Color Conversion Image Processing Projects Research Ideas
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This document discusses the history and evolution of server side analysis and visualization tools from 2004 to 2010. In 2004, GIS professionals used these tools for land use and environmental planning. Over time, the tools developed to allow for 3D modeling and analysis without requiring plugins. They also became more visual, analytical, configurable, and able to handle large and small scale projects. By 2010, the tools no longer had the limitations of earlier systems and were ready for testing and use.
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Human vision is an important factor in the areas of image processing. Research has been done for years to make automatic image processing but still human intervention can not be denied and thus better human intervention is necessary. Two most important points are required to improve human vision which are light and color. Gamma encoder is the one which helps to improve the properties of human vision and thus to maintain visual quality gamma encoding is necessary.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) color space is vastly used. Moreover, for computer graphics RGB color space is called the most established choice to acquire desired color. RGB color space has a great effort on simplifying the design and architecture of a system. However, RGB struggles to deal efficiently for the images those belong to the real-world.
Images are captured using cameras, videos and other devices using different magnifications. In most cases during processing, in compare to the original outlook the images appear either dark or bright in contrast. Human vision affects and thus poor quality image analysis may occur. Consequently this poor manual image analysis may have huge difference from the computational image analysis outcome. Question may arise here why we will use gamma encoding when histogram equalization or histogram normalization can enhance images. Enhancing images does not improve human visualization quality all the time because sometimes it brightens the image quality when it is needed to darken and vice-versa. Human vision reflects under universal illumination environment (not pitch black or blindingly bright) thus follows an approximate gamma or power function. Hence, this is not a good idea to brighten images all the time when better human visualization can be obtained while darkening the images. Better human visualization is important for manual image processing which leads to compare the outcome with the semiautomated or automated one. Considering the importance of gamma encoding in image processing we propose an efficient color model which will help to improve visual quality for manual processing as well as will lead analyzers to analyze images automatically for comparison and testing purpose.
Image enhancement with the application of local and global enhancement method...
This project enhances dark images using HSV color model, histogram equalization, and contrast adjustment techniques. HSV separates an image into hue, saturation, and value channels, allowing histogram equalization to increase contrast without affecting color balance. Histogram equalization spreads out pixel intensity values for better contrast. The project interface allows users to upload dark images and apply enhancement methods with a button click to generate improved output images.
Image enhancement with the application of local and global enhancement method...
This project enhances dark images using HSV color model, histogram equalization, and contrast adjustment techniques. HSV separates an image into hue, saturation, and value channels, allowing histogram equalization to increase contrast without affecting color balance. Histogram equalization spreads out pixel intensity values for better contrast. The project interface allows users to upload dark images and apply enhancement methods with a button click to generate improved output images.
Glossary dip
This document contains a glossary of terms used in digital image processing compiled by Upendra. It includes definitions for over 80 terms ranging from basic concepts like pixels, color models, and image formats to more advanced image processing techniques like morphological operations, filtering, and geometric transformations. Each term includes a brief 1-2 sentence definition. The glossary is intended to provide a comprehensive overview of commonly used vocabulary in the field of digital image processing.
Psuedo color
The document discusses pseudo color images and techniques for converting grayscale images to color. It defines pseudo color images as grayscale images mapped to color according to a lookup table or function. It describes various color schemes for this mapping, including grayscale schemes that use shades of gray and oscillating schemes that emphasize certain grayscale ranges in color. The document also discusses using piecewise linear functions and smooth non-linear functions to transform grayscale levels to color for purposes such as enhancing contrast or reducing noise in images.
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Human vision is an important factor in the areas of image processing. Research has been done for years to make automatic image processing but still human intervention can not be denied and thus better human intervention is necessary. Two most important points are required to improve human vision which are light and color. Gamma encoder is the one which helps to improve the properties of human vision and thus to maintain visual quality gamma encoding is necessary.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) color space is vastly used. Moreover, for computer graphics RGB color space is called the most established choice to acquire desired color. RGB color space has a great effort on simplifying the design and architecture of a system. However, RGB struggles to deal efficiently for the images those belong to the real-world.
