This document presents a combined structure and texture image inpainting algorithm for natural scene image completion. It first decomposes the original image into a structure component and a texture component. It then reconstructs the missing information in the structure component using a structure inpainting algorithm and repairs the texture component using an exemplar-based texture synthesis technique. The algorithm takes advantage of both structure inpainting and texture synthesis methods to effectively reconstruct images. Experimental results on natural images show the proposed approach provides higher quality inpainted images compared to some existing methods.
11.comparative analysis and evaluation of image imprinting algorithmsAlexander Decker
This document compares and evaluates two image inpainting algorithms: Marcelo Bertalmio's PDE-based algorithm and Zhaolin Lu et al's exemplar-based algorithm. Through experiments on images with different sized occluded regions, it finds that the PDE-based algorithm cannot reconnect structures or restore textures in large regions, while the exemplar-based algorithm can find patches to fill regions while preserving structures. Quantitative evaluation using PSNR shows the exemplar-based algorithm achieves lower MSE (error) for occlusion sizes from 10 to 40 pixels. The document provides examples comparing output of the two algorithms and discusses parameters needed for each.
Comparative analysis and evaluation of image imprinting algorithmsAlexander Decker
This document compares and evaluates two different types of image inpainting algorithms: Marcelo Bertalmio's PDE-based algorithm and Zhaolin Lu et al's exemplar-based algorithm. Both algorithms are tested on images with variable occlusion sizes. The PDE-based algorithm is better at preserving linear structures for small regions but cannot reconnect structures or restore texture in large regions. The exemplar-based algorithm can find proper textures to fill large regions while preserving linear structures. Quantitative evaluation using PSNR shows that the exemplar-based algorithm achieves lower MSE values, especially for larger occlusion sizes. Therefore, the exemplar-based algorithm produces better results overall, particularly for filling in large missing regions of an image.
Comparative Study and Analysis of Image Inpainting TechniquesIOSR Journals
Abstract: Image inpainting is a technique to fill missing region or reconstruct damage area from an image.It
removes an undesirable object from an image in visually plausible way.For filling the part of image, it use
information from the neighboring area. In this dissertation work, we present a Examplar based method for
filling in the missing information in an image, which takes structure synthesis and texture sysnthesis together.
In exemplar based approach it used local information from an image to patch propagation.We have also
implement Nonlocal Mean approach for exemplar based image inpainting.In Nonlocal mean approach it find
multiple samples of best exemplar patches for patch propagation and weight their contribution according to
their similarity to the neighborhood under evaluation. We have further extended this algorithm by considering
collaborative filtering method to synthesize and propagate with multiple samples of best exemplar patches. We
have to preformed experiment on many images and found that our algorithm successfully inpaint the target
region.We have tested the accuracy of our algorithm by finding parameter like PSNR and compared PSNR
value for all three different approaches.
Keywords: Texture Synthesis, Structure Synthesis, Patch Propagation ,imageinpainting ,nonlocal approach,
collabrative filtering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A Survey of Image Segmentation based on Artificial Intelligence and Evolution...IOSR Journals
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
This document discusses the application of morphological image processing in forensics for fingerprint enhancement. It provides background on morphological operations like dilation, erosion, opening and closing. It explains how these operations can be used to enhance degraded fingerprints by thickening ridges, joining broken ridges, and separating overlapped ridges. The morphological image processing concepts are implemented in Java to experimentally enhance fingerprint images and reduce noise.
This document summarizes a paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
11.comparative analysis and evaluation of image imprinting algorithmsAlexander Decker
This document compares and evaluates two image inpainting algorithms: Marcelo Bertalmio's PDE-based algorithm and Zhaolin Lu et al's exemplar-based algorithm. Through experiments on images with different sized occluded regions, it finds that the PDE-based algorithm cannot reconnect structures or restore textures in large regions, while the exemplar-based algorithm can find patches to fill regions while preserving structures. Quantitative evaluation using PSNR shows the exemplar-based algorithm achieves lower MSE (error) for occlusion sizes from 10 to 40 pixels. The document provides examples comparing output of the two algorithms and discusses parameters needed for each.
Comparative analysis and evaluation of image imprinting algorithmsAlexander Decker
This document compares and evaluates two different types of image inpainting algorithms: Marcelo Bertalmio's PDE-based algorithm and Zhaolin Lu et al's exemplar-based algorithm. Both algorithms are tested on images with variable occlusion sizes. The PDE-based algorithm is better at preserving linear structures for small regions but cannot reconnect structures or restore texture in large regions. The exemplar-based algorithm can find proper textures to fill large regions while preserving linear structures. Quantitative evaluation using PSNR shows that the exemplar-based algorithm achieves lower MSE values, especially for larger occlusion sizes. Therefore, the exemplar-based algorithm produces better results overall, particularly for filling in large missing regions of an image.
Comparative Study and Analysis of Image Inpainting TechniquesIOSR Journals
Abstract: Image inpainting is a technique to fill missing region or reconstruct damage area from an image.It
removes an undesirable object from an image in visually plausible way.For filling the part of image, it use
information from the neighboring area. In this dissertation work, we present a Examplar based method for
filling in the missing information in an image, which takes structure synthesis and texture sysnthesis together.
In exemplar based approach it used local information from an image to patch propagation.We have also
implement Nonlocal Mean approach for exemplar based image inpainting.In Nonlocal mean approach it find
multiple samples of best exemplar patches for patch propagation and weight their contribution according to
their similarity to the neighborhood under evaluation. We have further extended this algorithm by considering
collaborative filtering method to synthesize and propagate with multiple samples of best exemplar patches. We
have to preformed experiment on many images and found that our algorithm successfully inpaint the target
region.We have tested the accuracy of our algorithm by finding parameter like PSNR and compared PSNR
value for all three different approaches.
Keywords: Texture Synthesis, Structure Synthesis, Patch Propagation ,imageinpainting ,nonlocal approach,
collabrative filtering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A Survey of Image Segmentation based on Artificial Intelligence and Evolution...IOSR Journals
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
This document discusses the application of morphological image processing in forensics for fingerprint enhancement. It provides background on morphological operations like dilation, erosion, opening and closing. It explains how these operations can be used to enhance degraded fingerprints by thickening ridges, joining broken ridges, and separating overlapped ridges. The morphological image processing concepts are implemented in Java to experimentally enhance fingerprint images and reduce noise.
This document summarizes a paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
This document summarizes and analyzes image segmentation and edge detection techniques for medical images. It discusses several current segmentation methods like histogram-based, edge detection, region growing, level set, and graph partitioning methods. The document then proposes a new active contour model for image segmentation that uses both edge and region information to segment images with undefined boundaries. It also discusses solving computational difficulties of models using level set theory. In conclusion, the proposed segmentation algorithms are shown to outperform some well-known methods in accuracy and processing speed.
