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Trimester 1 2019/2020

Digital Image Processing dumped with WikiTeam tools.

Digital Image Processing dumped with WikiTeam tools.

Digital Image Processing dumped with WikiTeam tools.

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

Tri 1, 2022/2023

Tri 1, 2022/2023

Tri 1, 2022/2023

Tri 1, 2022/2023

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.

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

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.

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.

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.

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.

Image processing involves the use of techniques developed for manipulating images with the objective of enhancing some aspect of interest in the image. The type of image depends on the sensor used to capture the image, ie. camera, spectral, radar, sonar, thermal. This paper considers the role of image processing in applications in telecommunications involving the acquisition and transmission of images from large distances. These images are processed using algorithms developed on computers to enable the interpretation of the data. Such applications include images of distant planets transmitted by space probes, remote sensing applications of which there are several specialized areas such as monitoring and exploration of the earth’s natural resources (eg. vegetation, oceanography, mineral exploration, atmospheric ozone layer etc.), weather forecasting, map making, military intelligence. Image processing vastly adds to the effective use of these applications. Cellular phones now have the capability to capture and transmit images. Though it would require substantially more processing power than is present in cellular phones now, it is suggested given the developments in information technology, it should become possible to build lip reading capability and speech recognition capability in cellular phones. The cellular phone has all the hardware requirements for acquisition of both the audio and visual data. Designing lip reading capability in cell phones could make it possible for the mute persons to communicate with others via cell phone. Combined with speech recognition, this would enable voice communication to be made over low bandwidth channels. However, the present state of audio and visual speech recognition technology will need to attain significantly better performance than what is achievable today. This paper reviews the applications of image processing, its implementation, and image processing algorithms.

Image processing involves the use of techniques developed for manipulating images with the objective of enhancing some aspect of interest in the image. The type of image depends on the sensor used to capture the image, ie. camera, spectral, radar, sonar, thermal. This paper considers the role of image processing in applications in telecommunications involving the acquisition and transmission of images from large distances. These images are processed using algorithms developed on computers to enable the interpretation of the data. Such applications include images of distant planets transmitted by space probes, remote sensing applications of which there are several specialized areas such as monitoring and exploration of the earth’s natural resources (eg. vegetation, oceanography, mineral exploration, atmospheric ozone layer etc.), weather forecasting, map making, military intelligence. Image processing vastly adds to the effective use of these applications. Cellular phones now have the capability to capture and transmit images. Though it would require substantially more processing power than is present in cellular phones now, it is suggested given the developments in information technology, it should become possible to build lip reading capability and speech recognition capability in cellular phones. The cellular phone has all the hardware requirements for acquisition of both the audio and visual data. Designing lip reading capability in cell phones could make it possible for the mute persons to communicate with others via cell phone. Combined with speech recognition, this would enable voice communication to be made over low bandwidth channels. However, the present state of audio and visual speech recognition technology will need to attain significantly better performance than what is achievable today. This paper reviews the applications of image processing, its implementation, and image processing algorithms.

This article is from Korean Journal of Radiology , volume 15 . Abstract Objective: To compare new full-field digital mammography (FFDM) with and without use of an advanced post-processing algorithm to improve image quality, lesion detection, diagnostic performance, and priority rank. Materials and Methods: During a 22-month period, we prospectively enrolled 100 cases of specimen FFDM mammography (Brestige®), which was performed alone or in combination with a post-processing algorithm developed by the manufacturer: group A (SMA), specimen mammography without application of "Mammogram enhancement ver. 2.0"; group B (SMB), specimen mammography with application of "Mammogram enhancement ver. 2.0". Two sets of specimen mammographies were randomly reviewed by five experienced radiologists. Image quality, lesion detection, diagnostic performance, and priority rank with regard to image preference were evaluated. Results: Three aspects of image quality (overall quality, contrast, and noise) of the SMB were significantly superior to those of SMA (p < 0.05). SMB was significantly superior to SMA for visualizing calcifications (p < 0.05). Diagnostic performance, as evaluated by cancer score, was similar between SMA and SMB. SMB was preferred to SMA by four of the five reviewers. Conclusion: The post-processing algorithm may improve image quality with better image preference in FFDM than without use of the software.

