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Learning Separable Filters

Publications associées (40)

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SLIC Superpixels Compared to State-of-the-art Superpixel Methods

Pascal Fua, Sabine Süsstrunk, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi, Appu Shaji

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically c ...

Institute of Electrical and Electronics Engineers2012

Geodesic Active Fields

Dominique Zosso

Image registration is the concept of mapping homologous points in a pair of images. In other words, one is looking for an underlying deformation field that matches one image to a target image. The spectrum of applications of image registration is extremely ...

EPFL2011

Stereo Vision Matching using Characteristics Vectors

Jean-Philippe Thiran

Stereo vision is a usual method to obtain depth information from images. The problems encountered when applying the majority of well established algorithms to provide this information are due to the high computational load required. This occurs in both the ...

2010

DAISY: A Fast Descriptor for Dense Wide Baseline Stereo and Multiview Reconstruction

Engin Tola

Stereo reconstruction is a fundamental problem of computer vision. It has been studied for more than three decades and significant progress has been made in recent years as evidenced by the quality of the models now being produced. This is highly related w ...

EPFL2010

SLIC Superpixels

Pascal Fua, Sabine Süsstrunk, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi, Appu Shaji

Superpixels are becoming increasingly popular for use in computer vision applications. However, there are few algorithms that output a desired number of regular, compact superpixels with a low computational overhead. We introduce a novel algorithm that clu ...

2010

Dense deformation field estimation for atlas registration using the active contour framework

Valérie Duay

A key research area in computer vision is image segmentation. Image segmentation aims at extracting objects of interest in images or video sequences. These objects contain relevant information for a given application. For example, a video surveillance appl ...

EPFL2008

Towards Fully Automatic Image Segmentation Evaluation

Touradj Ebrahimi, Lutz Goldmann, Péter Vajda

Spatial region (image) segmentation is a fundamental step for many computer vision applications. Although many methods have been proposed, less work has been done in developing suitable evaluation methodologies for comparing different approaches. The main ...

Springer2008

Stability and Convergence of the Level Set Method in Computer Vision

Kunal Narayan Chaudhury

Several computer vision problems, like segmentation, tracking and shape modeling, are increasingly being solved using level set methodologies. But the critical issues of stability and convergence have always been neglected in most of the level set implemen ...

Elsevier2007

Unsupervised multiscale image segmentation

Ana Dimitrijevic

In the early stages of visual information processing one of the most fundamental and complex task is segmentation. This process is used to divide images or image sequences into meaningful regions or objects. Such a representation is definitely needed to go ...

EPFL2006

New support vector algorithms for multicategorical data applied to real-time object recognition

Silvio Borer

Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithm ...

EPFL2003