This presentation deals with examplar-based inpainting. It is based on the following papers:
(i) C. Guillemot and O. Le Meur, Image inpainting: overview and recent advances, IEEE Signal Processing Magazine, Vol. 1, pp. 127-144, 2014.
(ii) O. Le Meur, M. Ebdelli and C. Guillemot, Hierarchical super-resolution-based inpainting, IEEE TIP, vol. 22(10), pp. 3779-3790, 2013.
(iii) O. Le Meur and C. Guillemot, Super-resolution-based inpainting, ECCV 2012.
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.
Region filling and object removal by exemplar based image inpaintingWoonghee Lee
To get rid of (an) object(s) at a picture or to restore a picture from scratches or holes, Criminisi at el. suggested an algorithm which is combied "texture synthesis" and "inpainting". I made the slide to present at a class to introduce this algorithm. I refered a slide http://bit.ly/1Ng7DNt. I wish this slide may help you to understand the algorithm. Thank you.
GRAY SCALE IMAGE SEGMENTATION USING OTSU THRESHOLDING OPTIMAL APPROACHJournal For Research
Image segmentation is often used to distinguish the foreground from the background. Image segmentation is one of the difficult research problems in the machine vision industry and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, the Otsu method, improves the image segmentation effect obviously. It can be implemented by two different approaches: Iteration approach and Custom approach. In this paper both approaches has been implemented on MATLAB and give the comparison of them and show that both has given almost the same threshold value for segmenting image but the custom approach requires less computations. So if this method will be implemented on hardware in an optimized way then custom approach is the best option.
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.
Region filling and object removal by exemplar based image inpaintingWoonghee Lee
To get rid of (an) object(s) at a picture or to restore a picture from scratches or holes, Criminisi at el. suggested an algorithm which is combied "texture synthesis" and "inpainting". I made the slide to present at a class to introduce this algorithm. I refered a slide http://bit.ly/1Ng7DNt. I wish this slide may help you to understand the algorithm. Thank you.
GRAY SCALE IMAGE SEGMENTATION USING OTSU THRESHOLDING OPTIMAL APPROACHJournal For Research
Image segmentation is often used to distinguish the foreground from the background. Image segmentation is one of the difficult research problems in the machine vision industry and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, the Otsu method, improves the image segmentation effect obviously. It can be implemented by two different approaches: Iteration approach and Custom approach. In this paper both approaches has been implemented on MATLAB and give the comparison of them and show that both has given almost the same threshold value for segmenting image but the custom approach requires less computations. So if this method will be implemented on hardware in an optimized way then custom approach is the best option.
Visual attention networks are so pervasive in the human brain that eye movements carry a wealth of information that can be exploited for many purposes. In this paper, we present evidence that information derived from observers' gaze can be used to infer their age. This is the first study showing that simple features extracted from the ordered sequence of fixations and saccades allow us to predict the age of an observer. Eye movements of 101 participants split into 4 age groups (adults, 6-10 year-old, 4-6 year-old and 2 year-old) were recorded while exploring static images. The analysis of observers' gaze provides evidence of age-related differences in viewing patterns. Therefore, we extract from the scanpaths several features, including fixation durations and saccade amplitudes, and learn a direct mapping from those features to age using Gentle AdaBoost classifiers. Experimental results show that the proposed image-blind method succeeds in predicting the age of the observer up to 92% (2y.o. vs adults) of the time.
How saccadic models help predict where we look during a visual task? Applicat...Olivier Le Meur
We present saccadic models which are an alternative way to predict where observers look at. Compared to saliency models, saccadic models generate plausible visual scanpaths from which saliency maps can be computed. In addition these models have the advantage of being adaptable to different viewing conditions, viewing tasks and types of visual scene. We demonstrate that saccadic models perform better than existing saliency models for predicting where an observer looks at in free-viewing condition and quality-task condition (i.e. when observers have to score the quality of an image). For that, the joint distributions of saccade amplitudes and orientations in both conditions (i.e. free-viewing and quality task) have been estimated from eye tracking data. Thanks to saccadic models, we hope we will be able to improve upon the performance of saliency-based quality metrics, and more generally the capacity to predict where we look within visual scenes when performing visual tasks.