REMOVING OCCLUSION IN IMAGES USING SPARSE PROCESSING AND TEXTURE SYNTHESISIJCSEA Journal
The document presents a method for removing large occlusions from images using sparse processing and texture synthesis. It involves decomposing the image into structure and texture images using sparse representations. The occluded regions in the structure image are filled in using sparse reconstruction, which retains image structures. Texture synthesis is then performed on the texture image to fill in the occluded texture. Finally, the reconstructed structure and texture images are combined to produce the occlusion-free output image. The method is shown to effectively remove large occlusions while avoiding blurring and retaining both structures and textures. It outperforms other inpainting methods in terms of visual quality.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document summarizes a research paper that proposes an algorithm for detecting brain tumors in MRI images based on analyzing bilateral symmetry. The algorithm first performs preprocessing like smoothing and contrast enhancement. It then identifies the bilateral symmetry axis of the brain. Next, it segments the image into symmetric regions, enhancing asymmetric edges that may indicate a tumor. Experiments showed the algorithm can automatically detect tumor positions and boundaries. The algorithm leverages the fact that brain MRI of a healthy person is nearly bilaterally symmetric, while a tumor disrupts this symmetry.
OBJECT SEGMENTATION USING MULTISCALE MORPHOLOGICAL OPERATIONSijcseit
Object segmentation plays an important role in human visual perception, medical image processing and content based image retrieval. It provides information for recognition and interpretation. This paper uses mathematical morphology for image segmentation. Object segmentation is difficult because one usually does not know a priori what type of object exists in an image, how many different shapes are there and what regions the image has. To carryout discrimination and segmentation several innovative segmentation methods, based on morphology are proposed. The present study proposes segmentation method based on multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.The method is tested on various datasets and results shows that it can be used for both interactive and automatic segmentation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image Enhancement and Restoration by Image InpaintingIJERA Editor
Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e .it fills the missing or damaged region in an image utilizing spatial information of its neighboring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. The main goal of the algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. This proposed work presents image inpainting process for image enhancement and restoration by using structural, texture and exemplar techniques. This paper presents efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a blockbased sampling process.
The document discusses clustering images based on their properties. Images are converted into intensity, contrast, Weibull and fractal images. Eight properties are calculated for each image type, including brightness, standard deviation, entropy, skewness, kurtosis, separability, spatial frequency and visibility. The properties are normalized and clustered using k-means clustering. Tables show normalized property values for different image types. The clustering groups similar images based on their discriminative properties.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document summarizes and reviews different techniques for video inpainting. It begins by defining video inpainting and distinguishing it from image inpainting due to additional temporal factors that must be considered. It then categorizes and reviews three main approaches: PDE-based methods, texture synthesis methods, and patch-based methods. For each approach, one or two influential works are described, focusing on the techniques and limitations. The review concludes by noting that while progress has been made, developing a video inpainting technique that can ensure both spatial and temporal consistency remains a challenge.
This document provides a review of various approaches for image inpainting, which is the process of restoring lost or damaged parts of an image. It discusses partial differential equation (PDE) based inpainting, exemplar based inpainting, texture synthesis based inpainting, and hybrid inpainting approaches. PDE based methods diffuse image information into missing regions but can produce blurry results for large textures. Exemplar based methods iteratively copy patches from surrounding areas to fill missing regions, better preserving textures but being computationally expensive. The document provides an overview of different inpainting techniques and their applications and limitations.
Optical character recognition (OCR) is a technology that converts images of typed, handwritten or printed text into machine-encoded text. The document describes the OCR process which includes image pre-processing, segmentation, feature extraction and recognition using a multi-layer perceptron neural network. It discusses advantages such as increased efficiency and ability to instantly search text. Disadvantages include issues with low quality documents. Applications include data entry for business documents and making printed documents searchable.
In this project we have implemented a tool to inpaint selected regions from an image. Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information. The tool provides a user interface wherein the user can open an image for inpainting, select the parts
of the image that he wants to reconstruct. The tool would then automatically inpaint the selected area according to the background information. The image can
then be saved. The inpainting in based on the exemplar based approach. The basic aim of this approach is to find examples (i.e. patches) from the image and
replace the lost data with it. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like
dates, subtitles etc.; and the removal of entire objects from the image like microphones or wires in special effects.
Study of Image Inpainting Technique Based on TV Modelijsrd.com
This paper is related with an image inpainting method by which we can reconstruct a damaged or missing portion of an image. A fast image inpainting algorithm based on TV (Total variational) model is proposed on the basis of analysis of local characteristics, which shows the more information around damaged pixels appears, the faster the information diffuses. The algorithm first stratifies and filters the pixels around damaged region according to priority, and then iteratively inpaint the damaged pixels from outside to inside on the grounds of priority again. By using this algorithm inpainting speed of the algorithm is faster and greater impact.
A Review on Image Inpainting to Restore ImageIOSR Journals
This document reviews various techniques for image inpainting to restore damaged images. It discusses diffusion-based inpainting and texture synthesis approaches. Specific techniques covered include:
1. PDE-based inpainting using isophote lines from surrounding areas.
2. Multiresolution inpainting dividing images into blocks and considering variance, percentage of damaged pixels.
3. Exemplar-based completion using image fragments from global examples.
4. Inpainting of natural scenes limiting search horizontally using Fourier transforms.
The document compares advantages and disadvantages of each approach for efficiently and accurately restoring images. Wavelet transforms and morphological component analysis are also reviewed for inpainting texture and cartoon layers
This document summarizes and analyzes image segmentation and edge detection techniques for medical images. It discusses several current segmentation methods like histogram-based, edge detection, region growing, level set, and graph partitioning methods. The document then proposes a new active contour model for image segmentation that uses both edge and region information to segment images with undefined boundaries. It also discusses solving computational difficulties of models using level set theory. In conclusion, the proposed segmentation algorithms are shown to outperform some well-known methods in accuracy and processing speed.
REMOVING OCCLUSION IN IMAGES USING SPARSE PROCESSING AND TEXTURE SYNTHESISIJCSEA Journal
The document presents a method for removing large occlusions from images using sparse processing and texture synthesis. It involves decomposing the image into structure and texture images using sparse representations. The occluded regions in the structure image are filled in using sparse reconstruction, which retains image structures. Texture synthesis is then performed on the texture image to fill in the occluded texture. Finally, the reconstructed structure and texture images are combined to produce the occlusion-free output image. The method is shown to effectively remove large occlusions while avoiding blurring and retaining both structures and textures. It outperforms other inpainting methods in terms of visual quality.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document summarizes a research paper that proposes an algorithm for detecting brain tumors in MRI images based on analyzing bilateral symmetry. The algorithm first performs preprocessing like smoothing and contrast enhancement. It then identifies the bilateral symmetry axis of the brain. Next, it segments the image into symmetric regions, enhancing asymmetric edges that may indicate a tumor. Experiments showed the algorithm can automatically detect tumor positions and boundaries. The algorithm leverages the fact that brain MRI of a healthy person is nearly bilaterally symmetric, while a tumor disrupts this symmetry.
