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From the bitsavers.org collection, a scanned-in computer-related document. components :: intel :: MMX :: Using MMX Technology In Digital Image Processing 1997

The study exhibits to understand the cracks generation and propagation inside the material as a source of the identification. The research is based primarily on the assessment of deflection, tension, and fracture propagation in structures through digital image processing (DIP) and its correlation. The DIP is a non-destructive assessment technique to capture microstructure pictures and loading in various loading scenarios. In order to monitor features and assign their position to a defined coordinate system, photogrammetry concepts are applied. The generated displacement data represents the typical movement present concerning the initial location in the centre of the tiny sub-images employed in the analysis. Aerial or satellite photos collected before and after the earthquake may currently be analyzed in techniques to deduce co-seismic terrain malformations with sub-pixel correlation. Due to the intrinsic diversity of natural faults and deformations, the interpretation of this data is not straightforward. The study of the correlation with either program clearly shows the relative shifts, the friction interface, the rupture arrest on the border, and wing fractures. The observations of displacement collected are transformed into strains using non-local denoising algorithms. This work is the first step in combining the digital picture association approach with high-speed photography to record transient dynamic rupture events. For measuring surface movement and tension in concrete, the DIP technology and the MATLAB® Program tracking have been employed.

Digital Image Processing dumped with WikiTeam tools.

An extensive computer modeling for polycapillary optics that includes realistic models for the effects of profile defects shows good agreement with measured data. Measured polycapillary fiber transmission is in excess of 65% at 20 keV for borosilicate glass optics, and is in excess of 42% for short leaded glass optics. Measured high angle transmission which corresponds to scatter transmission is lower than 1% for 12 cm long borosilicate optics at 20 kev and less than 4% for 3 cm long leaded glass optics at 80 keV. A long prototype borosilicate magnifying tapered monolithic optic has demonstrated contrast enhancement due to removal of scatter transmission, while at the same time increasing the system MTF performance at all spatial frequencies. Computer modeling, measurement and defect studies have led to better manufacturing capabilities for monolithic optics. The newly magnifying monolithic optics have a high transmission efficiency. Multitaper optics have been produced and tested. Image analysis have provided the means for the image artifact analysis and reduction.

Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it.  Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing.  The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various pre-processing technique for noise removal and image enhancement. Then the image is undergone to segmentation using Thresholding method. Some features of image have to be extracted using ABCD rules. In this work, Asymmetry index and Geometric features are extracted from the segmented image. These features are given as the input to classifier.  Artificial Neural Network ANN with feed forward architecture is used for classification purpose. It classifies the given image into cancerous or non-cancerous. The proposed algorithm has been tested on the ISIC International Skin Imaging Collaboration 2017 training and test datasets. The ground truth data of each image is available as well, so performance of this work can evaluate quantitatively.  by Khaing Thazin Oo | Dr. Moe Mon Myint | Dr. Khin Thuzar Win "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features"  Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018,  URL: http://www.ijtsrd.com/papers/ijtsrd18751.pdf Direct Link: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18751/skin-cancer-detection-using-digital-image-processing-and-implementation-using-ann-and-abcd-features/khaing-thazin-oo

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and nondestructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and nondestructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and nondestructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and nondestructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and nondestructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and nondestructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.

Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and nondestructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.

Lectures from the course webcast. Copied to archive.org on closure of the UCBerkeley webcast site in order to preserve access. Note: some lectures may appear to be missing (gap in numbering). This usually means that there was a holiday, exam, or no lecture on the day the recording was due, although sometimes it does mean that the recording is not available.

Lectures from the course webcast. Copied to archive.org on closure of the UCBerkeley webcast site in order to preserve access. Note: some lectures may appear to be missing (gap in numbering). This usually means that there was a holiday, exam, or no lecture on the day the recording was due, although sometimes it does mean that the recording is not available.