Images are captured using cameras, videos and other devices using different magnifications. In most cases during processing, in compare to the original outlook the images appear either dark or bright in contrast. Human vision affects and thus poor quality image analysis may occur. Consequently this poor manual image analysis may have huge difference from the computational image analysis outcome. Question may arise here why we will use gamma encoding when histogram equalization or histogram normalization can enhance images. Enhancing images does not improve human visualization quality all the time because sometimes it brightens the image quality when it is needed to darken and vice-versa. Human vision reflects under universal illumination environment (not pitch black or blindingly bright) thus follows an approximate gamma or power function. Hence, this is not a good idea to brighten images all the time when better human visualization can be obtained while darkening the images. Better human visualization is important for manual image processing which leads to compare the outcome with the semiautomated or automated one. Considering the importance of gamma encoding in image processing we propose an efficient color model which will help to improve visual quality for manual processing as well as will lead analyzers to analyze images automatically for comparison and testing purpose.
Image enhancement with the application of local and global enhancement method...
This project enhances dark images using HSV color model, histogram equalization, and contrast adjustment techniques. HSV separates an image into hue, saturation, and value channels, allowing histogram equalization to increase contrast without affecting color balance. Histogram equalization spreads out pixel intensity values for better contrast. The project interface allows users to upload dark images and apply enhancement methods with a button click to generate improved output images.
Image enhancement with the application of local and global enhancement method...
This project enhances dark images using HSV color model, histogram equalization, and contrast adjustment techniques. HSV separates an image into hue, saturation, and value channels, allowing histogram equalization to increase contrast without affecting color balance. Histogram equalization spreads out pixel intensity values for better contrast. The project interface allows users to upload dark images and apply enhancement methods with a button click to generate improved output images.
Glossary dip
This document contains a glossary of terms used in digital image processing compiled by Upendra. It includes definitions for over 80 terms ranging from basic concepts like pixels, color models, and image formats to more advanced image processing techniques like morphological operations, filtering, and geometric transformations. Each term includes a brief 1-2 sentence definition. The glossary is intended to provide a comprehensive overview of commonly used vocabulary in the field of digital image processing.
Psuedo color
The document discusses pseudo color images and techniques for converting grayscale images to color. It defines pseudo color images as grayscale images mapped to color according to a lookup table or function. It describes various color schemes for this mapping, including grayscale schemes that use shades of gray and oscillating schemes that emphasize certain grayscale ranges in color. The document also discusses using piecewise linear functions and smooth non-linear functions to transform grayscale levels to color for purposes such as enhancing contrast or reducing noise in images.
A PROJECT REPORT ON REMOVAL OF UNNECESSARY OBJECTS FROM PHOTOS USING MASKING
This document presents a project report on removing unnecessary objects from photos using masking techniques. It discusses using algorithms like Fast Marching and Navier-Stokes to fill in missing image data and maintain continuity across boundaries. The Fast Marching method begins at region boundaries and works inward, prioritizing completion of boundary pixels first. Navier-Stokes uses fluid dynamics equations to continue intensity value functions and ensure they remain continuous at boundaries. Color filtering can also be used to segment specific colored objects or regions. The project aims to implement these techniques to remove unwanted objects from images and fill the resulting gaps seamlessly.
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Abstract: In this paper we present a process called color
transfer which can borrow one image’s color
characteristics from another. Most current colorization
algorithms either require a significant user effort or have
large computational time. Here focus on orthogonal color
space i.e. lαβ color space without correlation between the
axes is given. Here we have implemented two global color
transfer algorithms in lαβ color space using simple color
statistical information such as mean, standard deviation
and covariance between the pixels of image. Our approach
is the extension of Reinhard's. Our local color transfer
algorithm uses simple color statistical analysis to recolor
the target image according to selected color range in
source image. Target image’s color influence mask is
prepared. It is a mask that specifies what parts of target
image will be affected according to selected color range.
