Presentation is loading. Please wait.

Presentation is loading. Please wait.

Image Inpainting Using Pre-Trained Classification CNN

Published byBeverley Fowler Modified over 6 years ago

Similar presentations

Presentation on theme: "Image Inpainting Using Pre-Trained Classification CNN"— Presentation transcript:

1 Image Inpainting Using Pre-Trained Classification CNN
By - Yaniv Kerzhner & Adar Elad Supervisor - Dr. Yaniv Romano

2 The process of reconstructing lost parts in a given image
The Problem - Image Inpainting The process of reconstructing lost parts in a given image M This project aims to apply convolutional neural networks (CNN) for solving the inpainting problem Our prime goal - Investigate whether a pre-trained CNN classifier (for digits/faces) has memorized the data in order to fill it in Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

3 CNN – Convolutional Neural Networks
Input Output Lena Bengio Abbel Hinton Le-Cun Lin 1% 90% 2% Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

4 The straight-forward solution
Given a set of training pairs (original and corrupted images), train a network in a supervised fashion Neural Network ℒ , Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

5 The straight-forward solution
Given a set of training pairs (original and corrupted images), train a network in a supervised fashion Nian Cai. et. al. (2015) J. Xie et. al. (2012) D. Pathak et. al. (2016) Raymond A, et. al. (2017) Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

6 The Proposed Method Pre-trained CNN car house lizard tree
Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

7 The Proposed Method Pre-trained CNN car house lizard tree
Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

8 The Proposed Method Pre-trained CNN car house lizard tree
Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

9 The Proposed Method Pre-trained CNN car house lizard tree
Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

10 The Proposed Method Pre-trained CNN car house lizard tree
Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

11 The Proposed Method The Proposed Method Adar Elad & Yaniv Kerzhner
The Computer-Science Department The Technion

12 Results – MNIST dataset
original Masked Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Our Result Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

13 Results – Yale Extended B dataset
original Masked Our Result Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

14 90% of the image is missing !!
Results – ImageNet dataset 90% of the image is missing !! Masked Our Result Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source. Adar Elad & Yaniv Kerzhner The Computer-Science Department The Technion

15 Results – Special Cases
Hallucination - The specific case of image inpainting where all the data is missing 3 5 8 Illumination - Many face images in the dataset were taken in the dark, the question then arises: could these images be illuminated with the assistant of our algorithm? input It is possible to distinguish that all the details of the original image still remains after the inpainting. Meaning, all we did is to add information to the dark parts. our result Suggested Interaction Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source.

16 Time to Conclude Time to Conclude Suggested Interaction
Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source.

17 Time to Conclude Suggested Interaction
Timing: After showing the entire content of this slide Question: Will this algorithm operate well on radar (SAR) images (show such images)? Problem Type: Checkbox Problem with answers YES or NO, and explain Problem Type: Checkbox Problem with answers YES or NO, and explain in a discussion Then show the answer – No, because the model does not match this data source.

Download ppt "Image Inpainting Using Pre-Trained Classification CNN"

Similar presentations

World of work How is science used in the world of work? Science in the world of work.

World of work How is science used in the world of work? Science in the world of work.
1 Image Classification MSc Image Processing Assignment March 2003.

1 Image Classification MSc Image Processing Assignment March 2003.
Unsupervised Learning With Neural Nets Deep Learning and Neural Nets Spring 2015.

Unsupervised Learning With Neural Nets Deep Learning and Neural Nets Spring 2015.
1 Automated Feature Abstraction of the fMRI Signal using Neural Network Clustering Techniques Stefan Niculescu and Tom Mitchell Siemens Medical Solutions,

1 Automated Feature Abstraction of the fMRI Signal using Neural Network Clustering Techniques Stefan Niculescu and Tom Mitchell Siemens Medical Solutions,
Spatial Pyramid Pooling in Deep Convolutional

Spatial Pyramid Pooling in Deep Convolutional
Image Denoising and Inpainting with Deep Neural Networks Junyuan Xie, Linli Xu, Enhong Chen School of Computer Science and Technology University of Science.