OBJECT SEGMENTATION USING MULTISCALE MORPHOLOGICAL OPERATIONSijcseit
Object segmentation plays an important role in human visual perception, medical image processing and content based image retrieval. It provides information for recognition and interpretation. This paper uses mathematical morphology for image segmentation. Object segmentation is difficult because one usually does not know a priori what type of object exists in an image, how many different shapes are there and what regions the image has. To carryout discrimination and segmentation several innovative segmentation methods, based on morphology are proposed. The present study proposes segmentation method based on multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.The method is tested on various datasets and results shows that it can be used for both interactive and automatic segmentation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image Enhancement and Restoration by Image InpaintingIJERA Editor
Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e .it fills the missing or damaged region in an image utilizing spatial information of its neighboring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. The main goal of the algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. This proposed work presents image inpainting process for image enhancement and restoration by using structural, texture and exemplar techniques. This paper presents efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a blockbased sampling process.
The document discusses clustering images based on their properties. Images are converted into intensity, contrast, Weibull and fractal images. Eight properties are calculated for each image type, including brightness, standard deviation, entropy, skewness, kurtosis, separability, spatial frequency and visibility. The properties are normalized and clustered using k-means clustering. Tables show normalized property values for different image types. The clustering groups similar images based on their discriminative properties.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document summarizes and reviews different techniques for video inpainting. It begins by defining video inpainting and distinguishing it from image inpainting due to additional temporal factors that must be considered. It then categorizes and reviews three main approaches: PDE-based methods, texture synthesis methods, and patch-based methods. For each approach, one or two influential works are described, focusing on the techniques and limitations. The review concludes by noting that while progress has been made, developing a video inpainting technique that can ensure both spatial and temporal consistency remains a challenge.
This document provides a review of various approaches for image inpainting, which is the process of restoring lost or damaged parts of an image. It discusses partial differential equation (PDE) based inpainting, exemplar based inpainting, texture synthesis based inpainting, and hybrid inpainting approaches. PDE based methods diffuse image information into missing regions but can produce blurry results for large textures. Exemplar based methods iteratively copy patches from surrounding areas to fill missing regions, better preserving textures but being computationally expensive. The document provides an overview of different inpainting techniques and their applications and limitations.
Optical character recognition (OCR) is a technology that converts images of typed, handwritten or printed text into machine-encoded text. The document describes the OCR process which includes image pre-processing, segmentation, feature extraction and recognition using a multi-layer perceptron neural network. It discusses advantages such as increased efficiency and ability to instantly search text. Disadvantages include issues with low quality documents. Applications include data entry for business documents and making printed documents searchable.
In this project we have implemented a tool to inpaint selected regions from an image. Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information. The tool provides a user interface wherein the user can open an image for inpainting, select the parts
of the image that he wants to reconstruct. The tool would then automatically inpaint the selected area according to the background information. The image can
then be saved. The inpainting in based on the exemplar based approach. The basic aim of this approach is to find examples (i.e. patches) from the image and
replace the lost data with it. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like
dates, subtitles etc.; and the removal of entire objects from the image like microphones or wires in special effects.
Study of Image Inpainting Technique Based on TV Modelijsrd.com
This paper is related with an image inpainting method by which we can reconstruct a damaged or missing portion of an image. A fast image inpainting algorithm based on TV (Total variational) model is proposed on the basis of analysis of local characteristics, which shows the more information around damaged pixels appears, the faster the information diffuses. The algorithm first stratifies and filters the pixels around damaged region according to priority, and then iteratively inpaint the damaged pixels from outside to inside on the grounds of priority again. By using this algorithm inpainting speed of the algorithm is faster and greater impact.
A Review on Image Inpainting to Restore ImageIOSR Journals
This document reviews various techniques for image inpainting to restore damaged images. It discusses diffusion-based inpainting and texture synthesis approaches. Specific techniques covered include:
1. PDE-based inpainting using isophote lines from surrounding areas.
2. Multiresolution inpainting dividing images into blocks and considering variance, percentage of damaged pixels.
3. Exemplar-based completion using image fragments from global examples.
4. Inpainting of natural scenes limiting search horizontally using Fourier transforms.
The document compares advantages and disadvantages of each approach for efficiently and accurately restoring images. Wavelet transforms and morphological component analysis are also reviewed for inpainting texture and cartoon layers
Composite materials are made of a resin matrix and filler particles. They have superior properties to their individual components. There are several types of composites classified by filler particle size: macrofilled (8-12 μm), small particle (1-5 μm), microfilled (0.04-0.4 μm), and hybrid (1 μm). Macrofilled composites have the largest particles and produce the roughest surfaces, while microfilled composites have the smallest particles and smoothest surfaces. Hybrid composites have a mixture of particle sizes. The different types have various indications for use depending on their mechanical properties and ability to be polished.
This document summarizes several existing techniques for image inpainting, including texture synthesis, geometric partial differential equations (PDEs), and coherence-based methods. It then proposes a combined model that incorporates elements of these different approaches into a variational framework. Specifically, it suggests combining texture synthesis, PDE-based diffusion, and enforcing coherence among neighboring pixels and across frames for video inpainting. The goal is to approximate the minimum of the proposed energy functional to better fill in missing or corrupted regions of images and video frames.
Inpainting refers to the art of restoring lost parts of image and reconstructing them based on the background information i.e Image inpainting is the process of reconstructing lost or deteriorated parts of images using information from surrounding areas. In fine art museums, inpainting of degraded paintings is traditionally carried out by professional artists and usually very time consuming.The purpose of inpainting is to reconstruct missing regions in a visually plausible manner so that it seems reasonable to the human eye. There have been several approaches proposed for the same.
This paper gives an overview of different Techniques of Image Inpainting.The proposed work includes the overview of PDE based inpainting algorithm and Texture synthesis based inpainting algorithm. This paper presents a brief survey on comparative study of these two techniques used for Image Inpainting.
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
This paper proposes a new algorithm for removing large objects from digital images by filling in the removed region in a visually plausible manner. It combines exemplar-based texture synthesis with structure propagation from image inpainting. The key insights are that exemplar-based texture synthesis can replicate both texture and structure if the filling order is determined by propagating confidence in pixel values similar to inpainting, and that a single algorithm can synthesize both pure and composite textures. The proposed algorithm efficiently synthesizes textures and propagates structures into the removed region by sampling exemplar patches based on a confidence term calculated from local image gradients. This allows it to generate natural-looking results while avoiding blurring issues of previous approaches.