After that target image is recolored in lαβ color space
according to prepared color influence map. In the lαβ
color space luminance and chrominance information is
separate so it allows making image recoloring optional.
The basic color transformation uses stored color statistics
of source and target image. All the algorithms are
implemented in JAVA object oriented language. The main
advantage of proposed method over the existing one is it
allows the user to recolor a part of the image in a simple &
intuitive way, preserving other color intact & achieving
natural look.
Index Terms: color transfer, local color statistics, color
characteristics, orthogonal color space, color influence
map.
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This paper presents a new color space normalization (CSN) technique for enhancing the discriminating power of color space along with the principal component analysis (PCA) for the face recognition process. The common RGB technique is not suitable for the characterizing of the skin color due to the presence of luminance factor. In the YCbCr color space, the luminance information is contained in Y component, and the chrominance information is in Cb and Cr. Therefore, the luminance information can be easily de-embedded. Different color spaces have different discriminating power, in this paper, eye can be perfectly detected by using YcbCr color space and the mouth regions can be perfectly detected by using the YIQ color space. Then PCA is used to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. PCA allows us to compute a linear transformation that maps data from a high dimensional space to a lower dimensional space. It covers standard deviation, covariance, eigenvectors and eigenvalues. Face recognition is obtained by PCA without much loss of information. Experiments using different databases by varying the facial expressions (open/closed eyes, smiling/not smiling) show that the proposed method by combining color space discrimination and PCA can improve face recognition to a great extend.
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This document proposes improvements to image edge detection methods. It summarizes previous approaches that have limitations like poor localization of edges, inability to remove noise, and high computational time. The proposed hybrid approach uses principal component analysis and Canny edge detection in parallel across multiple processors. This achieves faster and more efficient edge detection than prior methods. However, the document suggests edge detection quality could be further improved by using an improved wavelet transformation instead of PCA. It recommends a proposal based on wavelet transformations and Canny detection with operator fusion to first apply wavelet noise cancellation and smoothing before edge detection.
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IRJET- Design of Image Resolution Enhancement by using DWT and SWT
1) The document proposes a technique for image resolution enhancement using discrete wavelet transform (DWT) and stationary wavelet transform (SWT).
2) It decomposes an input image using DWT into subbands, then applies bicubic interpolation to the high frequency subbands and SWT to minimize information loss.
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Technical concepts for graphic design production includes:
1- History Of Graphic Design.
2- Graphics Types.
3- Bitmaps.
4- Color Gamut.
5- Files Formats.
6- Resolutions.
7- Color Depth.
8- Document Structure.
9- Digital Printing.
10 - pdf.
11- Color Management System CMS.
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This document presents a comparative study of various image contrast enhancement techniques. It discusses techniques like histogram equalization, gamma correction, brightness preserving bi-histogram equalization (BBHE), brightness preserving dynamic histogram equalization (BPDHE), and region based adaptive contrast enhancement (RACE). The study evaluates the performance of these techniques on different color images using objective parameters like entropy, absolute contrast error, and peak signal to noise ratio. The results show that the BPDHE technique generally produces enhanced images with less color error, higher contrast-to-noise ratio, and entropy values indicating more details compared to the other techniques. BPDHE is therefore found to be the best technique for enhancing image contrast while preserving color and brightness.
Image Processing(Beta1)
The document discusses image processing and provides details about:
1) The stages of image processing - input, editing, and output. The input stage deals with converting analog images to digital form. The editing stage manipulates the image. The output stage saves the transformed image.
2) Image processing operations like geometric transformations, color corrections, digital compositing, and extending dynamic range.
3) Applications of image processing like face detection, medical imaging, and remote sensing.
Sign Language Recognition Using Image Processing For Mute People
Abstract: Computer recognition of sign language is an important research problem for enabling communication with mute people. This project introduces an efficient and fast algorithm for identification of the number of fingers opened in a gesture representing an alphabet of the Binary Sign Language.
