PmWiki.Research History
Hide minor edits - Show changes to output
Changed line 26 from:
%rfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/research/cm/mbike1.png |
to:
%rfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/ny2.png |
Changed lines 18-19 from:
to:
Changed lines 25-28 from:
to:
%rfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/research/cm/mbike1.png |
Added lines 28-30:
I am also interested in giving computers the ability to "see" just as humans do. A computer should be able to know where it is and what surrounds it just by looking. Today, cameras are ubiquitous but we lack the computational algorithms to process the images into more useful representations.
Changed lines 4-7 from:
!Selected Projects
to:
Changed lines 25-26 from:
to:
\\
\\
\\
Changed line 20 from:
%rfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/research/flash/camera.png |
to:
%rfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/research/flash/camera3.png |
Changed lines 20-27 from:
[[<<]]
%block rframe
to:
%rfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/research/flash/camera.png |
Changed lines 22-23 from:
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of images in several applications that extend the capabilities of conventional cameras.
to:
Computational Photography is an area at the convergence of Computer Graphics and Vision. My co-authors and I have used low-level statistical models of images in several applications that extend the capabilities of conventional cameras.
Changed line 8 from:
%lfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/poster2.png |
to:
%rfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/poster2.png |
Added line 18:
Changed lines 8-12 from:
%block rframe%http://cs.nyu.edu/~fergus/ssl.png%%
%block rframe%http://cs.nyu.edu/~fergus/tinyimages.jpg%%\\
\\
\\
\\
\\
to:
%lfloat text-align=center margin-top=5px margin-right=25px margin-bottom=5px margin-left=25px% http://cs.nyu.edu/~fergus/poster2.png |
Added lines 11-20:
The Internet
is an incredibly rich resource of information that I am interested in
using in conjunction with object recognition algorithms. The challenge
is to design powerful algorithms that can scale to the billions of
images on the web. My co-authors and I have explored methods that rely
on massive amounts of data, as well as more traditional parametric
models.
is an incredibly rich resource of information that I am interested in
using in conjunction with object recognition algorithms. The challenge
is to design powerful algorithms that can scale to the billions of
images on the web. My co-authors and I have explored methods that rely
on massive amounts of data, as well as more traditional parametric
models.
Added line 28:
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of images in several applications that extend the capabilities of conventional cameras.
Deleted line 2:
Changed lines 5-12 from:
to:
!Selected Projects
%block rframe%http://cs.nyu.edu/~fergus/ssl.png%%
%block rframe%http://cs.nyu.edu/~fergus/tinyimages.jpg%%\\
\\
\\
Changed lines 15-16 from:
to:
[[<<]]
%block rframe%http://cs.nyu.edu/~fergus/deblur-tn.png%%\\
%block rframe%http://cs.nyu.edu/~fergus/dark2.jpg%%\\
%block rframe%http://cs.nyu.edu/~fergus/deblur-tn.png%%\\
%block rframe%http://cs.nyu.edu/~fergus/dark2.jpg%%\\
Changed lines 22-23 from:
to:
[[<<]]
Deleted lines 27-31:
One of my main areas of research is Object Recognition. Here the goal is to give computers the ability to "see" just as humans do. A computer should be able to know where it is and what surrounds it just by looking. Today, cameras are ubiquitous but we lack the computational algorithms to process the images into more useful representations.
I have focused on the problem of recognizing object categories. While there are now viable methods for finding specific objects (e.g. a can of Coke) in images, the more general problem of finding categories of objects (e.g. all cans of soda) is harder. My co-authors and I have proposed various probabilistic representations which can be used in conjunction with machine learning methods to learn object models from a set of images containing the desired class of object. This model can then be used to recognize instances of the class in novel images.
Changed lines 9-10 from:
The Internet is an incredibly rich resource of information that I am interested in using in conjunction with object recognition algorithms.
to:
The Internet is an incredibly rich resource of information that I am interested in using in conjunction with object recognition algorithms. The challenge is to design powerful algorithms that can scale to the billions of images on the web. My co-authors and I have explored methods that rely on massive amounts of data, as well as more traditional parametric models.
Changed line 12 from:
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of image gradients in two applications that extend the capabilities of conventional cameras.
to:
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of images in several applications that extend the capabilities of conventional cameras.
Changed lines 7-13 from:
to:
!! [[PmWiki/InternetVision|Leveraging the Internet for Object Recognition]]
The Internet is an incredibly rich resource of information that I am interested in using in conjunction with object recognition algorithms.
!! [[PmWiki/Comp Photo|Computational Photography]]
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of image gradients in two applications that extend the capabilities of conventional cameras.