Applying image processing method to treat digital signals 作者： WANG Peng 1 ZHANG Ruan-Yu 2 YUAN Xue-Dong 2 XU Zu-Run 2 AN Zhu 1 作者单位： 1. Institute of Nuclear Science and Technology of Sichuan University, Key Laboratory of Radiation Physics and Technology, Ministry of Education, Chengdu 610064, China 2. The College of Physics Science and Technology of Sichuan University, Chengdu 610064, China 提交时间： 2023-06-18 摘要: In this paper, we present a novel method for digital nuclear signal processing based on image processing and recognition, which can improve signal-to-noise ratio of digital nuclear signal effectively without changing the signal shape. The digital nuclear signal with a time-amplitude series is converted into a grayscale image with adjustable pixel size. Template of the converted image is extracted by means of modern image processing methods, such as spatial digital low-pass filtering, image binary and the skeleton extracting of images. The needed parameters are extracted from the template image. The method of template extracting presented in this paper can be used flexibly to extract template of nuclear signals, whether the whole or even part of that, and got multi templates corresponding to the whole or partial characters of the signals. The results of image processing, along with -ray energy spectrum of 241Am acquired by this method, show that the new method provides a way to develop future digital nuclear instruments of high efficiency and flexibility, high density and multi parameters. Image of digital nuclear signal Skeleton extracting Waveform information extracting 期刊： Nuclear Science and Techniques 分类： 核科学技术 >> 核探测技术与核电子学 投稿状态： 已在期刊出版 引用： ChinaXiv:202306.00433 (或此版本 ChinaXiv:202306.00433V1 ) DOI:dx.doi.org/10.13538/j.1001-8042/nst.25.050403 CSTR:32003.36.ChinaXiv.202306.00433.V1 推荐引用方式： WANG Peng,ZHANG Ruan-Yu,YUAN Xue-Dong,XU Zu-Run,AN Zhu.(2023).Applying image processing method to treat digital signals.Nuclear Science and Techniques.doi:dx.doi.org/10.13538/j.1001-8042/nst.25.050403 版本历史 [V1] 2023-06-18 17:37:39 ChinaXiv:202306.00433V1 下载全文

Tri 1, 2022/2023

Trimester 1 2017/2018

Trimester 1 2017/2018

Trimester 1 2017/2018

Trimester 1 2017/2018

The ability of human pointing in digitized aerial imagery is studied. Automated methods for extracting edge locations in digital imagery are determined with subpixel accuracy. An algorithm for automatically extracting cross targets from aerial imagery is developed and implemented. Crosses are recognized using a method based on Fourier descriptors. Precise locations of the crosses are determined by Fourier descriptions and further refined using a least squares adjustment model. Mean cross location errors are as low as 0.03 pixels. Metric distortions caused by digital image processing techniques are studied using the Fourier descriptor-least squares algorithms. Cross positions are 'shifted' by as much as 0.5 pixels when cosine compression is applied with a 16-fold bit reduction. Mean and median filters show little metric distortions. An isotropic median filter is developed. Resampling with various interpolation functions shows that metric distortion is lower when using bilinear interpolation as compared to nearest neighbor or bucubic interpolation.

Some techniques were investigated for extracting terrain surface roughness data from digital aerial imagery. Measurement of tonal changes in the imagery is one feasible approach. A digital image data base was assembled containing 10 image planes, 4096 x 4096 pixels/plane, 8 bits/pixel. The planes contain Landsat Thematic Mapper image data, four digitized aerial photos which were rectified, registered to the Thematic Mapper image and mosaicked, and digitized aerial radar imagery, all for the same scene. From the digital photomosaic, open water and forested areas were extracted using a gray level-texture classification.

This study was performed to investigate man-machine interactive digital image processing techniques applied to the extraction of terrain analysis data from aerial imagery. The study focuses primarily on the extraction of vegetation data elements from digitized panchromatic photography, with a small amount of attention given to thermal infrared and side-looking radar imagery. Of thirteen vegetation data elements listed in the USAETL Terrain Analysis Procedural Guide for Vegetation, eight are addressed in varying degrees of depth in the study. Interactive digital techniques are developed for vegetation/land cover boundary extraction, and for the extraction of several forest related data elements such as percent canopy closure, number of stems per hectare, tree crown diameter, number of trees per hectare in each stem diameter class, and others. The extraction techniques are developed using an existing general-purpose, interactive digital image analysis system--the General Electric DIAL (Digital Image Analysis Laboratory). Results are compared in two cases to those achieved via existing manual analysis procedures. While the interactive digital extraction techniques exhibit a promising potential, some technical problems remain. Further development, testing and evaluation are warranted using greater variety of test imagery, including radar and thermal imagery.