Image Denoising and Inpainting with Deep Neural Networks Junyuan Xie, Linli Xu, Enhong Chen School of Computer Science and Technology University of Science.
CS532 Neural Networks Dr. Anwar Majid Mirza Lecture No. 3 Week2, January 22 nd, 2008 National University of Computer and Emerging.

CS532 Neural Networks Dr. Anwar Majid Mirza Lecture No. 3 Week2, January 22 nd, 2008 National University of Computer and Emerging.
Mining Discriminative Components With Low-Rank and Sparsity Constraints for Face Recognition Qiang Zhang, Baoxin Li Computer Science and Engineering Arizona.

Mining Discriminative Components With Low-Rank and Sparsity Constraints for Face Recognition Qiang Zhang, Baoxin Li Computer Science and Engineering Arizona.
Hurieh Khalajzadeh Mohammad Mansouri Mohammad Teshnehlab

Hurieh Khalajzadeh Mohammad Mansouri Mohammad Teshnehlab
KYLE PATTERSON Automatic Age Estimation and Interactive Museum Exhibits Advisors: Prof. Cass and Prof. Lawson.

KYLE PATTERSON Automatic Age Estimation and Interactive Museum Exhibits Advisors: Prof. Cass and Prof. Lawson.
Representations for object class recognition David Lowe Department of Computer Science University of British Columbia Vancouver, Canada Sept. 21, 2006.

Representations for object class recognition David Lowe Department of Computer Science University of British Columbia Vancouver, Canada Sept. 21, 2006.
School of Engineering and Computer Science Victoria University of Wellington Copyright: Peter Andreae, VUW Image Recognition COMP 112 2014 # 18.

School of Engineering and Computer Science Victoria University of Wellington Copyright: Peter Andreae, VUW Image Recognition COMP # 18.
Multiple Instance Learning for Sparse Positive Bags Razvan C. Bunescu Machine Learning Group Department of Computer Sciences University of Texas at Austin.

Multiple Instance Learning for Sparse Positive Bags Razvan C. Bunescu Machine Learning Group Department of Computer Sciences University of Texas at Austin.
Image Decomposition, Inpainting, and Impulse Noise Removal by Sparse & Redundant Representations Michael Elad The Computer Science Department The Technion.

Image Decomposition, Inpainting, and Impulse Noise Removal by Sparse & Redundant Representations Michael Elad The Computer Science Department The Technion.
Mentor Prof. Amitabha Mukerjee Deepak Pathak Kaustubh Tapi 10346

Mentor Prof. Amitabha Mukerjee Deepak Pathak Kaustubh Tapi 10346
Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.

Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.
Convolutional Restricted Boltzmann Machines for Feature Learning Mohammad Norouzi Advisor: Dr. Greg Mori Simon Fraser University 27 Nov 2009 1.

Convolutional Restricted Boltzmann Machines for Feature Learning Mohammad Norouzi Advisor: Dr. Greg Mori Simon Fraser University 27 Nov
Object Recognizing. Deep Learning Success in 2012 DeepNet and speech processing.

Object Recognizing. Deep Learning Success in 2012 DeepNet and speech processing.
Logan Lebanoff Mentor: Haroon Idrees

Logan Lebanoff Mentor: Haroon Idrees
Parsing Natural Scenes and Natural Language with Recursive Neural Networks INTERNATIONAL CONFERENCE ON MACHINE LEARNING (ICML 2011) RICHARD SOCHER CLIFF.

Parsing Natural Scenes and Natural Language with Recursive Neural Networks INTERNATIONAL CONFERENCE ON MACHINE LEARNING (ICML 2011) RICHARD SOCHER CLIFF.

Similar presentations

Ads by Google