The system does not require the hand to be perfectly aligned to the camera. The project uses image processing system to identify, especially English alphabetic sign language used by the mute people to communicate. The basic objective of this project is to develop a computer based intelligent system that will enable mute people significantly to communicate with all other people using their natural hand gestures.
The idea consisted of designing and building up an intelligent system using image processing, machine learning and artificial intelligence concepts to take visual inputs of sign language’s hand gestures and generate easily recognizable form of outputs.
Hence the objective of this project is to develop an intelligent system which can act as a translator between the sign language and the spoken language dynamically and can make the communication between people with mute and normal people both effective and efficient. The system is we are implementing for Binary sign language but it can detect any sign language with prior image processing.
color image processing
This document discusses color image processing and various color models. It begins with an overview of color fundamentals, including the visible light spectrum and primary/secondary colors. It then describes several color models - RGB, CMY, and HSI. Conversion between these color spaces is also covered. The document also discusses pseudocolor image processing techniques like intensity slicing and gray level to color transformations. Finally, it covers full-color image processing, including treating each color component separately, color complements, and color image smoothing and segmentation in RGB space.
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This document describes a new lossless color image compression algorithm based on hierarchical prediction and context-adaptive arithmetic coding. It decorrelates RGB images using a reversible color transform, then encodes the Y component conventionally and the chrominance components hierarchically using upper, left, and lower pixels for prediction rather than just upper and left. An appropriate context model is defined for prediction errors, which are arithmetic coded. Testing showed this method achieves better bit rate compression than JPEG2000 and JPEG-XR.WiFi Network Simulator Projects Research Guidance
This document discusses WiFi network simulator projects and tools. It lists several popular network simulators like NS-3, OPNET, Omnet++ and Qualnet that can be used for WiFi network simulation projects in MATLAB. It then provides examples of recent research topics conducted using WiFi network simulators, including energy efficient load balancing between LTE and WiFi networks and jamming-resistant frequency hopping in cognitive WiFi networks. Finally, it outlines some channel estimation models used in WiFi network simulator projects, such as energy optimization with delay sensitive traffic and transmit power adaptation in WiFi mesh networks for rescue operations.
Types of Network Simulators Research Help
This document discusses different types of network simulators that can be used in MATLAB. It lists several open-source simulators like NS-2, NS-3, Omnet++ and proprietary simulators like Qualnet and Opnet. It also mentions some current research projects using network simulation in areas like supercomputer networks, isolated power systems, rumor routing protocols and wireless applications. Finally, it provides examples of modern research topics involving areas like smart grids, mobile network simulation, caching systems and taxi dispatching.
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wepik-enhancing-visual-data-exploring-arithmetic-and-logic-operations-in-imag...
The document discusses arithmetic and logic operations in image processing. It explains that image processing involves manipulating images using mathematical operations to enhance quality and extract information. It then covers various techniques like filtering, segmentation, feature extraction, brightness adjustment, contrast enhancement, image masking, edge detection, color correction, noise reduction, and morphological operations. These techniques have revolutionized medical imaging and satellite imaging by enabling applications like tumor detection, environmental monitoring, and disaster management. Future trends include integrating machine learning and deep learning with traditional image processing.
User Interactive Color Transformation between Images
Abstract: In this paper we present a process called color
transfer which can borrow one image’s color
characteristics from another. Most current colorization
algorithms either require a significant user effort or have
large computational time. Here focus on orthogonal color
space i.e. lαβ color space without correlation between the
axes is given. Here we have implemented two global color
transfer algorithms in lαβ color space using simple color
statistical information such as mean, standard deviation
and covariance between the pixels of image. Our approach
is the extension of Reinhard's. Our local color transfer
algorithm uses simple color statistical analysis to recolor
the target image according to selected color range in
source image. Target image’s color influence mask is
prepared. It is a mask that specifies what parts of target
image will be affected according to selected color range.