This study was performed to investigate man-machine interactive digital image processing techniques applied to the extraction of terrain analysis data from aerial imagery. The study focuses primarily on the extraction of vegetation data elements from digitized panchromatic photography, with a small amount of attention given to thermal infrared and side-looking radar imagery. Of thirteen vegetation data elements listed in the USAETL Terrain Analysis Procedural Guide for Vegetation, eight are addressed in varying degrees of depth in the study. Interactive digital techniques are developed for vegetation/land cover boundary extraction, and for the extraction of several forest related data elements such as percent canopy closure, number of stems per hectare, tree crown diameter, number of trees per hectare in each stem diameter class, and others. The extraction techniques are developed using an existing general-purpose, interactive digital image analysis system--the General Electric DIAL (Digital Image Analysis Laboratory). Results are compared in two cases to those achieved via existing manual analysis procedures. While the interactive digital extraction techniques exhibit a promising potential, some technical problems remain. Further development, testing and evaluation are warranted using greater variety of test imagery, including radar and thermal imagery.

Capillary optics, which use total external reflection to transmit x rays, have been proved to have great advantages in scatter reduction and resolution improvement in mammography application. The purpose of the proposed work is to perform lens simulation and image artifact elimination as part of the work of capillary optical mammography research. A lens simulation extended from the-previous single fiber simulation has been implemented, a lens for the protocol mammography system has been re-measured in an extended energy range and analyzed with the program. The simulation has been proved to be helpful in understanding the lens performance. Modulation Transfer Function (MTF) is the most fundamental measurement of spatial resolution used in radiology. Non-uniform transmission introduces difficulties in MTF measurement. A method to measure MTF by taking an edge image and processed with background reduction technique has been developed. The modeling capability has been extended and has lead to a real development of understanding of the nature of polycapillary defects, which is already leading to improvements in the manufacturing processes. Further, the increased confidence in the modeling allows future lens geometries to be effectively tested in simulation so that design parameters can be rapidly optimized.

Capillary optics, which use total external reflection to transmit x rays, have been proved to have great advantages in scatter reduction and resolution improvement in mammography application. The purpose of the proposed work is to perform lens simulation and image artifact elimination as part of the work of capillary optical mammography research. A lens simulation extended from the-previous single fiber simulation has been implemented, a lens for the protocol mammography system has been re-measured in an extended energy range and analyzed with the program. The simulation has been proved to be helpful in understanding the lens performance. Modulation Transfer Function (MTF) is the most fundamental measurement of spatial resolution used in radiology. Non-uniform transmission introduces difficulties in MTF measurement. A method to measure MTF by taking an edge image and processed with background reduction technique has been developed. The modeling capability has been extended and has lead to a real development of understanding of the nature of polycapillary defects, which is already leading to improvements in the manufacturing processes. Further, the increased confidence in the modeling allows future lens geometries to be effectively tested in simulation so that design parameters can be rapidly optimized.

Capillary optics, which use total external reflection to transmit x rays, have been proved to have great advantages in scatter reduction and resolution improvement in mammography application. The purpose of the proposed work is to perform lens simulation and image artifact elimination as part of the work of capillary optical mammography research. A lens simulation extended from the-previous single fiber simulation has been implemented, a lens for the protocol mammography system has been re-measured in an extended energy range and analyzed with the program. The simulation has been proved to be helpful in understanding the lens performance. Modulation Transfer Function (MTF) is the most fundamental measurement of spatial resolution used in radiology. Non-uniform transmission introduces difficulties in MTF measurement. A method to measure MTF by taking an edge image and processed with background reduction technique has been developed. The modeling capability has been extended and has lead to a real development of understanding of the nature of polycapillary defects, which is already leading to improvements in the manufacturing processes. Further, the increased confidence in the modeling allows future lens geometries to be effectively tested in simulation so that design parameters can be rapidly optimized.

