Robust Phase-Correlation based Registration of Airborne Videos using Motion Estimation

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Learning to Find Unpaired Cross-Spectral Correspondences

Seungryong Kim

We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration

David Andrew Barry, Ulrich Lemmin, Daniel Sage, Abolfazl Irani Rahaghi

A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Plat ...

2019

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Bugra Tekin

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

EPFL2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

Vision-based detection of aircrafts and UAVs

Artem Rozantsev

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

EPFL2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

EPFL2017

Graph-based compression of dynamic 3D point cloud sequences

Pascal Frossard, Ntorina Thanou

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of th ...

Institute of Electrical and Electronics Engineers2016

Encoder-Driven Inpainting Strategy in Multiview Video Compression

Pascal Frossard, Thomas Maugey, Yu Gao

In free viewpoint video systems, where a user has the freedom to select a virtual view from which an observation image of the 3D scene is rendered, the scene is commonly represented by texture and depth images from multiple nearby viewpoints. In such repre ...

Institute of Electrical and Electronics Engineers2016

SHAPE: Linear-Time Camera Pose Estimation With Quadratic Error-Decay

Martin Vetterli, Adam James Scholefield, Alireza Ghasemi

We propose a novel camera pose estimation or perspective-n-point (PnP) algorithm, based on the idea of consistency regions and half-space intersections. Our algorithm has linear time-complexity and a squared reconstruction error that decreases at least qua ...

Ieee2016

Motion Vector Estimation of Textureless Objects Exploiting Reaction-Diffusion Cellular Automata

Alexandre Schmid, Kazuyoshi Ishimura

Conventional motion estimation algorithms extract motion vectors from image sequences based on the image's local-brightness differences in consecutive images. Therefore, motion vectors are extracted along the moving edges formed by moving objects over thei ...

Old City Publishing Inc2016