Introducing context-dependent and spatially-variant viewing biases in saccadi...Olivier Le Meur
O. Le Meur and A. Coutrot, Introducing context-dependent and spatially-variant viewing biases in saccadic models, Accepted for publication in Vision Research, 2016.
Previous research showed the existence of systematic tendencies in viewing behavior during scene exploration. For instance, saccades are known to follow a positively skewed, long-tailed distribution, and to be more frequently initiated in the horizontal or vertical directions. In this study, we hypothesize that these viewing biases are not universal, but are modulated by the semantic visual category of the stimulus. We show that the joint distribution of saccade amplitudes and orientations significantly varies from one visual category to another. These joint distributions are in addition spatially variant within the scene frame. We demonstrate that a saliency model based on this better understanding of viewing behavioral biases and blind to any visual information outperforms well-established saliency models. We also propose a saccadic model that takes into account classical low-level features and spatially-variant and context-dependent viewing biases.
Color transfer between high-dynamic-range imagesOlivier Le Meur
Color transfer methods alter the look of a source image with regards to a reference image. So far, the proposed color transfer methods have been limited to low-dynamic-range (LDR) images. Unlike LDR images, which are display-dependent, high-dynamic-range (HDR) images contain real physical values of the world luminance and are able to capture high luminance variations and finest details of real world scenes. Therefore, there exists a strong discrepancy between the two types of images. In this paper, we bridge the gap between the color transfer domain and the HDR imagery by introducing HDR extensions to LDR color transfer methods. We tackle the main issues of applying a color transfer between two HDR images. First, to address the nature of light and color distributions in the context of HDR imagery, we carry out modifications of traditional color spaces. Furthermore, we ensure high precision in the quantization of the dynamic range for histogram computations. As image clustering (based on light and colors) proved to be an important aspect of color transfer, we analyze it and adapt it to the HDR domain. Our framework has been applied to several state-of-the-art color transfer methods. Qualitative experiments have shown that results obtained with the proposed adaptation approach exhibit less artifacts and are visually more pleasing than results obtained when straightforwardly applying existing color transfer methods to HDR images.
Style-aware robust color transfer, H. Hristova, O. Le Meur, R. Cozot and K. Bouatouch, Computational Aesthetic, 2015.
Transferring features, such as light and colors, between input and reference images is the main objective of color transfer methods. Current state-of-the-art methods focus mainly on the complete transfer of the light and color distributions. However, they do not successfully grasp specific light and color variations in image styles. In this paper, we propose a local method for carrying out a transfer of style between two images. Our method partitions both images to Gaussian distributed clusters by considering their main style features. These features are automatically determined by the classification step of our algorithm. Moreover, we present several novel policies for input/reference cluster mapping, which have not been tackled so far by previous methods. To complete the style transfer, for each pair of corresponding clusters, we apply a parametric color transfer method and a local chromatic adaptation transform. Results, subjective user evaluation as well as objective evaluation show that the proposed method obtains visually pleasing and artifact-free images, respecting the reference style.
Saccadic model of eye movements for free-viewing conditionOlivier Le Meur
O. Le Meur and Z. Liu, Saccadic model of eye movements for free-viewing condition, Vision Research, 2015.
We propose a new framework to predict visual scanpaths of observers while they freely watch a visual scene. The visual fixations are inferred from bottom-up saliency and several oculomotor biases. Bottom-up saliency is represented by a saliency map whereas the oculomotor biases (saccade amplitudes and saccade orientations) are modeled using public eye tracking datasets. Our experiments show that the simulated scanpaths exhibit similar trends of human eye movements in a free-viewing condition. The generated scanpaths are more similar to human scanpaths than those generated by two existing methods. In addition, we show that computing saliency maps from simulated visual scanpaths allows to outperform existing saliency models.