Capillary optics, which use total external reflection to transmit x rays, have been proved to have great advantages in scatter reduction and resolution improvement in mammography application. The purpose of the proposed work is to perform lens simulation and image artifact elimination as part of the work of capillary optical mammography research. A lens simulation extended from the-previous single fiber simulation has been implemented, a lens for the protocol mammography system has been re-measured in an extended energy range and analyzed with the program. The simulation has been proved to be helpful in understanding the lens performance. Modulation Transfer Function (MTF) is the most fundamental measurement of spatial resolution used in radiology. Non-uniform transmission introduces difficulties in MTF measurement. A method to measure MTF by taking an edge image and processed with background reduction technique has been developed. The modeling capability has been extended and has lead to a real development of understanding of the nature of polycapillary defects, which is already leading to improvements in the manufacturing processes. Further, the increased confidence in the modeling allows future lens geometries to be effectively tested in simulation so that design parameters can be rapidly optimized.

Capillary optics, which use total external reflection to transmit x rays, have been proved to have great advantages in scatter reduction and resolution improvement in mammography application. The purpose of the proposed work is to perform lens simulation and image artifact elimination as part of the work of capillary optical mammography research. A lens simulation extended from the-previous single fiber simulation has been implemented, a lens for the protocol mammography system has been re-measured in an extended energy range and analyzed with the program. The simulation has been proved to be helpful in understanding the lens performance. Modulation Transfer Function (MTF) is the most fundamental measurement of spatial resolution used in radiology. Non-uniform transmission introduces difficulties in MTF measurement. A method to measure MTF by taking an edge image and processed with background reduction technique has been developed. The modeling capability has been extended and has lead to a real development of understanding of the nature of polycapillary defects, which is already leading to improvements in the manufacturing processes. Further, the increased confidence in the modeling allows future lens geometries to be effectively tested in simulation so that design parameters can be rapidly optimized.

Capillary optics, which use total external reflection to transmit x rays, have been proved to have great advantages in scatter reduction and resolution improvement in mammography application. The purpose of the proposed work is to perform lens simulation and image artifact elimination as part of the work of capillary optical mammography research. A lens simulation extended from the-previous single fiber simulation has been implemented, a lens for the protocol mammography system has been re-measured in an extended energy range and analyzed with the program. The simulation has been proved to be helpful in understanding the lens performance. Modulation Transfer Function (MTF) is the most fundamental measurement of spatial resolution used in radiology. Non-uniform transmission introduces difficulties in MTF measurement. A method to measure MTF by taking an edge image and processed with background reduction technique has been developed. The modeling capability has been extended and has lead to a real development of understanding of the nature of polycapillary defects, which is already leading to improvements in the manufacturing processes. Further, the increased confidence in the modeling allows future lens geometries to be effectively tested in simulation so that design parameters can be rapidly optimized.

Capillary optics, which use total external reflection to transmit x rays, have been proved to have great advantages in scatter reduction and resolution improvement in mammography application. The purpose of the proposed work is to perform lens simulation and image artifact elimination as part of the work of capillary optical mammography research. A lens simulation extended from the-previous single fiber simulation has been implemented, a lens for the protocol mammography system has been re-measured in an extended energy range and analyzed with the program. The simulation has been proved to be helpful in understanding the lens performance. Modulation Transfer Function (MTF) is the most fundamental measurement of spatial resolution used in radiology. Non-uniform transmission introduces difficulties in MTF measurement. A method to measure MTF by taking an edge image and processed with background reduction technique has been developed. The modeling capability has been extended and has lead to a real development of understanding of the nature of polycapillary defects, which is already leading to improvements in the manufacturing processes. Further, the increased confidence in the modeling allows future lens geometries to be effectively tested in simulation so that design parameters can be rapidly optimized.

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.

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.

TRI 1 2016/2017

TRI 1 2016/2017

TRI 1 2016/2017

Trimester 1 2018/2019

An attempt is made to draw a parallel between the existing geophysical data processing service industries and the emerging earth resources data support requirements. The relationship of seismic data analysis to ERTS data analysis is natural because in either case data is digitally recorded in the same format, resulting from remotely sensed energy which has been reflected, attenuated, shifted and degraded on its path from the source to the receiver. In the seismic case the energy is acoustic, ranging in frequencies from 10 to 75 cps, for which the lithosphere appears semi-transparent. In earth survey remote sensing through the atmosphere, visible and infrared frequency bands are being used. Yet the hardware and software required to process the magnetically recorded data from the two realms of inquiry are identical and similar, respectively. The resulting data products are similar.

