Computes locations and descriptors for space-time interest
points in video. The detector is based on the extension of Harris
operator to space-time as described in "On Space-Time Interest
Points", I.Laptev, IJCV 2005. The code does not implement scale
selection, instead interest points are detected at multiple spatial
and temporal scales. The implemented descriptors HOG (Histograms of
Oriented Gradients) and HOF (Histograms of Optical Flow) are computed
for 3D video patches in the neighbourhood of detected STIPs. This
detector and descriptors have been successfully used for action
recognition in the paper "Learning Realistic Human Actions from
Movies", Ivan Laptev, Marcin Marszalek, Cordelia Schmid and Benjamin
Rozenfeld; in Proc. CVPR'08.
SPAMS (SPArse Modeling Software) is an optimization toolbox composed of a set of binaries implementing algorithms to address various machine learning and signal processing problems involving a large number of small/medium size sparse decompositions.
Our multi-view stereopsis PMVS software was
developed in collaboration with Y. Furukawa at the University of
Illinois at Urbana-Champaign (Furukawa and
Ponce, 2007) and is publicly available for academics. Licensing
negociations with several companies are under
Resampling Penalization is a family of model selection procedure by
penalization that can use any exchangeable weighted bootstrap resampling scheme to compute a penalty. It is
properly defined in the general framework and extensively studied for histogram selection in regression (see journal paper
). This software is a Matlab package for performing Resampling Penalization for several examples of
weights in the histogram selection case.
The Resampling Penalization package is provided free for
non-commercial use under the terms of the GNU
General Public License.
A Matlab package to remove non-uniform blur due to camera shake from a single image, as described in (Whyte et al., 2010
This code aligns historical paintings of Pompeii to a 3D model
constructed from photographs, as described in:
B. C. Russell, J. Sivic, J. Ponce, and H. Dessales.
Automatic Alignment of Paintings and Photographs Depicting a 3D Scene.
3rd International IEEE Workshop on 3D Representation for Recognition (3dRR-11),
associated with ICCV 2011.
This time-lapse videos from YouTube provide a rich source of common human-object interactions, including
more than 400,000 frames obtained from 146 time-lapse videos of challenging and realistic indoor scenes.
This dataset was used in the paper "Scene semantics from long-term observation of people", Vincent Delaitre, David F. Fouhey, Ivan Laptev, Josef Sivic, Abhinav Gupta, Alexei Efros; In Proc. ECCV 2012.
Include a collection of monocular time lapse sequences collected from YouTube and a dataset of still images of indoor scenes.
This dataset was used in single-view 3D scene understanding, as described in the paper "People Watching: Human Actions as a Cue for Single-View Geometry", David F. Fouhey, Vincent Delaitre, Abhinav Gupta, Alexei Efros, Ivan Laptev, Josef Sivic; In Proc. ECCV 2012.
This dataset was used in the paper "Track to the Future: Spatio-temporal Video Segmentation with Long-range Motion Cues", Jose Lezama, Karteek Alahari, Josef Sivic, Ivan Laptev; In Proc. CVPR 2011.
This annotated data set contains ground truth labels of face tracks of six different movies.
The tracks are labeled with gender (female/male) and age (youth/not youth).
This dataset was used in the paper "Semi-supervised learning of facial attributes in video", Neva Cherniavsky, Ivan Laptev, Josef Sivic and Andrew Zisserman; In Parts and Attributes Workshop, ECCV 2010.
A dataset for human action classification in still images. Action classes are Interacting with computer, Photographing, Playing Instrument, Riding Bike, Riding Horse, Running, Walking
This dataset was used in the paper "Recognizing human actions in still images: a study of bag-of-features and part-based representations", V. Delaitre, I. Laptev and J. Sivic; In Proc. BMVC 2010.
Include 15 scene categories, 3D object recognition stereo dataset, 3D photography dataset, visual hull datasets, birds, butterflies, object recognition dataset, texture dataset, and video sequences.