Methods for comparing scanpaths and saliency maps: strengths and weaknessesOlivier Le Meur
Methods for comparing saliency maps and scanpaths. More details in:
O. Le Meur & T. Baccino, Methods for comparing scanpaths and saliency maps: strengths and weaknesses, Behavior Research Methods (BRM) 2013, http://dx.doi.org/10.3758/s13428-012-0226-9
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Visual attention networks are so pervasive in the human brain that eye movements carry a wealth of information that can be exploited for many purposes. In this paper, we present evidence that information derived from observers' gaze can be used to infer their age. This is the first study showing that simple features extracted from the ordered sequence of fixations and saccades allow us to predict the age of an observer. Eye movements of 101 participants split into 4 age groups (adults, 6-10 year-old, 4-6 year-old and 2 year-old) were recorded while exploring static images. The analysis of observers' gaze provides evidence of age-related differences in viewing patterns. Therefore, we extract from the scanpaths several features, including fixation durations and saccade amplitudes, and learn a direct mapping from those features to age using Gentle AdaBoost classifiers. Experimental results show that the proposed image-blind method succeeds in predicting the age of the observer up to 92% (2y.o. vs adults) of the time.
How saccadic models help predict where we look during a visual task? Applicat...Olivier Le Meur
We present saccadic models which are an alternative way to predict where observers look at. Compared to saliency models, saccadic models generate plausible visual scanpaths from which saliency maps can be computed. In addition these models have the advantage of being adaptable to different viewing conditions, viewing tasks and types of visual scene. We demonstrate that saccadic models perform better than existing saliency models for predicting where an observer looks at in free-viewing condition and quality-task condition (i.e. when observers have to score the quality of an image). For that, the joint distributions of saccade amplitudes and orientations in both conditions (i.e. free-viewing and quality task) have been estimated from eye tracking data. Thanks to saccadic models, we hope we will be able to improve upon the performance of saliency-based quality metrics, and more generally the capacity to predict where we look within visual scenes when performing visual tasks.
Introducing context-dependent and spatially-variant viewing biases in saccadi...Olivier Le Meur
O. Le Meur and A. Coutrot, Introducing context-dependent and spatially-variant viewing biases in saccadic models, Accepted for publication in Vision Research, 2016.
Previous research showed the existence of systematic tendencies in viewing behavior during scene exploration. For instance, saccades are known to follow a positively skewed, long-tailed distribution, and to be more frequently initiated in the horizontal or vertical directions. In this study, we hypothesize that these viewing biases are not universal, but are modulated by the semantic visual category of the stimulus. We show that the joint distribution of saccade amplitudes and orientations significantly varies from one visual category to another. These joint distributions are in addition spatially variant within the scene frame. We demonstrate that a saliency model based on this better understanding of viewing behavioral biases and blind to any visual information outperforms well-established saliency models. We also propose a saccadic model that takes into account classical low-level features and spatially-variant and context-dependent viewing biases.
Color transfer between high-dynamic-range imagesOlivier Le Meur
Color transfer methods alter the look of a source image with regards to a reference image. So far, the proposed color transfer methods have been limited to low-dynamic-range (LDR) images. Unlike LDR images, which are display-dependent, high-dynamic-range (HDR) images contain real physical values of the world luminance and are able to capture high luminance variations and finest details of real world scenes. Therefore, there exists a strong discrepancy between the two types of images. In this paper, we bridge the gap between the color transfer domain and the HDR imagery by introducing HDR extensions to LDR color transfer methods. We tackle the main issues of applying a color transfer between two HDR images. First, to address the nature of light and color distributions in the context of HDR imagery, we carry out modifications of traditional color spaces. Furthermore, we ensure high precision in the quantization of the dynamic range for histogram computations. As image clustering (based on light and colors) proved to be an important aspect of color transfer, we analyze it and adapt it to the HDR domain. Our framework has been applied to several state-of-the-art color transfer methods. Qualitative experiments have shown that results obtained with the proposed adaptation approach exhibit less artifacts and are visually more pleasing than results obtained when straightforwardly applying existing color transfer methods to HDR images.
Style-aware robust color transfer, H. Hristova, O. Le Meur, R. Cozot and K. Bouatouch, Computational Aesthetic, 2015.