An attempt is made to draw a parallel between the existing geophysical data processing service industries and the emerging earth resources data support requirements. The relationship of seismic data analysis to ERTS data analysis is natural because in either case data is digitally recorded in the same format, resulting from remotely sensed energy which has been reflected, attenuated, shifted and degraded on its path from the source to the receiver. In the seismic case the energy is acoustic, ranging in frequencies from 10 to 75 cps, for which the lithosphere appears semi-transparent. In earth survey remote sensing through the atmosphere, visible and infrared frequency bands are being used. Yet the hardware and software required to process the magnetically recorded data from the two realms of inquiry are identical and similar, respectively. The resulting data products are similar.

[1] Introduction: The development of brown spots on banana peel has a notable effect on the texture, color and taste of this fruit. So that the appearance of these spots reduces the quality of the fruit and affect its sale market. In recent years, in order to evaluate the quality and classification of agricultural products, the various systems based on computer vision technology have been widely considered. These systems as the computer image analysis methods have been successful in measuring the visual quality of different products (Riyadi et al., 2007; Roseleena et al., 2011; Rodriguez-pulido et al. 2012). According to research by Probha and Kumar (2015), the extracted color properties from the banana image were more effective than other features in identifying the different stages of the banana ripening. Also, Mendoza and Aguilera (2004) detected the different stages of banana ripening based on the color, texture parameters and the distribution of brown spots on banana peel using image processing technique with a precision of 98%. Nadafzadeh et al. (2018) designed a non-linear mathematical model using the Genetic Programming (GP) to predicting and evaluating the activity of polyphenol oxidase enzymes (PPO) and peroxides (POD) during the browning process of the banana peel; using the extracted parameters from image as inputs of proposed model, the correlation coefficients to predicting of PPO and POD enzymes were obtained 0.98 and 0.97, respectively. The aim of this study was to investigate the changes of color, dimensions and chemical parameters of several banana fruit groups (different in terms of appearance) as well as their marketability (the total acceptance of fruit) by Gaussian regression model (GPR) during the storage period. Therefore, using the proposed method in this research, the required product can be available according to the consumer demand.   Materials and Methods: In this study, one hundred banana samples were prepared from a market on the first day of the experiments. Samples were different in terms of shape and size, and were classified into 5 different groups. Group A had small size and curvature; B group compared to Group A had more curvature; the curvature of the samples in the group C was high, and in terms of size were medium. While the size of the bananas in group D was large, they had a small curvature. Also, the features of the group E were similar to the group D, but the curvature was greater in this group (group E). All of the samples were kept at the ambient temperature (25° C) away from the direct light for 7 days. During the days of experiments (days 0, 2, 4 and 6), five samples were examined from each group: after taking images of samples under the constant light conditions, and performing of manual measurements, they were subjected to destructive tests (laboratory tests) and sensory tests. After the images acquisition of samples, the preprocessing operations such as image enhancement, noise removal by the area opening, and the implementation of the image segmentation process using the method of Otsu adaptive thresholding were conducted (Gonzalez et al., 2004). Finally, 11 color parameters (R, G, B, L, a, b, h, s, v, C, H) and 4 dimensional characteristics (diameter, curvature radius, long and small length) were extracted from each image. In the laboratory method, the TSS value was measured by a digital refractometer, and amount of pH and acidity were also measured by a fruit juice analysis titrator. Eventually, in order to investigate the changes of measured parameters, statistical analysis was performed in a randomized complete block design by SAS 9.3 software at a significance level of 5% using Duncan's multiple comparison test.   Results and discussion: Gradually along with the appearance of dark spots on the banana peel, many of the qualitative parameters such as the color, dimensions and chemical features were changed during the storage period. According to results of the Duncan's multiple range test, the values of color coordinates R, G, B, L, b, h, v, C, and H gradually reduced, and the values of these parameters were significant in all the experiments days (p<0.05). The parameter S also had a decreasing trend during the storage period, and the changes of this parameter was significant in the first days of the experiments compared to the ending days; during this period, the color parameter a increased significantly. Due to the changes of the banana fruit texture, the amount of the curvature radius, the small and large lengths, total soluble solids, pH and total titration acidity gradually decreased. Based on the results of the statistical analysis, there were no significant differences between dimensional parameters measured by non-destructive method and manual measurement (p>0.05). It is worth noting that in this study, the spent time to conduct the manual measurements of the dimensional parameters of a banana sample was 510 seconds, while all of these measurements were performed using a digital image processing method at 1.015 seconds. Therefore, it can be said that when the number of samples is high, using of the proposed method is also very cost-effective in terms of time, and it has high accuracy during the measurement. In the sensory evaluation, the results show that the best and most acceptable group of bananas were groups C, D and E, which had long size and low curvature; these groups of bananas had delicious texture, desirable flavor and low levels of brown spots on their peel. In the following, the non-destructive parameters were used to the development of Gaussian regression model (GPR), and finally, it was shown that the quality of banana fruit as well as its marketability (the total acceptance of fruit) are predictable during the storage period by GPR with a correlation coefficient of 0.91, MAPE (20.47), RMSE (0.43), SRE (0.71) and RAV (0.20 ). The appearance quality of the banana fruit is very effective in its acceptability for customer. In this research, the image processing technique as a non-destructive method was used to extract a set of color (R, G, B, L, a, b, h, s, v, C and H) and morphological properties (diameter, curvature radius, long length and small length) from banana image in order to evaluate its quality during storage. According to the results of Duncan's statistical analysis at the probability level of 5% and Pearson correlation results, the most suitable parameters were chosen to apply in Gaussian regression model. The results showed that the image processing technique is capable to evaluating the changes of color and dimensional parameters of banana fruit, and also the proposed model have a satisfactory performance (R2=0.91) in predicting the overall acceptance parameter of the banana.  

