Publication

Learning Separable Filters

Pascal Fua
2012
Reports

Abstract

While learned image features can achieve great accuracy on different Computer Vision problems, their use in real-world situations is still very limited as their extraction is typically time-consuming. We therefore propose a method to learn image features that can be extracted very efficiently using separable filters, by looking for low rank filters. We evaluate our approach on both the image categorization and the pixel classification tasks and show that we obtain similar accuracy as state-of-the-art methods, at a fraction of the computational cost.

Official source

https://infoscience.epfl.ch/entities/publication/02be3118-8c7a-49be-bd76-48795b358524

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Pascal Fua
2012
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/02be3118-8c7a-49be-bd76-48795b358524

About this result

Related concepts (23)

Medical image computing

Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images.

Computer vision

Computer vision tasks include methods for , , and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input to the retina in the human analog) into descriptions of the world that make sense to thought processes and can elicit appropriate action.

Computer stereo vision

Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera. By comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels. This is similar to the biological process of stereopsis. In traditional stereo vision, two cameras, displaced horizontally from one another, are used to obtain two differing views on a scene, in a manner similar to human binocular vision.

Related publications (24)

Learning Separable Filters

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