Speakers |
Syllabus |
Titles
& Abstracts |
|
Shape
Context, Shape
Distance, Feature Correspondence, Deformable Templates, Thin Plate
Spline |
|
|
Graphical
Models, Approximate Inference |
|
|
Medical
images, registration, (level set) segmentation, PDF estimation,
feature detection using the monogenic signal, shape representation
for tumour growth, decision support, cellular processes |
|
Sanjiv
Kumar
Google Research
Carnegie Mellon University, USA |
Graphical
models, Discriminative methods, Image classification, Image context,
Markov Random Field, Conditional Random Field, Discriminative Random
Field |
|
|
Supervised
and Unsupervised learning, object recognition, invariant representations,
deep belief networks, energy-based models, feature hierarchies |
|
|
Scale
and affine invariant keypoint detectors, Image description, Object
recognition, Image matching, Image classification |
|
|
Gruph
Cuts, Markov Random Fields,
Video Segmentation, OBJCUT,
Applications in Computer Vision |
|
|
Gist,
The role of Context, Scene understanding |
|
| Advanced
Research Seminars |
Speakers |
Syllabus |
Titles
& Abstracts |
Ken-ichi
Maeda
Toshiba, Japan |
Subspace
Method, Mutual Subspace Method, Canonical Angle, Face Recognition,
Character Recognition
|
|
|
Object
categorization, Spatial reasoning, Shape representation, Correlograms,
3D modeling
|
|
Speakers |
Syllabus |
Related
Papers and Talks |
|
Markov
random fields, segmentation, motion
How
to read
|
|
| Laboratory,
Implementation Details, Demo, Resources |
Speakers |
Syllabus |
Titles
& Abstracts |
|
OpenCV,
Datasets and resources |
|
|
Efficient
Object Oriented Programming for Vision, Effective Library Development,
Easy GUIs |
|
|
Classification,
Support Vector Machine, Boosting, Textonboost, Discriminative Learning.
|
|
|
Recognition
with templates, Vision for HCI |
|
|
Video Segmentation, Learning and Inference, Matlab, Netlab, |
|
|