This paper discusses a technique for producing suspended material distribution maps by spectrum matching and two different techniques for detecting time-dependent changes recorded by the Landsat MSS-computer-generated false color composites for visual interpretation and detection by pixel-for-pixel comparison. Included in the paper are some of the results of a course of study and development at the U. S. Army Engineer Waterways Experiment Station (WES) that have produced a family of computer programs and algorithms designed to extract meaningful geometric and radiometric information from digitized image data, interpret the data, and depict the results as tabular lists printed with conventional line printers or as images on photographic film that can be used to make photographic images, maps, or charts. Specifically described are the equipment used for image data processing and applications of image data processing techniques for (a) suspened material distribution mapping, (b) production of color composites from Landsat computer-compatible tapes, (c) change detection, and (d) generation of color separations for offset printing.

The recent proliferation of color displays has accentuated the need for a metric of color contrast which can be used to predict human operator performance. To obtain the data for forming a metric of color contrast, one needs the capability of presenting a broad range of colors in varying geometric arrangements. This report describes an approach to developing the capability to display colors of known chromaticity using a color digital image processing system. This report covers the development of measurement technique and software used in characterizing the display system, the reduction of spectroradiometric data, the use of color mixing equations for color digital image processing systems, and the development of display system software. (Author)

Trimester 3 2015/2016

Digital image processing is the manipulation of the digital representation of an image. Source of the image can range from a common television video signal to complex imaging systems, such as a scanning electron microscope. Whatever the source of the image, the basic techniques for storing and manipulating data are common to many image processing applications. The actual hardware used in image processing is called a video frame grabber or frame buffer. Video frame grabbers costing under $1000 are now available for many microcomputers including the IBM-PC, APPLE, and S-100 bus systems. Thus, image processing is available to users where previously costs were prohibitive, This document describes the image storage, filtering techniques, and one possible application in the biological sciences. Keywords: Pixels; Convolution techniques; Fourier filtering; and Resolution. (Author)

Book Source: Digital Library of India Item 2015.193640 dc.contributor.author: N. K. Tripathi dc.date.accessioned: 2015-07-08T03:06:52Z dc.date.available: 2015-07-08T03:06:52Z dc.date.digitalpublicationdate: 0000-00-00 dc.identifier.barcode: 1990010091950 dc.identifier.origpath: /rawdataupload/upload/0091/950 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/193640 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 202 dc.format.mimetype: application/pdf dc.language.iso: English dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Engineering dc.subject.classification: Engineering. Technology In General dc.subject.classification: Civill Engineering dc.title: Digital Image Processing Of Satellite Data