The Internet is an incredibly rich resource of information that I am interested in using in conjunction with object recognition algorithms.
!! [[PmWiki/Comp Photo|Computational Photography]]
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of image gradients in two applications that extend the capabilities of conventional cameras.
Deleted lines 19-27:
The Internet is an incredibly rich resource of information that I am interested in using in conjunction with object recognition algorithms. One possibility is to use Internet image search engines to provide a diverse set of images from which object category models may be trained. In turn, these models may be applied to collections of images from the Internet or elsewhere, enabling search by visual content (known as content-based image retrieval, CBIR), rather than the text-based searches that are currently employed. Link
Another, more recent direction of investigation, is in "brute force" approaches to recognition, using 100's of millions of images gathered from the web. By using overwhelming amounts of data, very simple algorithms can perform surprisingly well. Link
!! [[PmWiki/Comp Photo|Computational Photography]]
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of image gradients in two applications that extend the capabilities of conventional cameras.
Changed line 8 from:
!! Object Recognition Link
to:
!! [[http://www.cs.nyu.edu/~fergus/research/cm/constellation_model.html|Object Recognition]]
Added lines 1-22:
!Research Overview
I am interested in modeling the statistics of images, from high-level representations of scenes and objects to low-level cues such as image gradients. Such models may be used for a range of applications within Computer Vision and Computational Photography. Below are brief descriptions of my research, along with links to pages giving more details.
!! Object Recognition Link
One of my main areas of research is Object Recognition. Here the goal is to give computers the ability to "see" just as humans do. A computer should be able to know where it is and what surrounds it just by looking. Today, cameras are ubiquitous but we lack the computational algorithms to process the images into more useful representations.
I have focused on the problem of recognizing object categories. While there are now viable methods for finding specific objects (e.g. a can of Coke) in images, the more general problem of finding categories of objects (e.g. all cans of soda) is harder. My co-authors and I have proposed various probabilistic representations which can be used in conjunction with machine learning methods to learn object models from a set of images containing the desired class of object. This model can then be used to recognize instances of the class in novel images.
!! Leveraging the Internet for Object Recognition
The Internet is an incredibly rich resource of information that I am interested in using in conjunction with object recognition algorithms. One possibility is to use Internet image search engines to provide a diverse set of images from which object category models may be trained. In turn, these models may be applied to collections of images from the Internet or elsewhere, enabling search by visual content (known as content-based image retrieval, CBIR), rather than the text-based searches that are currently employed. Link
Another, more recent direction of investigation, is in "brute force" approaches to recognition, using 100's of millions of images gathered from the web. By using overwhelming amounts of data, very simple algorithms can perform surprisingly well. Link
!! [[PmWiki/Comp Photo|Computational Photography]]
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of image gradients in two applications that extend the capabilities of conventional cameras.
I am interested in modeling the statistics of images, from high-level representations of scenes and objects to low-level cues such as image gradients. Such models may be used for a range of applications within Computer Vision and Computational Photography. Below are brief descriptions of my research, along with links to pages giving more details.
!! Object Recognition Link
One of my main areas of research is Object Recognition. Here the goal is to give computers the ability to "see" just as humans do. A computer should be able to know where it is and what surrounds it just by looking. Today, cameras are ubiquitous but we lack the computational algorithms to process the images into more useful representations.
I have focused on the problem of recognizing object categories. While there are now viable methods for finding specific objects (e.g. a can of Coke) in images, the more general problem of finding categories of objects (e.g. all cans of soda) is harder. My co-authors and I have proposed various probabilistic representations which can be used in conjunction with machine learning methods to learn object models from a set of images containing the desired class of object. This model can then be used to recognize instances of the class in novel images.
!! Leveraging the Internet for Object Recognition
The Internet is an incredibly rich resource of information that I am interested in using in conjunction with object recognition algorithms. One possibility is to use Internet image search engines to provide a diverse set of images from which object category models may be trained. In turn, these models may be applied to collections of images from the Internet or elsewhere, enabling search by visual content (known as content-based image retrieval, CBIR), rather than the text-based searches that are currently employed. Link
Another, more recent direction of investigation, is in "brute force" approaches to recognition, using 100's of millions of images gathered from the web. By using overwhelming amounts of data, very simple algorithms can perform surprisingly well. Link
!! [[PmWiki/Comp Photo|Computational Photography]]
Computational Photography is an area at the convergence of Computer Graphics and Vision. Through the use of computation, its goal is to overcome the limitations of traditional cameras, producing a richer, more informative representation of the visual world. My co-authors and I have used low-level statistical models of image gradients in two applications that extend the capabilities of conventional cameras.