Saliency Detection Using Regression Trees on Hierarchical Image Segments

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TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation

Pascal Fua, Mateusz Kozinski, Leonardo Citraro, Subeesh Vasu

Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal t ...

2020

Learning Vision-based Flight in Drone Swarms by Imitation

Dario Floreano, Fabrizio Schiano, Fabian Maximilian Schilling, Julien Jean Denis Nicolas Lecoeur

Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation l ...

2019

Capsule networks explain complex spatial processing

Michael Herzog, Adrien Christophe Doerig

Classically, visual processing is described as a cascade of local feedforward computations. This view has received striking support from the success of convolutional neural networks (CNNs). However, CNNs only roughly mimic human vision. For example, CNNs d ...

SAGE PUBLICATIONS LTD2019

New Approaches to Artistic Synthesis Relying on Visual Perception

Mahmut Sami Arpa

Artists often take advantage of the limitations of the human visual system to create art that provides new experiences for the observers. Similarly, in this thesis, we explore new computational artistic compositions that create new visual experiences by re ...

EPFL2019

Beyond Bouma's window: How to explain global aspects of crowding?

Michael Herzog, Gregory Francis, Adrien Christophe Doerig, Alban Bornet, Aaron Michael Clarke

In crowding, perception of an object deteriorates in the presence of nearby elements. Although crowding is a ubiquitous phenomenon, since elements are rarely seen in isolation, to date there exists no consensus on how to model it. Previous experiments show ...

2019

Using the Neurorobotics platform to explain global processing in visual crowding

Michael Herzog, Gregory Francis, Alban Bornet

The Neurorobotics Platform of the Human Brain Project hosts many different large-scale models that can easily be connected with each other. Here, we linked a deep neural network for saliency computation to a spiking cortical model for visual segmentation ( ...

2018

Crowding asymmetries explained by a model of image segmentation

Michael Herzog, Gregory Francis, Adrien Christophe Doerig, Alban Bornet

In crowding, perception of a target deteriorates when neighboring flankers are presented. Flankers close to the fovea deteriorate performance less strongly than flankers presented peripherally, the well-known crowding asymmetries. For example, we presented ...

2017

Multimodal person recognition in audio-visual streams

Do Hoang Nam Le

Multimedia databases are growing rapidly in size in the digital age. To increase the value of these data and to enhance the user experience, there is a need to make these videos searchable through automatic indexing. Because people appearing and talking in ...

EPFL2019

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

Pascal Fua, Mathieu Salzmann, Carlos Joaquin Becker, Róger Bermúdez Chacón, Okan Altingövde

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences—motifs that are visually similar across different acquisitions—to infer changes on the parame ...

Institute of Electrical and Electronics Engineers2020

An Accelerator for High Efficient Vision Processing

Paolo Ienne, Robert Fasthuber

In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency a ...

Ieee-Inst Electrical Electronics Engineers Inc2017