A CONCERT EVALUATION OF EXEMPLAR BASED IMAGE INPAINTING ALGORITHMS FOR NATURA...cscpconf
Image inpainting derives from restoration of art works, and has been applied to repair ancient
art works. Inpainting is a technique of restoring a partially damaged or occluded image in an
undetectable way. It fills the damaged part of an image by employing information of the
undamaged part according to some rules to make it look “reasonable” to human eyes. Digital
image inpainting is relatively new area of research, but numerous and different approaches to
tackle the inpainting problem have been proposed since the concept was first introduced. This
paper analyzes and compares the recent exemplar based inpainting algorithms by Minqin Wang
and Hao Guo et al. A number of examples on real images are demonstrated to evaluate the
results of algorithms using Peak Signal to Noise Ratio (PSNR)
Texture Unit based Approach to Discriminate Manmade Scenes from Natural Scenesidescitation
This document summarizes a research paper that proposes a method to discriminate between natural and manmade scenes using texture analysis. It analyzes local texture information in images using a "texture unit matrix" approach. Texture units characterize the texture of a pixel and its neighbors. Texture unit matrices are generated from images and used to form feature vectors. A self-organizing map (SOM) classifier is then used to classify images as natural or manmade based on these feature vectors. The researchers tested their method on databases of "near" scenes within 10 meters and "far" scenes about 500 meters away. Their results found that analyzing the minimum texture unit matrix in a base-5 approach provided the most accurate classifications between natural and manmade scenes
IRJET - Deep Learning Approach to Inpainting and Outpainting SystemIRJET Journal
This document discusses a deep learning approach for image inpainting and outpainting. It proposes a new generative model-based approach using a fully convolutional neural network that can process images with multiple holes at variable locations and sizes. The model aims to not only synthesize novel image structures, but also explicitly utilize surrounding image features as references during training to generate better predictions. Experiments on faces, textures and natural images demonstrate the proposed approach generates higher quality inpainting results than existing methods. It aims to address limitations of CNNs in borrowing information from distant areas by leveraging texture and patch synthesis approaches.
Image in Painting Techniques: A survey IOSR Journals
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
Texture refers to the spatial arrangement of basic image elements or textons. It provides useful information for identifying objects or regions of interest. Texture representation has applications in areas like object recognition, medical imaging, and scene understanding. This document discusses several aspects of texture representation, including texture shape extraction, texture synthesis, and texture segmentation. It also describes challenges in representing textures from large images and video, and proposes a method to automatically infer a compact texture representation from input data without external information.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
Texture Segmentation Based on Multifractal Dimension ijsc
This document presents a new texture segmentation algorithm based on multifractal dimension. The algorithm divides an image into blocks and extracts feature vectors for each block using box counting method on multiple thresholds of the image. A supervised learning phase is used to classify blocks based on these feature vectors by extracting mean and standard deviation values for sample windows labeled by an expert. The algorithm was tested on multi-texture images by extracting feature vectors for each small block and classifying them based on the trained classifier.
DIGITAL RESTORATION OF TORN FILMS USING FILTERING T ECHNIQUESAM Publications
The acceptance of digital imaging is motivating many photography enthusiasts to transfer their
photographic archive to digital form. Scans of negatives and positives are preferred to be scanned at high resolution
which makes small cracks and scratches very apparent. These unsightly defects have become an important issue
for consumers. Filtering techniques are used for the restoration process which is fully automatic whereas the existing
systems were semi-automatic or completely manual. The method used for the detection of tear is dilation process and
top-hat transform. Top-hat transform might misinterpret dark brush strokes as cracks. In order to avoid these
unwanted alterations to the original image, brush strokes are separated from the actual cracks using clustering
technique. Tear removal includes order statistics filtering which deals with the reconstruction of missing or
damaged image areas.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
AUTOMATED IMAGE MOSAICING SYSTEM WITH ANALYSIS OVER VARIOUS IMAGE NOISEijcsa
Mosaicing is blending together of several arbitrarily shaped images to form one large balanced image such
that boundaries between the original images are not seen. Image mosaicing creates a large field of view
using of scene and the result image can be used for texture mapping of a 3D environment too. Blended
image has become a wide necessity in images captured from real time sensor devices, bio-medical
equipment, satellite images from space, aerospace, security systems, brain mapping, genetics etc. Idea
behind this work is to automate the Image Mosaicing System so that blending may be fast, easy and
efficient even if large number of images are considered. This work also provides an analysis of blending
over images containing different kinds of distortion and noise which further enhances the quality of the
system and make the system more reliable and robust.
Content Based Image Retrieval Approach Based on Top-Hat Transform And Modifie...cscpconf
In this paper a robust approach is proposed for content based image retrieval (CBIR) using texture analysis techniques. The proposed approach includes three main steps. In the first one, shape detection is done based on Top-Hat transform to detect and crop object part of the image. Second step is included a texture feature representation algorithm using color local binary patterns (CLBP) and local variance features. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Low noise sensitivity, rotation invariant, shift invariant, gray scale invariant and low computational complexity are some of other advantages.
A Combined Method with automatic parameter optimization for Multi-class Image...AM Publications
Multi-class image semantic segmentation deals with many applications in consumer electronics
fields such as image editing and image retrieval. Segmentation is done by combining the top down and bottomup
segmentation. Top-Down Process can be done by Semantic Texton Forest and bottom up- process using
JSEG. These two segmentation process can be executed in a combined manner. But this cannot choose the
optimal value of JSEG parameter for each interested semantic category. Hence an automatic parameter selection
algorithm has been proposed. An automatic parameter selection technique called an automatic multilevel
thresholding algorithm using stratified sampling and PSO is used to remedy the limitations.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
This document summarizes a research paper that proposes a new ontology-based method for automatic image retrieval and annotation using 5,000 images from the Corel dataset. The method combines global and regional visual features with contextual relationships defined in an ontology. It creates a new ontology based on WordNet to semantically relate tags and reduce gaps between low-level features and high-level concepts. Experimental results show the proposed method increases annotation accuracy compared to other methods.
Similar to 11.combined structure and texture image inpainting algorithm for natural scene image completion (20)
Abnormalities of hormones and inflammatory cytokines in women affected with p...Alexander Decker
Women with polycystic ovary syndrome (PCOS) have elevated levels of hormones like luteinizing hormone and testosterone, as well as higher levels of insulin and insulin resistance compared to healthy women. They also have increased levels of inflammatory markers like C-reactive protein, interleukin-6, and leptin. This study found these abnormalities in the hormones and inflammatory cytokines of women with PCOS ages 23-40, indicating that hormone imbalances associated with insulin resistance and elevated inflammatory markers may worsen infertility in women with PCOS.
A usability evaluation framework for b2 c e commerce websitesAlexander Decker
This document presents a framework for evaluating the usability of B2C e-commerce websites. It involves user testing methods like usability testing and interviews to identify usability problems in areas like navigation, design, purchasing processes, and customer service. The framework specifies goals for the evaluation, determines which website aspects to evaluate, and identifies target users. It then describes collecting data through user testing and analyzing the results to identify usability problems and suggest improvements.
