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Exploiting Large Image Sets for Road Scene Parsing

Publications associées (65)

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Color Image Segmentation Using a Fuzzy Inference System

Matin Macktoobian

A novel method is proposed in the scope of image segmentation that solves this problem by breaking it into two main blocks. The first block's functionality is a method to anticipate the color basis of each segment in segmented images. One of the challenges ...

IEEE2019

Clinical equivalence assessment of T2 synthesized pediatric brain magnetic resonance imaging

Tobias Kober, Tom Hilbert, Jocelyn Philippe

Background and purpose. - Automated synthetic magnetic resonance imaging (MRI) provides qualitative, weighted image contrasts as well as quantitative information from one scan and is well-suited for various applications such as analysis of white matter dis ...

MASSON EDITEUR2019

Beyond Cartesian Representations for Local Descriptors

Pascal Fua, Kwang Moo Yi, Eduard Trulls Fortuny, Anastasiia Mishchuk, Patrick Winfried Josef Ebel

The dominant approach for learning local patch descriptors relies on small image regions whose scale must be properly estimated a priori by a keypoint detector. In other words, if two patches are not in correspondence, their descriptors will not match. A s ...

IEEE COMPUTER SOC2019

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

Learning to See through Reflections

Sabine Süsstrunk

"Pictures of objects behind a glass are difficult to interpret" "and understand due to the superposition of two real images: a reflection layer and a background layer. Separation of these two layers is challenging due to the ambiguities in as- signing text ...

IEEE2018

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Bin Jin

Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...

EPFL2018

Revealing Information by Averaging

Sabine Süsstrunk, Roger Hersch, Mahmut Sami Arpa

We present a method for hiding images in synthetic videos and reveal them by temporal averaging. The main challenge is to develop a visual masking method that hides the input image both spatially and temporally. Our masking approach consists of temporal an ...

Optical Soc Amer2017

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

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Alexandre Massoud Alahi

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth imag ...

Springer2016

Efficient Transductive Semantic Segmentation

Mathieu Salzmann

Semantically describing the contents of images is one of the classical problems of computer vision. With huge numbers of images being made available daily, there is increasing interest in methods for semantic pixel labelling that exploit large image sets. ...

Ieee2016