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Digital Image Processing dumped with WikiTeam tools.

Digital Image Processing dumped with WikiTeam tools.

[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.  

[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.  

[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.  

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

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

introductory digital image processing, a remote sensing approach by jensen

introductory digital image processing, a remote sensing approach by jensen

There are no author-identified significant results in this report.

Several areas of applications of image processing to astronomy were identified and discussed. These areas include: (1) deconvolution for atmospheric seeing compensation; a comparison between maximum entropy and conventional Wiener algorithms; (2) polarization in galaxies from photographic plates; (3) time changes in M87 and methods of displaying these changes; (4) comparing emission line images in planetary nebulae; and (5) log intensity, hue saturation intensity, and principal component color enhancements of M82. Examples are presented of these techniques applied to a variety of objects.

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.

TRI 1 2016/2017

TRI 1 2016/2017

TRI 1 2016/2017

TRI 1 2016/2017

TRI 1 2016/2017

TRI 1 2016/2017

TRI 1 2016/2017

TRI 1 2016/2017

Digital Image Processing B. Jahne

Trimester 1 2020/2021

Trimester 1 2020/2021

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

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

Book Source: Digital Library of India Item 2015.193638 dc.contributor.author: Nilanchal Patel dc.date.accessioned: 2015-07-08T03:06:37Z dc.date.available: 2015-07-08T03:06:37Z dc.date.digitalpublicationdate: 0000-00-00 dc.identifier.barcode: 1990010091949 dc.identifier.origpath: /rawdataupload/upload/0091/949 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/193638 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 259 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 Remotely

Image processing is process of modifying nature of an image in order to improve its visual information about human interpretations & autonomously machine detection. Digital image processing is subset of electronics realm in which a picture is converted to an array of small integers known as pixels that reflect a physical attribute such as particular circumstance & data in digital memory before being h&led by a computer equipment. two primary application areas for digital image processing techniques that have generated attention are enhancing pictorial information for human interpretation & processing image data for storage, transmission, & representation for autonomous machine perception. Edges define boundaries, & edge recognition is one of most difficult tasks in image processing.. As a result, it is a basic issue in image processing. This article looks at many stages of digital picture processing. This study may be exp&ed in future by applying similar methods to a variety of digital pictures.

This AASERT Grant on the development of a Pulse-Burst Laser Source and Digital Image Processing for Measurements of High-Speed, Time evolving Flow was originally paired with Grant #F49620-92-J-021V entitled, Quantitative Imaging of Time-Evolving Structure in Supersonic and Hypersonic Flows. That grant ended in December 1994. The students have more recently been working in conjunction on the development of diagnostics in association with Shock Propagation and Supersonic Drag in Low Temperature Plasmas (#F49620-97-1-0497) which began June 1, 1997. Major accomplishments achieved under this AASERT Grant were the fabrication of a cavity-locked, injection-seeded Ti:Sapphire Laser and the demonstration of UV filtered Rayleigh scattering imaging in a supersonic jet, the fabrication/characterization of a narrow passband transmission filter, and the development of a new concept for a line imaging Raman spectrometer for flow field, combustion, and plasma diagnostics.

The phase 2 of an experimental study has been performed to investigate the feasibility and use of man-machine interactive digital image processing techniques when applied to the extraction of terrain analysis data from aerial imagery. This phase of the study concentrated on applying techniques to extract water resources data elements and on the development and coding of software and algorithms. The developed software and algorithms, pertained to filtering of themes, and image scaling. Two digital processing techniques to 'skeletonize' features, best mapped as lines, were established and tested with limited success. Relative to water resources data elements, success was achieved in the extraction of alignment of watercourses and shore line alignment of watercourses of water bodies. Partial success was achieved with the elements of delineation of wet areas and the identification of terminal points of watercourse segments. Image processing techniques for the extraction of areas subject to flooding and for the measurement of bank heights were not practical with the existing software. The development/implementation of software, and the processing and analysis of imagery was conducted at the General Electric Digital Image Analysis Laboratory. (Author)

The objective of the second phase of this investigation was to continue the development of the interactive multi-channel image classification capabilities of the DIAL system. This development proceeded in four directions. Formal demonstrations and a 'hands on' course in the DIAL algorithms implemented under the first phase of the investigation were given. Additional DIAL algorithms to support classification were developed, coded, and tested. These included a Program Module (PM) to apply the Karhunen - Loeve transformation to a multi-channel image, which has the effect of reducing the dimensionality of an image without significantly decreasing its information content. In addition two algorithms in refining class assignment by relaxation methods were developed. One was selected, then coded on DIAL and was applied to a classification of a LACIE intensive site, where it removed 'speckle', sharpened field boundaries, and increased the overall classification accuracy. A task to program the computationally intensive part of the maximum likelihood method on the STARAN was undertaken jointly with ETL. Finally an experiment in the maximum likelihood classification of a LANDSAT scene using the DIAL PMs was performed in cooperation with an ETL botanist. This experiment demonstrated the utility of the interactive classification algorithms in the study of the relationship between flora and geological structures. (Author)