A universal model for managing the marketing executives in nigerian banksAlexander Decker
This document discusses a study that aimed to synthesize motivation theories into a universal model for managing marketing executives in Nigerian banks. The study was guided by Maslow and McGregor's theories. A sample of 303 marketing executives was used. The results showed that managers will be most effective at motivating marketing executives if they consider individual needs and create challenging but attainable goals. The emerged model suggests managers should provide job satisfaction by tailoring assignments to abilities and monitoring performance with feedback. This addresses confusion faced by Nigerian bank managers in determining effective motivation strategies.
A unique common fixed point theorems in generalized dAlexander Decker
This document presents definitions and properties related to generalized D*-metric spaces and establishes some common fixed point theorems for contractive type mappings in these spaces. It begins by introducing D*-metric spaces and generalized D*-metric spaces, defines concepts like convergence and Cauchy sequences. It presents lemmas showing the uniqueness of limits in these spaces and the equivalence of different definitions of convergence. The goal of the paper is then stated as obtaining a unique common fixed point theorem for generalized D*-metric spaces.
A trends of salmonella and antibiotic resistanceAlexander Decker
This document provides a review of trends in Salmonella and antibiotic resistance. It begins with an introduction to Salmonella as a facultative anaerobe that causes nontyphoidal salmonellosis. The emergence of antimicrobial-resistant Salmonella is then discussed. The document proceeds to cover the historical perspective and classification of Salmonella, definitions of antimicrobials and antibiotic resistance, and mechanisms of antibiotic resistance in Salmonella including modification or destruction of antimicrobial agents, efflux pumps, modification of antibiotic targets, and decreased membrane permeability. Specific resistance mechanisms are discussed for several classes of antimicrobials.
A transformational generative approach towards understanding al-istifhamAlexander Decker
This document discusses a transformational-generative approach to understanding Al-Istifham, which refers to interrogative sentences in Arabic. It begins with an introduction to the origin and development of Arabic grammar. The paper then explains the theoretical framework of transformational-generative grammar that is used. Basic linguistic concepts and terms related to Arabic grammar are defined. The document analyzes how interrogative sentences in Arabic can be derived and transformed via tools from transformational-generative grammar, categorizing Al-Istifham into linguistic and literary questions.
A time series analysis of the determinants of savings in namibiaAlexander Decker
This document summarizes a study on the determinants of savings in Namibia from 1991 to 2012. It reviews previous literature on savings determinants in developing countries. The study uses time series analysis including unit root tests, cointegration, and error correction models to analyze the relationship between savings and variables like income, inflation, population growth, deposit rates, and financial deepening in Namibia. The results found inflation and income have a positive impact on savings, while population growth negatively impacts savings. Deposit rates and financial deepening were found to have no significant impact. The study reinforces previous work and emphasizes the importance of improving income levels to achieve higher savings rates in Namibia.
A therapy for physical and mental fitness of school childrenAlexander Decker
This document summarizes a study on the importance of exercise in maintaining physical and mental fitness for school children. It discusses how physical and mental fitness are developed through participation in regular physical exercises and cannot be achieved solely through classroom learning. The document outlines different types and components of fitness and argues that developing fitness should be a key objective of education systems. It recommends that schools ensure pupils engage in graded physical activities and exercises to support their overall development.
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This document summarizes a study examining efficiency in managing marketing executives in Nigerian banks. The study was examined through the lenses of Kaizen theory (continuous improvement) and efficiency theory. A survey of 303 marketing executives from Nigerian banks found that management plays a key role in identifying and implementing efficiency improvements. The document recommends adopting a "3H grand strategy" to improve the heads, hearts, and hands of management and marketing executives by enhancing their knowledge, attitudes, and tools.
This document discusses evaluating the link budget for effective 900MHz GSM communication. It describes the basic parameters needed for a high-level link budget calculation, including transmitter power, antenna gains, path loss, and propagation models. Common propagation models for 900MHz that are described include Okumura model for urban areas and Hata model for urban, suburban, and open areas. Rain attenuation is also incorporated using the updated ITU model to improve communication during rainfall.
A synthetic review of contraceptive supplies in punjabAlexander Decker
This document discusses contraceptive use in Punjab, Pakistan. It begins by providing background on the benefits of family planning and contraceptive use for maternal and child health. It then analyzes contraceptive commodity data from Punjab, finding that use is still low despite efforts to improve access. The document concludes by emphasizing the need for strategies to bridge gaps and meet the unmet need for effective and affordable contraceptive methods and supplies in Punjab in order to improve health outcomes.
A synthesis of taylor’s and fayol’s management approaches for managing market...Alexander Decker
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A survey paper on sequence pattern mining with incrementalAlexander Decker
This document summarizes four algorithms for sequential pattern mining: GSP, ISM, FreeSpan, and PrefixSpan. GSP is an Apriori-based algorithm that incorporates time constraints. ISM extends SPADE to incrementally update patterns after database changes. FreeSpan uses frequent items to recursively project databases and grow subsequences. PrefixSpan also uses projection but claims to not require candidate generation. It recursively projects databases based on short prefix patterns. The document concludes by stating the goal was to find an efficient scheme for extracting sequential patterns from transactional datasets.
A survey on live virtual machine migrations and its techniquesAlexander Decker
This document summarizes several techniques for live virtual machine migration in cloud computing. It discusses works that have proposed affinity-aware migration models to improve resource utilization, energy efficient migration approaches using storage migration and live VM migration, and a dynamic consolidation technique using migration control to avoid unnecessary migrations. The document also summarizes works that have designed methods to minimize migration downtime and network traffic, proposed a resource reservation framework for efficient migration of multiple VMs, and addressed real-time issues in live migration. Finally, it provides a table summarizing the techniques, tools used, and potential future work or gaps identified for each discussed work.
A survey on data mining and analysis in hadoop and mongo dbAlexander Decker
This document discusses data mining of big data using Hadoop and MongoDB. It provides an overview of Hadoop and MongoDB and their uses in big data analysis. Specifically, it proposes using Hadoop for distributed processing and MongoDB for data storage and input. The document reviews several related works that discuss big data analysis using these tools, as well as their capabilities for scalable data storage and mining. It aims to improve computational time and fault tolerance for big data analysis by mining data stored in Hadoop using MongoDB and MapReduce.
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3. Scalability and expandability challenges involve adapting to the increasing volume of media content and being able to support new media formats and outlets over time.
This document surveys trust architectures that leverage provenance in wireless sensor networks. It begins with background on provenance, which refers to the documented history or derivation of data. Provenance can be used to assess trust by providing metadata about how data was processed. The document then discusses challenges for using provenance to establish trust in wireless sensor networks, which have constraints on energy and computation. Finally, it provides background on trust, which is the subjective probability that a node will behave dependably. Trust architectures need to be lightweight to account for the constraints of wireless sensor networks.
