High Resolution Picture Transmission (AVHRR/HRPT) imagery for earth resource applications is provided for the applications scientist for use within the various Earth science, resource, and agricultural disciplines. A guide to processing NOAA AVHRR data using the hardware and software systems integrated for this NASA project is provided. The processing steps from raw data on computer compatible tapes (1B data format) through usable qualitative and quantitative products for applications are given. The manual is divided into two parts. The first section describes the NOAA satellite system, its sensors, and the theoretical basis for using these data for environmental applications. Part 2 is a hands-on description of how to use a specific image processing system, the International Imaging Systems, Inc. (I2S) Model 75 Array Processor and S575 software, to process these data.

This article is from BioMedical Engineering OnLine , volume 12 . Abstract Background: Efficacy and safety of various treatments using fractional laser or radiofrequency depend, to a large extent, on precise movement of equipment head across the patient’s skin. In addition, they both depend on uniform distribution of emitted pulses throughout the treated skin area. The pulses should be closely adjacent but they should not overlap. Pulse overlapping results in amplification of irradiation dose and carries the danger of unwanted effects. Methods: Images obtained in infrared mode (Flir SC5200 thermovision camera equipped with photon detector) were entered into Matlab environment. Thermal changes in the skin were forced by CO2RE laser. Proposed image analysis and processing methods enable automatic recognition of CO2RE laser sites of action, making possible to assess the correctness of performed cosmetic procedures. Results: 80 images were acquired and analyzed. Regions of interest (ROI) for the entire treatment field were determined automatically. In accordance with the proposed algorithm, laser-irradiated Li areas (ROI) were determined for the treatment area. On this basis, error values were calculated and expressed as percentage of area not covered by any irradiation dose (δo) and as percentage area which received double dose (δz). The respective values for the analyzed images were δo=17.87±10.5% and δz=1.97±1.5%, respectively. Conclusions: The presented method of verifying the correctness of performing low-invasive esthetic medical (cosmetic) procedures has proved itself numerous times in practice. Advantages of the method include: automatic determination of coverage error values δo and δz, non-invasive, sterile and remote-controlled thermovisual mode of measurements, and possibility of assessing dynamics of patient’s skin temperature changes.

This article is from BioMedical Engineering OnLine , volume 12 . Abstract Background: Efficacy and safety of various treatments using fractional laser or radiofrequency depend, to a large extent, on precise movement of equipment head across the patient’s skin. In addition, they both depend on uniform distribution of emitted pulses throughout the treated skin area. The pulses should be closely adjacent but they should not overlap. Pulse overlapping results in amplification of irradiation dose and carries the danger of unwanted effects. Methods: Images obtained in infrared mode (Flir SC5200 thermovision camera equipped with photon detector) were entered into Matlab environment. Thermal changes in the skin were forced by CO2RE laser. Proposed image analysis and processing methods enable automatic recognition of CO2RE laser sites of action, making possible to assess the correctness of performed cosmetic procedures. Results: 80 images were acquired and analyzed. Regions of interest (ROI) for the entire treatment field were determined automatically. In accordance with the proposed algorithm, laser-irradiated Li areas (ROI) were determined for the treatment area. On this basis, error values were calculated and expressed as percentage of area not covered by any irradiation dose (δo) and as percentage area which received double dose (δz). The respective values for the analyzed images were δo=17.87±10.5% and δz=1.97±1.5%, respectively. Conclusions: The presented method of verifying the correctness of performing low-invasive esthetic medical (cosmetic) procedures has proved itself numerous times in practice. Advantages of the method include: automatic determination of coverage error values δo and δz, non-invasive, sterile and remote-controlled thermovisual mode of measurements, and possibility of assessing dynamics of patient’s skin temperature changes.

This article is from BioMedical Engineering OnLine , volume 12 . Abstract Background: Efficacy and safety of various treatments using fractional laser or radiofrequency depend, to a large extent, on precise movement of equipment head across the patient’s skin. In addition, they both depend on uniform distribution of emitted pulses throughout the treated skin area. The pulses should be closely adjacent but they should not overlap. Pulse overlapping results in amplification of irradiation dose and carries the danger of unwanted effects. Methods: Images obtained in infrared mode (Flir SC5200 thermovision camera equipped with photon detector) were entered into Matlab environment. Thermal changes in the skin were forced by CO2RE laser. Proposed image analysis and processing methods enable automatic recognition of CO2RE laser sites of action, making possible to assess the correctness of performed cosmetic procedures. Results: 80 images were acquired and analyzed. Regions of interest (ROI) for the entire treatment field were determined automatically. In accordance with the proposed algorithm, laser-irradiated Li areas (ROI) were determined for the treatment area. On this basis, error values were calculated and expressed as percentage of area not covered by any irradiation dose (δo) and as percentage area which received double dose (δz). The respective values for the analyzed images were δo=17.87±10.5% and δz=1.97±1.5%, respectively. Conclusions: The presented method of verifying the correctness of performing low-invasive esthetic medical (cosmetic) procedures has proved itself numerous times in practice. Advantages of the method include: automatic determination of coverage error values δo and δz, non-invasive, sterile and remote-controlled thermovisual mode of measurements, and possibility of assessing dynamics of patient’s skin temperature changes.

This article is from BioMedical Engineering OnLine , volume 12 . Abstract Background: Efficacy and safety of various treatments using fractional laser or radiofrequency depend, to a large extent, on precise movement of equipment head across the patient’s skin. In addition, they both depend on uniform distribution of emitted pulses throughout the treated skin area. The pulses should be closely adjacent but they should not overlap. Pulse overlapping results in amplification of irradiation dose and carries the danger of unwanted effects. Methods: Images obtained in infrared mode (Flir SC5200 thermovision camera equipped with photon detector) were entered into Matlab environment. Thermal changes in the skin were forced by CO2RE laser. Proposed image analysis and processing methods enable automatic recognition of CO2RE laser sites of action, making possible to assess the correctness of performed cosmetic procedures. Results: 80 images were acquired and analyzed. Regions of interest (ROI) for the entire treatment field were determined automatically. In accordance with the proposed algorithm, laser-irradiated Li areas (ROI) were determined for the treatment area. On this basis, error values were calculated and expressed as percentage of area not covered by any irradiation dose (δo) and as percentage area which received double dose (δz). The respective values for the analyzed images were δo=17.87±10.5% and δz=1.97±1.5%, respectively. Conclusions: The presented method of verifying the correctness of performing low-invasive esthetic medical (cosmetic) procedures has proved itself numerous times in practice. Advantages of the method include: automatic determination of coverage error values δo and δz, non-invasive, sterile and remote-controlled thermovisual mode of measurements, and possibility of assessing dynamics of patient’s skin temperature changes.

Research on this contract was directed towards areas of mathematics and numerical computation which have applications to image/signal processing. The research can be broadly classified into the following areas: (1) sparse representations of functions and data, (2) adaptivity for generating sparse representations, (3) learning theory, (4) compression of digital elevation maps, and (5) wireless communication.