The objective of the second phase of this investigation was to continue the development of the interactive multi-channel image classification capabilities of the DIAL system. This development proceeded in four directions. Formal demonstrations and a 'hands on' course in the DIAL algorithms implemented under the first phase of the investigation were given. Additional DIAL algorithms to support classification were developed, coded, and tested. These included a Program Module (PM) to apply the Karhunen - Loeve transformation to a multi-channel image, which has the effect of reducing the dimensionality of an image without significantly decreasing its information content. In addition two algorithms in refining class assignment by relaxation methods were developed. One was selected, then coded on DIAL and was applied to a classification of a LACIE intensive site, where it removed 'speckle', sharpened field boundaries, and increased the overall classification accuracy. A task to program the computationally intensive part of the maximum likelihood method on the STARAN was undertaken jointly with ETL. Finally an experiment in the maximum likelihood classification of a LANDSAT scene using the DIAL PMs was performed in cooperation with an ETL botanist. This experiment demonstrated the utility of the interactive classification algorithms in the study of the relationship between flora and geological structures. (Author)

The objective of the second phase of this investigation was to continue the development of the interactive multi-channel image classification capabilities of the DIAL system. This development proceeded in four directions. Formal demonstrations and a 'hands on' course in the DIAL algorithms implemented under the first phase of the investigation were given. Additional DIAL algorithms to support classification were developed, coded, and tested. These included a Program Module (PM) to apply the Karhunen - Loeve transformation to a multi-channel image, which has the effect of reducing the dimensionality of an image without significantly decreasing its information content. In addition two algorithms in refining class assignment by relaxation methods were developed. One was selected, then coded on DIAL and was applied to a classification of a LACIE intensive site, where it removed 'speckle', sharpened field boundaries, and increased the overall classification accuracy. A task to program the computationally intensive part of the maximum likelihood method on the STARAN was undertaken jointly with ETL. Finally an experiment in the maximum likelihood classification of a LANDSAT scene using the DIAL PMs was performed in cooperation with an ETL botanist. This experiment demonstrated the utility of the interactive classification algorithms in the study of the relationship between flora and geological structures. (Author)

Three design tools for hybrid optical/digital imaging systems have been developed that provide new ways of designing the non-conventional imaging systems that are optimum for extended depth of field/focus, and for passively determining the range to objects in a scene. The first tool developed was a defocus transfer function (DTF) for imaging systems with radially symmetric pupils. The DTF allows one to visualize how the modulation transfer function (MTF) will change with focus. Prior work was valid only for one-dimensional optical systems. The second tool uses a form of a Radon-Wigner distribution to show the impulse response, or point spread function, as the focus changes. The third tool is the use of linear transformation operators to analyze and simulate optical systems. These three tools are proving to be very' powerful in the design of hybrid optical/signal processing systems.

There are fair details in the book discusses about digital image and how to use Matlab processing digital images.

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.

The Image-Based Information System (IBIS) was used to automate the cross country movement (CCM) mapping model developed by the Defense Mapping Agency (DMA). Existing terrain factor overlays and a CCM map, produced by DMA for the Fort Lewis, Washington area, were digitized and reformatted into geometrically registered images. Terrain factor data from Slope, Soils, and Vegetation overlays were entered into IBIS, and were then combined utilizing IBIS-programmed equations to implement the DMA CCM model. The resulting IBIS-generated CCM map was then compared with the digitized manually produced map to test similarity. The numbers of pixels comprising each CCM region were compared between the two map images, and percent agreement between each two regional counts was computed. The mean percent agreement equalled 86.21%, with an areally weighted standard deviation of 11.11%. Calculation of Pearson's correlation coefficient yielded +9.997. In some cases, the IBIS-calculated map code differed from the DMA codes: analysis revealed that IBIS had calculated the codes correctly. These highly positive results demonstrate the power and accuracy of IBIS in automating models which synthesize a variety of thematic geographic data.

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.

The Phase 3 of an experimental study has been performed to test several man-machine interactive digital image processing techniques for the extraction of forest related information including the outline of forested areas, tree stem location, tree stem densities and canopy closure. The techniques were applied to several different images covering a considerable range of forest types. Results of the digital processing were compared to a manual photo-interpretation and to actual field measurements. (Author)

The Phase 3 of an experimental study has been performed to test several man-machine interactive digital image processing techniques for the extraction of forest related information including the outline of forested areas, tree stem location, tree stem densities and canopy closure. The techniques were applied to several different images covering a considerable range of forest types. Results of the digital processing were compared to a manual photo-interpretation and to actual field measurements. (Author)

The Phase 3 of an experimental study has been performed to test several man-machine interactive digital image processing techniques for the extraction of forest related information including the outline of forested areas, tree stem location, tree stem densities and canopy closure. The techniques were applied to several different images covering a considerable range of forest types. Results of the digital processing were compared to a manual photo-interpretation and to actual field measurements. (Author)

The Phase 3 of an experimental study has been performed to test several man-machine interactive digital image processing techniques for the extraction of forest related information including the outline of forested areas, tree stem location, tree stem densities and canopy closure. The techniques were applied to several different images covering a considerable range of forest types. Results of the digital processing were compared to a manual photo-interpretation and to actual field measurements. (Author)

Digital Image Processing Lectures ( Spring 2025) 

Trimester 1 2020/2021

Includes bibliographical references and indexes

Includes bibliographical references and indexes