Transferring features, such as light and colors, between input and reference images is the main objective of color transfer methods. Current state-of-the-art methods focus mainly on the complete transfer of the light and color distributions. However, they do not successfully grasp specific light and color variations in image styles. In this paper, we propose a local method for carrying out a transfer of style between two images. Our method partitions both images to Gaussian distributed clusters by considering their main style features. These features are automatically determined by the classification step of our algorithm. Moreover, we present several novel policies for input/reference cluster mapping, which have not been tackled so far by previous methods. To complete the style transfer, for each pair of corresponding clusters, we apply a parametric color transfer method and a local chromatic adaptation transform. Results, subjective user evaluation as well as objective evaluation show that the proposed method obtains visually pleasing and artifact-free images, respecting the reference style.
Saccadic model of eye movements for free-viewing conditionOlivier Le Meur
O. Le Meur and Z. Liu, Saccadic model of eye movements for free-viewing condition, Vision Research, 2015.
We propose a new framework to predict visual scanpaths of observers while they freely watch a visual scene. The visual fixations are inferred from bottom-up saliency and several oculomotor biases. Bottom-up saliency is represented by a saliency map whereas the oculomotor biases (saccade amplitudes and saccade orientations) are modeled using public eye tracking datasets. Our experiments show that the simulated scanpaths exhibit similar trends of human eye movements in a free-viewing condition. The generated scanpaths are more similar to human scanpaths than those generated by two existing methods. In addition, we show that computing saliency maps from simulated visual scanpaths allows to outperform existing saliency models.
Methods for comparing scanpaths and saliency maps: strengths and weaknessesOlivier Le Meur
Methods for comparing saliency maps and scanpaths. More details in:
O. Le Meur & T. Baccino, Methods for comparing scanpaths and saliency maps: strengths and weaknesses, Behavior Research Methods (BRM) 2013, http://dx.doi.org/10.3758/s13428-012-0226-9
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
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Examplar-based inpainting
1. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Examplar-based inpainting
Olivier Le Meur
olemeur@irisa.fr
IRISA - University of Rennes 1
June 19, 2014
1 / 44
2. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Inpainting: context and issues (1/3)
This talk is about inpainting. We will heavily rely upon these papers:
C. Guillemot & O. Le Meur, Image inpainting: overview
and recent advances, IEEE Signal Processing Magazine,
Vol. 1, pp. 127-144, 2014.
O. Le Meur, M. Ebdelli and C. Guillemot, Hierarchical
super-resolution-based inpainting, IEEE TIP, vol.
22(10), pp. 3779-3790, 2013.
O. Le Meur & C. Guillemot, Super-resolution-based
inpainting, ECCV 2012.
2 / 44
3. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Inpainting: context and issues (2/3)
Inpainting
Inpainting corresponds to filling holes (i.e. missing areas) in im-
ages (Bertalmio et al., 2000).
Let be an image I defined as
I : Ω ⊂ Rn
−→ Rm
Let be a degradation operator M
M : Ω −→ {0, 1}
M(x) =
0, if x ∈ U
1, otherwise
Let F the observed image:
F = M ◦ I
n = 2 for a 2D image
m = 3 for (R,G,B) image
Ω = S ∪ U,
• S being the known part
of I
• U the unknown part of I
◦ is the Hadamard product
3 / 44
4. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Inpainting: context and issues (3/3)
Different configurations according to the definition of M:
Original image
80% of the pixels
have been
removed.
damaged portions
in black, scratches
object removal
Sparsity and
low-rank methods
Diffusion-based
methods
Examplar-based
methods
4 / 44
5. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Outline of the presentation
1 Inpainting: context and issues
2 Examplar-based inpainting
3 Variants of Criminisi’s method
4 Super-resolution-based inpainting method
5 Results and comparison with existing methods
6 Conclusion
5 / 44
6. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Outline of the presentation
1 Inpainting: context and issues
2 Examplar-based inpainting
Presentation
Notation
Criminisi et al.’s method
3 Variants of Criminisi’s method
4 Super-resolution-based inpainting method
5 Results and comparison with existing methods
6 Conclusion
6 / 44
7. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Examplar-based inpainting (1/4)
Texture synthesis
Examplar-based inpainting methods rely on the assumption that the
known part of the image provides a good dictionary which could be
used efficiently to restore the unknown part (Efros and Leung, 1999).
