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ChaLearn Looking at People: Inpainting and Denoising challenges

2021-06-24 · Sergio Escalera, Marti Soler, Stephane Ayache, Umut Guclu, Jun Wan, Meysam Madadi, Xavier Baro, Hugo Jair Escalante, Isabelle Guyon

Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined.

📄 PDF Abstract BibTeX arXiv:2106.13071

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Tasks

Denoising Pose Estimation

Methods 이 논문이 사용한 방법론

Inpainting Train a convolutional neural network to generate the contents of an arbitrary image region conditioned on its surroundings.

ChaLearn Looking at People: A Review of Events and Resources

2017-01-10 · Sergio Escalera, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon

This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Sin…

Gesture Recognition

ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016

2016-12-19 · 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2016 12 · Sergio Escalera, Mercedes Torres Torres, Brais Martínez, Xavier Baró 외

We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, address…

Age Estimation Gender Classification Gender Prediction General Classification

FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks

2018-12-26 · Sukesh Adiga V, Jayanthi Sivaswamy

Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extractio…

Denoising Image Denoising

U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

2018-07-29 · Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang, Ding Liu 외

This paper studies the challenging problem of fingerprint image denoising and inpainting. To tackle the challenge of suppressing complicated artifacts (blur, brightness, contrast, elastic transformation, occlusion, scrat…

Denoising Image Denoising SSIM

Deep End-to-end Fingerprint Denoising and Inpainting

2018-07-31 · Youness Mansar

This work describes our winning solution for the Chalearn LAP In-painting Competition Track 3 - Fingerprint Denoising and In-painting. The objective of this competition is to reduce noise, remove the background pattern a…

Denoising