This article is from Korean Journal of Radiology , volume 15 . Abstract Objective: To compare new full-field digital mammography (FFDM) with and without use of an advanced post-processing algorithm to improve image quality, lesion detection, diagnostic performance, and priority rank. Materials and Methods: During a 22-month period, we prospectively enrolled 100 cases of specimen FFDM mammography (Brestige®), which was performed alone or in combination with a post-processing algorithm developed by the manufacturer: group A (SMA), specimen mammography without application of "Mammogram enhancement ver. 2.0"; group B (SMB), specimen mammography with application of "Mammogram enhancement ver. 2.0". Two sets of specimen mammographies were randomly reviewed by five experienced radiologists. Image quality, lesion detection, diagnostic performance, and priority rank with regard to image preference were evaluated. Results: Three aspects of image quality (overall quality, contrast, and noise) of the SMB were significantly superior to those of SMA (p < 0.05). SMB was significantly superior to SMA for visualizing calcifications (p < 0.05). Diagnostic performance, as evaluated by cancer score, was similar between SMA and SMB. SMB was preferred to SMA by four of the five reviewers. Conclusion: The post-processing algorithm may improve image quality with better image preference in FFDM than without use of the software.

This article is from Korean Journal of Radiology , volume 15 . Abstract Objective: To compare new full-field digital mammography (FFDM) with and without use of an advanced post-processing algorithm to improve image quality, lesion detection, diagnostic performance, and priority rank. Materials and Methods: During a 22-month period, we prospectively enrolled 100 cases of specimen FFDM mammography (Brestige®), which was performed alone or in combination with a post-processing algorithm developed by the manufacturer: group A (SMA), specimen mammography without application of "Mammogram enhancement ver. 2.0"; group B (SMB), specimen mammography with application of "Mammogram enhancement ver. 2.0". Two sets of specimen mammographies were randomly reviewed by five experienced radiologists. Image quality, lesion detection, diagnostic performance, and priority rank with regard to image preference were evaluated. Results: Three aspects of image quality (overall quality, contrast, and noise) of the SMB were significantly superior to those of SMA (p < 0.05). SMB was significantly superior to SMA for visualizing calcifications (p < 0.05). Diagnostic performance, as evaluated by cancer score, was similar between SMA and SMB. SMB was preferred to SMA by four of the five reviewers. Conclusion: The post-processing algorithm may improve image quality with better image preference in FFDM than without use of the software.

Trimester 1 2020/2021

Trimester 1 2020/2021

Book Source: Digital Library of India Item 2015.197329 dc.contributor.author: Chada Dakshinamurty dc.date.accessioned: 2015-07-08T10:31:48Z dc.date.available: 2015-07-08T10:31:48Z dc.date.digitalpublicationdate: 2005-09-30 dc.identifier.barcode: 5990010097354 dc.identifier.origpath: /rawdataupload/upload/0097/354 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/197329 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 228 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Department Of Civil Engineering dc.title: Digital Image Processing For Ground Water In Carbonate Terrain

Book Source: Digital Library of India Item 2015.197329 dc.contributor.author: Chada Dakshinamurty dc.date.accessioned: 2015-07-08T10:31:48Z dc.date.available: 2015-07-08T10:31:48Z dc.date.digitalpublicationdate: 2005-09-30 dc.identifier.barcode: 5990010097354 dc.identifier.origpath: /rawdataupload/upload/0097/354 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/197329 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 228 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Department Of Civil Engineering dc.title: Digital Image Processing For Ground Water In Carbonate Terrain

The author has identified the following significant results. Results are reported for: (1) subscene registration, (2) full scene rectification and registration, (3) resampling techniques, (4) and ground control point (GCP) extraction. Subscenes (354 pixels x 234 lines) were registered to approximately 1/4 pixel accuracy and evaluated by change detection imagery for three cases: (1) bulk data registration, (2) precision correction of a reference subscene using GCP data, and (3) independently precision processed subscenes. Full scene rectification and registration results were evaluated by using a correlation technique to measure registration errors of 0.3 pixel rms thoughout the full scene. Resampling evaluations of nearest neighbor and TRW cubic convolution processed data included change detection imagery and feature classification. Resampled data were also evaluated for an MSS scene containing specular solar reflections.

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

[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&lt;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&gt;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&lt;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&gt;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&lt;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&gt;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&lt;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&gt;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.  

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

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.

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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.

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.

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

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

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

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

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

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

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