The recovered texture is therefore
learned from similar regions.
ª This can be done simply by
sampling, copying or
combining patches from the
known part of the image;
Template Matching
ª Patches are then stitched
together to fill in the missing
area.
7 / 44
8. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Examplar-based inpainting (2/4)
Notations:
ª a patch ψpx is a discretized
N × N neighborhood
centered on the pixel px.
This patch can be vectorized
in a raster-scan order as a
mN2
-dimensional vector;
ª ψuk
px
denotes the unknown
pixels of the patch;
ª ψk
px
denotes its known
pixels;
ª ψpx(i)
denotes the ith
nearest neighbour of ψpx ;
ª δU is the front line;
8 / 44
9. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Examplar-based inpainting (3/4)
Criminisi et al.’s algorithm
Criminisi et al. (Criminisi et al., 2004) has brought a new momentum
to inpainting applications and methods. They proposed a new method
based on two sequential stages:
1 Filling order computation;
2 Texture synthesis.
1 Filling order computation: P(px) = C(px) × D(px)
Confidence term
C(px) =
q∈ψk
px
C(q)
|ψpx
|
where |ψpx
| is the area of ψpx
.
Data term
D(px) =
| I⊥
(px) · npx
|
α
where α is a normalization
constant in order to ensure that
D(px) is in the range 0 to 1.
9 / 44
10. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Examplar-based inpainting (4/4)
2 Texture synthesis:
A template matching is performed within a local neighborhood:
py = arg min
q∈W
d(ψk
pq
, ψk
px∗ )
ª W ⊆ S is the window search;
ª ψk
px∗ are the known pixels of the patch ψpx∗ with the highest
priority;
ª ψk
py
are the known pixels of the nearest patch neighbor;
ª d(a, b) is the sum of squared differences between patches a and
b.
The pixels of the patch ψuk
py
are then copied into the unknown pixels
of the patch ψpx∗ .
10 / 44
11. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Outline of the presentation
1 Inpainting: context and issues
2 Examplar-based inpainting
3 Variants of Criminisi’s method
Filling order computation
Texture synthesis
Some examples
Limitations
4 Super-resolution-based inpainting method
5 Results and comparison with existing methods
6 Conclusion
11 / 44
12. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Filling order computation (1/4)
P(px) = C(px) × D(px)
Two variants are here presented:
ª Tensor-based data term (Le Meur et al., 2011);
ª Sparsity-based data term (Xu and Sun, 2010).
Many others: edge-based data term, transformation of the data term
in a nonlinear fashion, entropy-based data term...
12 / 44
13. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Filling order computation (2/4)
Tensor-based data term
Instead of using the gradient, (Le Meur et al., 2011) used the structure
tensor which is more robust:
D(px) = α + (1 − α)exp −
η
(λ1 − λ2)2
where η is a positive value and α ∈ [0, 1].
The structure tensor is a symmetric, positive semi-definite
matrix (Weickert, 1999):
Jρ,σ [I] = Kρ ∗
m
i=1
(Ii ∗ Kσ) (Ii ∗ Kσ)T
where Ka is a Gaussian kernel with a standard deviation a. The
parameters ρ and σ are called integration scale and noise scale,
respectively.
13 / 44
14. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Filling order computation (3/4)
D(px) = α + (1 − α)exp −
η
(λ1 − λ2)2
When λ1 λ2, the data term tends to α. It tends to 1 when
λ1 >> λ2.
14 / 44
15. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Filling order computation (4/4)
Sparsity-based data term
Sparsity-based data term (Xu and Sun, 2010) is based on the sparse-
ness of nonzero patch similarities:
D(px) =
|Ns(px)|
|N(px)|
×
pj ∈Ws
w2
px ,pj
where Ns and N are the numbers of valid and candidate patches in
the search window.
Weight wpx ,pj is proportional to the similarity between the two patches
centered on px and pj ( j wpx ,pj
= 1).