This document discusses private equity investments in Kenya. It provides background on private equity and discusses trends in various regions. The objectives of the study discussed are to establish the extent of private equity adoption in Kenya, identify common forms of private equity utilized, and determine typical exit strategies. Private equity can involve venture capital, leveraged buyouts, or mezzanine financing. Exits allow recycling of capital into new opportunities. The document provides context on private equity globally and in developing markets like Africa to frame the goals of the study.
This document discusses a study that analyzes the financial health of the Indian logistics industry from 2005-2012 using Altman's Z-score model. The study finds that the average Z-score for selected logistics firms was in the healthy to very healthy range during the study period. The average Z-score increased from 2006 to 2010 when the Indian economy was hit by the global recession, indicating the overall performance of the Indian logistics industry was good. The document reviews previous literature on measuring financial performance and distress using ratios and Z-scores, and outlines the objectives and methodology used in the current study.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
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This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
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11.combined structure and texture image inpainting algorithm for natural scene image completion
1. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.1, 2011
Combined Structure and Texture Image Inpainting Algorithm for
Natural Scene Image Completion
K. Sangeetha
Department of Computer Applications, Bannari Amman Institute of Technology
Sathyamangalam, Erode District 638 401, Tamilnadu, India
E-mail: kavigeeth@yahoo.com
Dr. P. Sengottuvelan
Department of Information Technology, Bannari Amman Institute of Technology
Sathyamangalam, Erode District 638 401, Tamilnadu, India
E-mail:sengottuvelanp@rediffmail.com
E. Balamurugan
Department of Computer Applications, Bannari Amman Institute of Technology
Sathyamangalam, Erode District 638 401, Tamilnadu, India
E-mail: rethinbs@gmail.com
Abstract
Image inpainting or image completion refers to the task of filling in the missing or damaged regions of an image
in a visually plausible way. Many works on this subject have been proposed these recent years. We present a
hybrid method for completion of images of natural scenery, where the removal of a foreground object creates a
hole in the image. The basic idea is to decompose the original image into a structure and a texture image.
Reconstruction of each image is performed separately. The missing information in the structure component is
reconstructed using a structure inpainting algorithm, while the texture component is repaired by an improved
exemplar based texture synthesis technique. Taking advantage of both the structure inpainting methods and
texture synthesis techniques, we designed an effective image reconstruction method. A comparison with some
existing methods on different natural images shows the merits of our proposed approach in providing high
quality inpainted images.
Keywords: Image inpainting, Decomposition method, Structure inpainting, Exemplar based, Texture synthesis
1. Introduction
Digital Image inpainting is the process of filling-in missing or damaged image information. Its applications
include removing scratches in old photos, removing text or logos, repairing damaged areas in unreliably
transmitted images, image zooming, completing the holes after removing undesired objects from an image, or
even creating artistic effects. Since the missing or damaged areas cannot be simply classified objectively; the
user needs to identify them. These specified regions are called inpainting domain.
The removal of portions of an image is an important tool in photo-editing and film post-production, such as
image restoration (e.g. scratch removal) and special effects (e.g. removal of objects). Image completion is an
area related to texture synthesis. Inpainting techniques were naturally extended from paintings to images and
films (Bertalmio et al 2000). Image inpainting and image completion differ in the area that is to be filled or
completed. In image completion regions are large and consist of textures, large scale structures and smooth
areas. The region to be removed is specified by the user. It is, generally, some foreground element that needs to
be taken off the scene. After removing the foreground the area is to be filled so that the image looks naturally
complete (Drori et al 2003).
Texture synthesis can also be used to complete regions where the texture is stationary or structured.
Reconstructing methods can be used to fill in large scale missing regions by interpolation. Inpainting is suitable
for relatively small, smooth and non-textured regions. Our approach focuses on image based completion; with
no knowledge of the underlying scene (Drori et al 2003). The image is completed by searching for appropriate
textures all over the image, such that on completion it preserves the natural appearance of the image.
For structure images and texture images, there are algorithms of two classes to tackle the inpainting problems:
Partial Differential Equation (PDE) or Calculus of Variation (CoV) based inpainting algorithms for structure
images (Bertalmio et al 2000, Chan et al 2001 and Chan 2002) and texture synthesis based algorithms for
texture images (Efros and T. Leung 1999, Criminisi et al 2004, Wong and Orchard 2008, G. T. N.
Komodakis 2007). The former class of algorithms completes the images by diffusing known surrounding
information into the inpainting area, while the latter find texture block from known region best matched block
formed by the neighborhood of a selected pixel on the boundary of inpainting domain, and then substitute the
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2. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.1, 2011
counterpart in the matched block for the unknown part of the pixel-neighborhood block. There are a few works
of hybrid inpainting algorithms based on image decomposition (Bertalmio et al 2003, Harald (2004), Rane et
al 2003).
2. Background and Review of Inpainting
For the inpainting problem it is essential to proceed to the discrimination between the structure and the texture
of an image. As structure we can define the main parts - objects of an image, whose surface is homogeneous
without having any details. As texture we can define the details on the surface of the objects which make the
images more realistic.
2.1. Structure Inpainting
The term inpainting was first used by (Bertalmio et al 2000). The idea is to propagate the isophotes that arrive at
the boundary of the inpainting region, smoothly inside the region while preserving the arrival angle. In the same
context of mimicking a natural process, Bertalmio et al. suggested another similar model, where the evolution of
the isophotes is based on the Navier Stokes equations that govern the evolution of fluid dynamics (Bertalmio et
al 2001). Apart from physical processes, images can also be modeled as elements of certain function spaces. An
early related work under the word”disocclusion” rather than inpainting was done by Masnou and Morel (1998).
Chan and Shen (2001) derived an inpainting model by considering the image as an element of the space of
Bounded Variation (BV) images, endowed with the Total Variation (TV) norm. The solution of the inpainting
problem comes from the minimization of an appropriate functional. This TV inpainting model was extended by
Chan and Shen (2001)to take into consideration curvature and the so called connectivity principle according to
which the human eye tends to reconstruct the broken edges. Along similar lines, Mumford and Shah (1989)
proposed another inpainting model, which takes explicit care of the edges on the functional to be minimized. Its
extension to account for curvature was proposed by Esedoglu and Shen (2002) using the Euler’s elastica.
2.2. Texture Inpainting
The problem of texture inpainting is highly connected with the problem of texture synthesis. A very simple and
highly effective algorithm was presented by Efros and Leung(1999). In this algorithm the image is modeled as a
Markov Random Field and texture is synthesized in a pixel by pixel way, by picking existing pixels with similar
neighborhoods in a randomized fashion. This algorithm performs very well but it is very slow since the filling-in
is being done pixel by pixel. An algorithm specific for texture inpainting was presented by Criminisi et al
(2004). The algorithm uses the same texture synthesis techniques as the Efros-Leung algorithm. The only
difference is that the pixels that are placed along the edges of the image are filled in with high priority. This
slight difference is adequate to give better results for image inpainting as is presented in the results by Criminisi
et al (2004).
