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From Image-level to Pixel-level Labeling with Convolutional Networks

Related publications (121)

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Weakly Supervised Object Segmentation with 004 dwaeConvolutional Neural Networks

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Can a machine learn how to segment different objects in real world images without having any prior knowledge about the delineation of the classes? In this paper, we demonstrate that this task is indeed possible. We address the problem by training a Convolu ...

Idiap2014

On Rendering Synthetic Images for Training an Object Detector

Pascal Fua, Vincent Lepetit, Artem Rozantsev

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of ...

2014

Incorporating Near-Infrared into Scene Understanding

Neda Salamati

Recent progress in computational photography has shown that we can acquire near-infrared (NIR) information in addition to the normal visible (RGB) information with only slight modification to the standard digital camera. In this thesis, we study if this ex ...

EPFL2013

Learning Context Cues for Synapse Segmentation

Pascal Fua, Graham Knott, Carlos Joaquin Becker, Karim Ali

We present a new approach for the automated segmentation of synapses in image stacks acquired by Electron Microscopy (EM) that relies on image features specifically designed to take spatial context into account. These features are used to train a classifie ...

Institute of Electrical and Electronics Engineers2013

Acquiring functional object knowledge through motor imagery?

Michiel van Elk

A widely investigated question in the research on the acquisition of novel functional object representations is the role of the action system. Whereas most studies so far have investigated the role of active action training on the acquisition of object rep ...

2012

Finding Objects of Interest in Images using Saliency and Superpixels

Radhakrishna Achanta

The ability to automatically find objects of interest in images is useful in the areas of compression, indexing and retrieval, re-targeting, and so on. There are two classes of such algorithms – those that find any object of interest with no prior knowledg ...

EPFL2011

Deformable shape models for 2D object segmentation

Robin Thandiackal

Given a set of images showing individual 2D instances of an object class, the goal is to learn object class deformation in 2D for segmentation automatically. Class deformation is modelled by linear combinations of basis shapes. Usually, given segmentation ...

2011

Extended Keyframe Detection with Stable Tracking for Multiple 3D Object Tracking

Vincent Lepetit

We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale well ...

Institute of Electrical and Electronics Engineers2011

Conditional Random Fields for Multi-Camera Object Detection

Pascal Fua, Horesh Beny Ben Shitrit, Xavier Boix Bosch, Gemma Roig Noguera

We formulate a model for multi-class object detection in a multi-camera environment. From our knowledge, this is the first time that this problem is addressed taken into account different object classes simultaneously. Given several images of the scene tak ...

2011

Material-Based Object Segmentation Using Near-Infrared Information

Sabine Süsstrunk, Neda Salamati

We present a framework to incorporate near-infrared (NIR) information into algorithms to better segment objects by isolating material boundaries from color and shadow edges. Most segmentation algorithms assign individual regions to parts of the object that ...

2010