A large value of the structure sparsity term means sparse similarity
with neighboring patches
⇒ a good confidence that the input patch is on some structure.
15 / 44
16. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Texture synthesis (1/4)
Texture synthesis with more than one candidate
From K patches ψpx(i)
which are the most similar to the known part
ψk
px
of the input patch, the unknown part of the patch to be filled ψuk
px
is then obtained by a linear combination of the sub-patches ψuk
px(i)
.
ψuk
px
=
K
i=1
wiψuk
px(i)
How can we compute the weights
wi of this linear combination?
Note: K is locally adjusted by using
an -ball including patches within a
certain radius.
16 / 44
17. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Texture synthesis (2/4)
ψuk
px
=
K
i=1
wiψuk
px(i)
Different solutions exist (Guillemot et al., 2013):
ª Average template matching: wi = 1
K , ∀i;
ª Non-local means approach (Buades et al., 2005):
wi = exp −
d(ψpk
x
, ψpk
x(i)
)
h2
ª Least-square method minimizing
E(w) = ψk
px
− Aw 2
2,a
w∗
= arg min
w
E(w)
17 / 44
18. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Texture synthesis (3/4)
ψuk
px
=
K
i=1
wiψuk
px(i)
ª Constrained Least-square optimization with the sum-to-one
constraint of the weight vector ⇒ LLE method (Saul and
Roweis, 2003)
E(w) = ψk
px
− Aw 2
2,a
w∗
= arg min
w
E(w) s.t. wT
1K = 1
ª Constrained Least-square optimization with positive weights ⇒
NMF method (Lee and Seung, 2001)
w∗
= arg min
w
E(w) s.t. wi ≥ 0
18 / 44
19. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Texture synthesis (4/4)
Similarity metrics:
ª Using a Gaussian weighted Euclidean distance
dL2 (ψpx
, ψpy
) = ψpx
− ψpy
2
2,a
where a controls the decay of the Gaussian function
g(k) = e−
|k|
2a2
, a > 0;
ª A better distance introduced in (Bugeau et al., 2010, Le Meur
and Guillemot, 2012):
d(ψpx , ψpy ) = dL2 (ψpx , ψpy ) × (1 + dH (ψpx , ψpy ))
where dH (ψpx
, ψpy
) is the Hellinger distance
dH (ψpx
, ψpy
) = 1 −
k
p1(k)p2(k)
where p1 and p2 represent the histograms of patches ψpx
, ψpy
,
respectively.
19 / 44
20. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Some Examples (1/2)
Inpainted pictures with (Criminisi et al., 2004)’s method (Courtesy of
P. P´erez):
20 / 44
22. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Limitations
Very sensitive to the parameter settings such as the filling order
and the patch size:
Examplar-based methods are a one-pass greedy algorithms.
22 / 44
23. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Outline of the presentation
1 Inpainting: context and issues
2 Examplar-based inpainting
3 Variants of Criminisi’s method
4 Super-resolution-based inpainting method
Proposed approach
More than one inpainting
Loopy Belief Propagation
Super-resolution
5 Results and comparison with existing methods
6 Conclusion
23 / 44
24. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Proposed approach (1/1)
Objectives of the proposed method
We apply an examplar-based inpainting algorithm several times and
fuse together the inpainted results.
less sensitive to the inpainting setting;
relax the greedy constraint.
The inpainting method is applied on a coarse version of the input
picture:
less demanding of computational resources;
less sensitive to noise;
K candidates for the texture synthesis without introducing blur.
Need to fuse the inpainted images and to retrieve the highest
frequencies
Loopy Belief Propagation and Super-Resolution algorithms.
24 / 44
25. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
More than one inpainting (1/1)
The baseline algorithm is an
examplar-based method:
ª Filling order
computation;
ª Texture synthesis.
ª Decimation factor n = 3
ª 13 sets of parameters
Table: Thirteen inpainting configurations.