2.3. Simultaneous Structure and Texture Inpainting
Since, in most images both structure and texture are present, a natural thing is to combine structure and texture
inpainting techniques to obtain more effective techniques, regarding image inpainting. This was done by
Brennan (2007). The algorithm is very simple. First the image is decomposed into its structure and texture
components, and then inpainting techniques are performed for both structure and for texture. Brennan (2007)
also proposed a model for simultaneous structure and texture image inpainting. Zhang Hong-bin, Wang Jia-wen
(2007) proposed an approach for image inpainting by integrating both structure and texture features. For
structure texture decomposition many variation methods can be used, like Meyer (2002) which was first used
for image denoising, or Aujol et al (2005),] which discriminates between texture and noise.
3. The Proposed Method
There are a few works of hybrid inpainting algorithms based on image decomposition (Bertalmio et al 2003,
Harald 2004 and Rane et al 2003). Inspired by Rane et al (2003), which categorized the lost transmission
blocks and filled in separately, we present a novel hybrid inpainting algorithm, which first separates the
damaged region into structure region and texture region. In this paper we use the combination of structure
inpainting and texture synthesis schemes to provide a robust algorithm.
3.1 Image Decomposition
Knowing that texture synthesis algorithms exist to accurately fill in regions of missing texture, and image
inpainting algorithms exist to fill in regions of missing image structure, a method is desired of decomposing a
given image into two sub-images. One sub-image will be a structure image which will be a cartoon-like version
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3. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.1, 2011
of the input image where large-scale edges are preserved but interior regions are smoothed. The other sub-image
will be a texture image which will contain all of the texture information of an image, including noise. These sub-
images can then be reconstructed using image inpainting and texture synthesis techniques.
Various models can be used to decompose images. In this paper, a model in Bertalmio (2006) to Meyer’s
model (2005) is applied to the image u.
The basic model used in the paper is: f = u + v where f is the input image, u is the structural image, and v is the
texture image. In this model, having the structure and texture images allows one to exactly reconstruct the
original image. The end goal of the deconstruction method is to have a very smooth image u which preserves all
the dominant edges in an image but is smooth on interior regions, and an image v which contains all the texture
in an image as well as the noise. These images will then be fed into an inpainting algorithm and a texture
synthesis algorithm respectively. The output of those algorithms can be recombined to obtain the final image.
3.2 Structural Part Inpainting
We apply the algorithm from Zhongyu Xu et al (2008) for structure inpainting. This algorithm revealed the
concepts in manual inpainting, thus got good inpainting results, especially for images with high contrast edges.
The inpainting problem is viewed as a particular case of image interpolation in which we intend to propagate
level lines. Expressing this in terms of local neighborhoods and using a Taylor expansion we derive a third-
order PDE that performs inpainting. This PDE is optimal in the sense that it is the most accurate third-order
PDE which can ensure continuation of level lines. The continuation is strong, allowing the restoration of thin
structures occluded by a wide gap. The result is also contrast invariant.
3.3 Damaged Area Classification
The aim of this part is to reduce the pixels to be synthesized and reduce the region to search for similar texture
blocks. We use algorithm developed by Zhongyu Xu et al (2008) for texture segmentation, and there are few
restrictions on which algorithm to adopt. Let us have a brief illustration.
A pixel (i, j) is called row maximum if u(i, j-1)<u(i, j)>u(i, j+1), and also there exists concepts of row
minimum, column maximum and column minimum, which are called row extremua and column extremua. A
pixel is called a local extremum if it is a row extremum and also a column extremum. Define the coarseness of a
pixel as the percentage of local extremua in the neighborhood. A pixel with proper coarseness is classified into
texture region, while high coarseness corresponds to noise and low coarseness to flat area.
3.4 Improved Exemplar Based Texture Synthesis
The exemplar-based inpainting algorithm consists mainly of three iterative steps, until all pixels in the inpainted
region are filled. The region to be filled, i.e., the target region is indicated by Ω, and its contour is denoted ∂Ω.
The contour evolves inward as the algorithm progresses, and so we also refer to it as the “fill front.” The source
region which remains fixed throughout the algorithm, provides samples used in the filling process. In order to
find the most similar patch in the source region to the target patch, we search the whole source image to find the
best fit. The similarity is measured by computing the sum of squared distance in color between each
corresponding pixel in the two patches.
We adopt the notations similar to that used in inpainting literature. The region to be filled, i.e., the inpainting
domain, is indicated by Ω, and its boundary is denoted δΩ. The source region, Φ, which remains fixed
throughout the algorithm, provides samples used in the filling process. The similarity measure based only on
color is insufficient to propagate accurate linear structures into the target region, and leads to garbage growing.
So, we add to this distance function a new term G representing image gradient as an additional similarity metric.
G = G(Ψp ) − G(Ψq )
Where G is the gradient value for each pixel in the two considering patches. Hence, the similarity function now
depends on the difference between the patches according to two criteria, the difference in color and in gradient
values. The details of the algorithm implementation is as follows,
3.4.1 Computing Patch Priorities
Given a patch Ψ p centered at the point p for some p ε ∂ Ω , its priority P (p) is defined as the product of two
terms P( p) = C( p)D( p) . C(p) is the confidence term and D (p) is the data term, and they are defined as
⊥
∑ε −
q Ψp ∩Ω
C(q) ∇ I p .n p
follows: C( p) = and D ( p ) = Where Ψp is the area of Ψ p ,α is a
ΨP α
normalization factor (e.g., α = 255 for a typical grey-level image), and n p is a unit vector orthogonal to the
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4. Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5758 (print) ISSN 2224-896X (online)
Vol 1, No.1, 2011
front δΩ in the point p. The priority is computed for every border patch, with distinct patches for each pixel on
the boundary of the target region. The patch with the highest priority is the target to fill.
3.4.2. Propagating Structure and Texture Information
Search the source region to find the patch which is most similar to Ψ ∧
. Formally,
p
Ψq∧ = argminΨqεΩ d (Ψp∧, Ψq ) . The distance d ( Ψ a , Ψ b ) between two generic patches
Ψ a and Ψ b is simply defined as the sum of squared differences (SSD) of the already filled pixels in the two
i=A
patches. d = ∑(I ai − I bi )2 + (Gai − Gbi )2 Where, G presents the image gradient vector, I is the RGB
i =1
color vector, D is the distance (the larger d is, the less similar they are), and A is the known pixels number in
Ψp∧ . Having found the source exemplar Ψq ∧ the value of each pixel to be filled is copied from its
corresponding position.
3.4.3. Updating Confidence Values
The confidence C (p) is updated in the area delimited by Ψ p ∧ , as follows:
c(q) = c( p ∧ )∀qεΨp∧ ∩ Ω As filling proceeds, confidence values decay, indicating that we are less sure of
the color values of pixels near the center of the target region. In texture synthesis the problem is that given a
small patch containing texture we wish to fill a much larger region with a texture that is visually similar to the
texture patch. From a probabilistic viewpoint the problem is that given a region that has some stationary
distribution we wish to synthesize additional samples from this distribution.
