Worth Reading

I decided to clean up my closet, so here comes the paper which I read a month ago:

Piotr Dollar, Behavior Recognition via Sparse Spatio-Temporal Features
comment: extend LoG to 3D video sequence, computing derivative through 2 spatial and 1 temporal dimension, find salient cubes. His toobox actually has a set of descriptors, worth playing.

Ahmed Elgammal, Inferring 3D Body Pose from Silhouettes using Activity Manifold Learning.
comment: an interesting idea. Find the manifold where continuous human motion lie and try to learn the 'inverse-projection' from this lower-dimension space to higher 3D projection.
However, there should be some fundamental problems: how to detect and solve the self-crossings?

David Beymer, Image Representations for Visual Learning.
comment: a pioneering work which learning the non-linear corresponding between 3D and 2D spaces of object model and images, which actually gives idea to Elgammal's paper.

David Heckerman, A Tutorial on Learning With Bayesian Networks
comment: as the topic says, a good tutorial on LEARNING with Bayesian Networks, not INFERRING. For inferring with Bayesian Networks, you have to look at other tutorials (Bishop, 2006, a very good book).

Michael Isard, CONDENSATION-conditional density propagation for visual tracking.
comment: A must read paper and tutorial for CONDENSATION

Dick de Ridder, Locally linear embedding for classification.
comment: A quite readable tutorial and tech-report for LLE

Sam T. Roweis, Nonlinear Dimensionality Reduction by Locally Linear Embedding.
comment: original Science paper for LLE

Joshua B. Tenenbaum, A Global Geometric Framework for Nonlinear Dimensionality Reduction.
comment: original Science paper for ISOMAP. published back to back with Sam's LLE paper.

David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints.
comment: original SIFT paper.

W.R. GILKS, Adaptive Rejection Sampling for Gibbs Sampling
comment: a clever modifcation of Gibbs sampling algorithm that bounds the sampling region adaptively to improve efficiency, quite interesting.

Weiming Hu, A Survey on Visual Surveillance of Object Motion and Behaviors.
comment: extensive survey.

Aaron F. Bobick, The Recognition of Human Movement Using Temporal Templates.
comment: original paper for Spatial-Temporal templates.

Haibin Ling, Diffusion Distance for Histogram Comparison.
comment: interesting algorithm to compare histograms using heat diffusion process simulation, which is quite reasonable.

Yossi Rubner, The Earth Mover's Distance as a Metric for Image Retrieval.
comment: EMD for histogram comparing, see Haibin Ling's paper.

Jianbo Shi, Good Feature to Track.
comment: very old tech report on Feature Detection.

Shivani Agarwal: Learning to Detect Objects in Images via a Sparse, Part-Based Representation.
comment: using bag-of-words strategy to detect objects. coocurrance and spatial information are encoded to train a SNOW classifier.