Setting Parameters
1
Patch’s size 5 × 5
Decimation factor n = 3
Search window 80 × 80
Sparsity-based filling order
2 default + rotation by 180 degrees
3 default + patch’s size 7 × 7
4
default + rotation by 180 degrees
+ patch’s size 7 × 7
5 default + patch’s size 11 × 11
6
default + rotation by 180 degrees
+ patch’s size 11 × 11
7 default + patch’s size 9 × 9
8
default + rotation by 180 degrees
+ patch’s size 9 × 9
9
default + patch’s size 9 × 9
+ Tensor-based filling order
10
default + patch’s size 7 × 7
+ Tensor-based filling order
11
default + patch’s size 5 × 5
+ Tensor-based filling order
12
default + patch’s size 11 × 11
+ Tensor-based filling order
13
default + rotation by 180 degrees
+ patch’s size 9 × 9
+ Tensor-based filling order
25 / 44
26. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Loopy Belief Propagation (1/5)
. . . . . .
Loopy Belief Propagation is used to fuse together the 13 inpainted
images.
Let be a finite set of labels L composed of M = 13 values.
E(l) =
p∈ν
Vd(lp) + λ
(n,m)∈N4
Vs(ln, lm)
where, lp the label of pixel px, ν represents the pixel in U and N4 is
a neighbourhood system. λ is a weighting factor.
26 / 44
27. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Loopy Belief Propagation (2/5)
E(l) =
p∈ν
Vd(lp) + λ
(n,m)∈N4
Vs(ln, lm)
ª Vd(lp) represents the cost of assigning a label lp to a pixel px:
Vd(lp) =
n∈L u∈υ
I(l)
(x + u) − I(n)
(x + u)
2
ª Vs(ln, lm) is the discontinuity cost:
Vs(ln, lm) = (ln − lm)
2
The minimization is performed iteratively (less than 15
iterations) (Boykov and Kolmogorov, 2004, Boykov et al., 2001,
Yedidia et al., 2005).
27 / 44
28. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Loopy Belief Propagation (3/5)
LBP convergence:
ª 13 inpainted image in
input;
ª 25 iterations;
ª resolution=80 × 120.
28 / 44
29. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Loopy Belief Propagation (4/5)
LBP convergence:
ª 13 inpainted image in
input;
ª 25 iterations;
ª resolution=120 × 80.
29 / 44
30. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Loopy Belief Propagation (5/5)
LBP convergence:
ª 13 inpainted image in
input;
ª 25 iterations;
ª resolution=200 × 135.
30 / 44
31. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Super-resolution (1/2)
For the LR patch corresponding
to the HR patch having the
highest priority:
ª We look for its best
neighbour;
ª Only the best candidate is
kept;
ª The corresponding HR
patch is simply deduced.
ª Its pixel values are then
copied into the unknown
parts of the current HR
patch.
31 / 44
32. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Super-resolution (2/2)
To speed-up the process, we can perform the
search:
ª within a search window;
ª within a dictionary (as illustrated on the
right) composed of LR patches with
their corresponding HR patches.
32 / 44
33. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Outline of the presentation
1 Inpainting: context and issues
2 Examplar-based inpainting
3 Variants of Criminisi’s method
4 Super-resolution-based inpainting method
5 Results and comparison with existing methods
Results
Comparison with existing methods
6 Conclusion
33 / 44
34. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Results (1/4)
34 / 44
35. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Results (2/4)
35 / 44
36. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Results (3/4)
36 / 44
37. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Results (4/4)
37 / 44
38. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Comparison with existing methods (1/5)
Three methods have been tested:
ª [Komodakis] N. Komodakis, and G. Tziritas, Image Completion
using Global Optimization. in CVPR 2007 (Komodakis and
Tziritas, 2007);
ª [Pritch] Y. Pritch, E. Kav-Venaki, S. Peleg, Shift-Map Image
Editing. in ICCV 2009 (Pritch et al., 2009);
ª [He] K. He and J. Sun, Statistics of Patch Offsets for Image
Completion. in ECCV 2012 (He and Sun, 2012).
38 / 44
39. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Comparison with existing methods (2/5)
From left to right: Komodakis, Pritch, He, Ours.
39 / 44
40. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Comparison with existing methods (3/5)
From left to right: Komodakis, Pritch, He, Ours.