After that target image is recolored in lαβ color space
according to prepared color influence map. In the lαβ
color space luminance and chrominance information is
separate so it allows making image recoloring optional.
The basic color transformation uses stored color statistics
of source and target image. All the algorithms are
implemented in JAVA object oriented language. The main
advantage of proposed method over the existing one is it
allows the user to recolor a part of the image in a simple &
intuitive way, preserving other color intact & achieving
natural look.
Index Terms: color transfer, local color statistics, color
characteristics, orthogonal color space, color influence
map.
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This document proposes an intelligent traffic information and control system that uses image processing and wireless communication to control traffic lights. A camera at intersections will capture images and detect vehicle presence to adjust light durations accordingly. An emergency vehicle clearance system will turn all lights green on its path. Zigbee modules allow wireless communication between an ambulance and traffic controller. Additionally, a traffic management system and chatbot provide traffic information to users. The system will use incremental development, initially controlling lights with Arduino then adding congestion control with image processing.
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How photo enhance looks today. What standart pipeline does it have. What interesting open source models and libraries you can use.
Presentation shortstory
This document discusses an approach to single image denoising that takes into account aspects of the camera imaging pipeline. It first "unprocesses" an image to reverse common image processing steps and estimate the original raw image captured by the sensor. A neural network is then trained to denoise these synthetic raw images. Key steps in the image formation process that are modeled include demosaicing, digital gain, white balance, color correction, gamma compression, and tone mapping. The network architecture is a U-Net, and it achieves state-of-the-art results with a 14-25% reduction in error compared to other methods on both raw and sRGB image metrics.
Advanced Hybrid Color Space Normalization for Human Face Extraction and Detec...
This paper presents a new color space normalization (CSN) technique for enhancing the discriminating power of color space along with the principal component analysis (PCA) for the face recognition process. The common RGB technique is not suitable for the characterizing of the skin color due to the presence of luminance factor. In the YCbCr color space, the luminance information is contained in Y component, and the chrominance information is in Cb and Cr. Therefore, the luminance information can be easily de-embedded. Different color spaces have different discriminating power, in this paper, eye can be perfectly detected by using YcbCr color space and the mouth regions can be perfectly detected by using the YIQ color space. Then PCA is used to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. PCA allows us to compute a linear transformation that maps data from a high dimensional space to a lower dimensional space. It covers standard deviation, covariance, eigenvectors and eigenvalues. Face recognition is obtained by PCA without much loss of information. Experiments using different databases by varying the facial expressions (open/closed eyes, smiling/not smiling) show that the proposed method by combining color space discrimination and PCA can improve face recognition to a great extend.
Detecting Boundaries for Image Segmentation and Object Recognition
This document proposes improvements to image edge detection methods. It summarizes previous approaches that have limitations like poor localization of edges, inability to remove noise, and high computational time. The proposed hybrid approach uses principal component analysis and Canny edge detection in parallel across multiple processors. This achieves faster and more efficient edge detection than prior methods. However, the document suggests edge detection quality could be further improved by using an improved wavelet transformation instead of PCA. It recommends a proposal based on wavelet transformations and Canny detection with operator fusion to first apply wavelet noise cancellation and smoothing before edge detection.
Survey on Local Color Image Descriptors
This document discusses local color image descriptors that can be extracted from images represented using Quaternionic representations. It provides an overview of several existing local descriptors that use Quaternionic representations, including QLRBP, QWLD, and QMCBP. QLRBP uses Clifford translation and local binary coding on the phase to generate descriptors from Quaternionically represented color images. QWLD integrates Quaternionic representation and Weber's law to develop robust descriptors. QMCBP uses Michelson contrast and Quaternionic representation to extract discriminative local features. The document evaluates these approaches on applications like person reidentification and face recognition, finding they outperform methods extracting descriptors from individual color channels.