4. Implementation and Experimental Results
Performance of the proposed inpainting method is demonstrated by some visual examples. We compare the
obtained results with the ones obtained by the methods in which the image is not decomposed, and just one
algorithm (either structure inpainting method or texture synthesis technique) is applied. Also, a comparison
between our method and the proposed approach by Bertalmio et al (2003) is made, as the proposed algorithm
considers simultaneous structure and texture inpainting.
We have used the parameters in the proposed approach unchanged for all the examples considered here. We
applied the image decomposition method with the number of numerical steps equal to 5, and for each step we
computed the estimates using 30 iterations. The value of λ and µ were set to 0.03 and 0.01, respectively.
In the texture synthesis algorithm, the size of template window Ψ is set to 9×9 pixels. This size should be
slightly larger than the largest distinguishable texture element or “texel”, in the source region (we defined this
size to be two times the texel size). During initialization, the function C ( p ) was valued to be C ( p) = 0 ,
∀ P εΩ , and C ( p) = 1 , ∀ p ε f − Ω .
Fig. 1-3 shows the results obtained by our method in comparison with the proposed methods by Bertalmio et al
(2003) and Marcelo Bertalmio (2009).
Experiments are carried out on the various natural scene images. Each input image is shown with inpainting
domain. These regions are highlighted in black color. We found out that the restored images look plausible in
general. It is obvious that the structure inpainting methods tend to blur the inpainted image, while the texture
synthesis techniques fail to reconstruct areas having additional geometric information, as being observed in Fig.
2
The presented results, clearly demonstrate that the algorithm introduced in this paper succeeds effectively in
inpainting large regions from images that consists of textures and surrounded by distinctive image structure. The
execution time required for the inpainting process depends on the size of the image and the regions to be
inpainted, and it ranges from few seconds to several minutes for large images.
5. Conclusion
We presented a new approach by combining structure inpainting method and texture synthesis technique in a
decomposition framework. The combination of these two powerful approaches enables us to simultaneously
recover texture and structure information. We demonstrated the quality of the results obtained by our method on
a variety of defected input images and compared them to the results obtained by several other methods. In all
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Vol 1, No.1, 2011
cases, we could verify higher quality for inpainted images using our approach.
References
Bertalmio et al (2000) “Image inpainting,” in Proceedings of ACM SIGGRAPH Conference on Computer
Graphics, pp. 417-424.
Drori et al (2003),” Fragment Based Image Completion” In Proceedings of ACM SIGGRAPH, ACM
Press.
Chan et al (2002) “Euler’s elastica and curvature based inpainting”, SIAM Journal of Applied
Mathematics, vol. 63, no. 2, pp. 564-592.
Chan and Shen (2001), “Mathematical models for local non-texture inpainting,” SIAM Journal of Applied
Mathematics, vol. 62, no. 3, pp. 1019-1043.
Efros and T. Leung (1999), “Texture synthesis by non-parametric sampling,” in Proc. Int. Conf Comp.
Vision, , pp.1033–1038
Criminisi et al (2004), “Region filling and object removal by exemplar- based image inpainting,”
IEEE Trans. Image Process., vol. 13, pp. 1200–1212.
Wong and Orchard (2008), “A non local-means approach to exemplar based inpainting,” Presented at the
IEEE Int. Conf,Image Processing.
G. T. N. Komodakis (2007), “Image completion using efficient belief propagation via priority scheduling
and dynamic pruning,” IEEE Trans. Image Process., vol. 16, pp. 2649– 2661.
Bertalmio et al (2003) “Simultaneous texture and structure image inpainting,” IEEE Trans. Image
Processing, vol. 12, no.8, pp. 882-889.
Harald (2004), “A combined PDE and texture synthesis approach to inpainting,” Proc. 8th European
Conference on Computer Vision, Prague, Czech Republic, vol. 2, pp. 214-224.
Rane et al (2003)“Structure and texture filling-in of missing image blocks in wireless transmission and
compression applications,” IEEE Trans. Image Processing, vol. 12, no.3, pp. 296- 303.
M. Bertalmio et al (2001) “Navier-Stokes, fluid dynamics and image and video inpainting,” Proc, of
IEEE-CVPR, pp. 355–362,
Masnou and Morel (1998), “Level lines based disocclusion,” in Proc, IEEE-ICIP, pp .259–263.
Chan and Shen (2001), “Nontexture inpainting by curvature driven diffusions,” Journal of Visual
communication and Image Representation, vol. 12, no. 4, pp. 436–449.
Mumford and Shah (1989) “Optimal approximations by piecewise smooth functions and associated
Variational problems,” Comm. Pure Appl. Math., vol. 42, no. 5, pp. 577–685.
Esedoglu and Shen (2002), “Digital inpainting based on the Mumford-Shah-Euler image model,”
European Journal of Applied Mathematics, vol. 13, no. 4, pp, 353–370.
Brennan (2007), “Simultaneous structure and texture image inpainting,” Department of Computer
Engineering, University of California at Santa Cruz, EE264 Rep.
Zhang Hong-bin, Wang Jia-wen (2007),”Image In Painting by Integrating Structure and Texture
Features”, Journal of Beijing University of Technology, vol.33(8):864-869.
Rudin et al(1992), “Nonlinear total variation based noise removal algorithms,” Physica D, vol. 60, no. 1-4,
pp. 259–268.
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Vol 1, No.1, 2011
Meyer (2002), “Oscillating Patterns in Image Processing and Nonlinear Evolution Equations,”
University Lecture Series Volume 22, AMS.
Aujol et al (2005), “Image decomposition into a bounded variation component and an oscillating
component,” Journal of Mathematical Imaging Vision, vol.22: 71–88.
Marcelo Bertalmío (2006),“Strong-continuation, Contrast-invariant inpainting with a Third-order optimal
PDE”, IEEE Transactions on Image Processing, VOL. 15, NO.7.
Zhongyu Xu et al (2008),”Image Inpainting Algorithm Based on Partial Differential Equation’,
International Colloquium on Computing, Communication, Control, and Management.
Ji-Ying Wu and Qiu-Qi Ruan (2009), “A Novel Exemplar-Based Image Completion Model” , Journal of
Information Science And Engineering 25, 481-497.
Aurelie Bugeau, Marcelo Bertalmıo (2009), “Combining Texture Synthesis and Diffusion for Image
Inpainting”, Proceedings of the 4th Inter. Conf. on Computer Vision Theory and Applications.
Fig. 1-3 shows the results obtained by our method in comparison with the proposed methods in [9] and [25].
Each figure has (a) original image (b) image with occlusion part (c) Result of [9] (d) result of [25] (e) result of
our method.
Fig. 1
(a) (b) (c) (d) (e)
(a) (b) (c) (d) (e)
(a) (b) (c) (d) (e)
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