40 / 44
41. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Comparison with existing methods (4/5)
Much more results on the link:
http://people.irisa.fr/Olivier.Le_Meur/publi/2013_TIP/
indexSoA.html
41 / 44
42. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Comparison with existing methods (5/5)
Limitations and failure cases:
From left to right: original, He’s method and proposed one.
ª No semantic information are used...
ª No objective quality metric.
42 / 44
43. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Outline of the presentation
1 Inpainting: context and issues
2 Examplar-based inpainting
3 Variants of Criminisi’s method
4 Super-resolution-based inpainting method
5 Results and comparison with existing methods
6 Conclusion
43 / 44
44. Examplar-based
inpainting
O. Le Meur
Inpainting:
context and
issues
Examplar-based
inpainting
Presentation
Notation
Criminisi et al.’s
method
Variants of
Criminisi’s
method
Filling order
computation
Texture synthesis
Some examples
Limitations
Super-resolution-
based inpainting
method
Proposed approach
More than one
inpainting
Loopy Belief
Propagation
Super-resolution
Conclusion
ª A new framework to perform inpainting of still color pictures:
coarse inpainting + super-resolution.
Binary file could be downloaded:
http://people.irisa.fr/Olivier.Le_Meur/publi/2013_
TIP/index.html
ª A natural extension is to deal with video inpainting.
A paper dealing with video inpainting under revision in IEEE TIP.
44 / 44
45. Examplar-based
inpainting
O. Le Meur
References
References
M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester. Image inpainting. In SIGGRPAH 2000, 2000.
Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.
IEEE Trans. On PAMI, 26(9):1124–1137, 2004.
Y. Boykov, O. Veksler, and R.Zabih. Efficient approximate energy minimization via graph cuts. IEEE Trans. On PAMI, 20(12):
1222–1239, 2001.
A. Buades, B. Coll, and J.M. Morel. A non local algorithm for image denoising. In IEEE Computer Vision and Pattern Recognition
(CVPR), volume 2, pages 60–65, 2005.
A. Bugeau, M. Bertalm´ıo, V. Caselles, and G. Sapiro. A comprehensive framework for image inpainting. IEEE Trans. on Image
Processing, 19(10):2634–2644, 2010.
A. Criminisi, P. P´erez, and K. Toyama. Region filling and object removal by examplar-based image inpainting. IEEE Trans. On
Image Processing, 13:1200–1212, 2004.
A. A. Efros and T. K. Leung. Texture synthesis by non-parametric sampling. In IEEE Computer Vision and Pattern Recognition
(CVPR), pages 1033–1038, 1999.
C. Guillemot, M. Turkan, O. Le Meur, and M. Ebdelli. Object removal and loss concealment using neigbor embedding methods.
Signal processing: image communication, 28:1405–1419, 2013.
K. He and J. Sun. Statistics of patch offsets for image completion. In ECCV, 2012.
N. Komodakis and G. Tziritas. Image completion using efficient belief propagation via priority scheduling and dynamic pruning.
IEEE Trans. On Image Processing, 16(11):2649 – 2661, 2007.
O. Le Meur and C. Guillemot. Super-resolution-based inpainting. In ECCV, pages 554–567, 2012.
O. Le Meur, J. Gautier, and C. Guillemot. Examplar-based inpainting based on local geometry. In ICIP, 2011.
D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In In NIPS, pages 556–562. MIT Press, 2001.
Y. Pritch, E. Kav-Venaki, and S. Peleg. Shift-map image editing. In ICCV’09, pages 151–158, Kyoto, Sept 2009.
L.K. Saul and S.T. Roweis. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine
Learning Research, 4:119–155, 2003.
J. Weickert. Coherence-enhancing diffusion filtering. International Journal of Computer Vision, 32:111–127, 1999.
Z. Xu and J. Sun. Image inpainting by patch propagation using patch sparsity. IEEE Trans. on Image Processing, 19(5):
1153–1165, 2010.
J.S. Yedidia, W.T. Freeman, and Y. Weiss. Constructing free energy approximations and generalized belief propagation algorithms.
IEEE Transactions on Information Theory, 51:2282–2312, 2005.
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