Traffic Sign Detection
Proposed an algorithm that detects the road signs in the images that were taken by a low-resolution camera mounted in front of a moving car
Real-Time Logo Detection and Tracking
This document presents a hybrid approach for real-time logo detection on mobile devices. The approach uses SIFT to initially detect logos, followed by online color calibration and moment invariants to track logos in subsequent frames. This allows for logo detection that works on different sizes and orientations in real-time. The hybrid approach detects logos within 700ms for initial detection and around 50ms for subsequent frames, providing a processing rate of 20 frames per second. The goal is to port this algorithm to mobile platforms for real-time logo detection applications.
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This document summarizes a research paper that proposes a new method for face recognition using Histogram Gabor Phase Pattern (HGPP) and adaptive binning. The method extracts features from faces using Gabor wavelets and encodes the phase information. It then applies adaptive binning to reduce the dimensionality of the feature space. Spatial histograms of the binned features are used to generate HGPP representations for matching faces. The paper presents the detailed methodology, provides experimental results on FERET databases, and compares performance to existing methods.
IRJET- Design of Image Resolution Enhancement by using DWT and SWT
1) The document proposes a technique for image resolution enhancement using discrete wavelet transform (DWT) and stationary wavelet transform (SWT).
2) It decomposes an input image using DWT into subbands, then applies bicubic interpolation to the high frequency subbands and SWT to minimize information loss.
3) The interpolated high frequency subbands are combined with the SWT high frequency subbands and input image. Inverse DWT is applied to generate a high resolution output image.
Master defence 2020 - Roman Riazantsev - 3D Reconstruction of Video Sign Lan...
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Abstract. Today virtual and augmented reality applications become more and more popular. Such a trend creates a demand for 3D processing algorithms which may be applied to many areas. This work is focused on sigh language video sequences. There are a lot of prerecorded photos and video dictionaries that can be transformed into 3D and unified in one place. We research nuances of hand pose video sequence analysis as well as the influence of results refinement for 2D and 3D keypoint detection. Besides that, we designed a solution for the parametrization of hand shape and engineered system for 3D hand pose reconstruction. Model show good results on train data but lack generalization. Retraining on multiple datasets and usage of various data augmentation techniques will improve performance.Technical concepts for graphic design production 2
Technical concepts for graphic design production includes:
1- History Of Graphic Design.
2- Graphics Types.
3- Bitmaps.
4- Color Gamut.
5- Files Formats.
6- Resolutions.
7- Color Depth.
8- Document Structure.
9- Digital Printing.
10 - pdf.
11- Color Management System CMS.
A Comparative Study on Image Contrast Enhancement Techniques
This document presents a comparative study of various image contrast enhancement techniques. It discusses techniques like histogram equalization, gamma correction, brightness preserving bi-histogram equalization (BBHE), brightness preserving dynamic histogram equalization (BPDHE), and region based adaptive contrast enhancement (RACE). The study evaluates the performance of these techniques on different color images using objective parameters like entropy, absolute contrast error, and peak signal to noise ratio. The results show that the BPDHE technique generally produces enhanced images with less color error, higher contrast-to-noise ratio, and entropy values indicating more details compared to the other techniques. BPDHE is therefore found to be the best technique for enhancing image contrast while preserving color and brightness.
Image Processing(Beta1)
The document discusses image processing and provides details about:
1) The stages of image processing - input, editing, and output. The input stage deals with converting analog images to digital form. The editing stage manipulates the image. The output stage saves the transformed image.
2) Image processing operations like geometric transformations, color corrections, digital compositing, and extending dynamic range.
3) Applications of image processing like face detection, medical imaging, and remote sensing.
Sign Language Recognition Using Image Processing For Mute People
Abstract: Computer recognition of sign language is an important research problem for enabling communication with mute people. This project introduces an efficient and fast algorithm for identification of the number of fingers opened in a gesture representing an alphabet of the Binary Sign Language.
The system does not require the hand to be perfectly aligned to the camera. The project uses image processing system to identify, especially English alphabetic sign language used by the mute people to communicate. The basic objective of this project is to develop a computer based intelligent system that will enable mute people significantly to communicate with all other people using their natural hand gestures.
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