September 12
Lecture 1: Course Introduction
Lecture 2: kNN classifier
September 19
Lecture 3: A few Computer Vision Concepts
Lecture 4: Image Representations
Lecture 4: Curse of Dimensionality
September 26
Paper Discussion:
"Recognizing Action at a Distance"
"80 million tiny images: a large dataset for non-parametric object and scene recognition"
October 3
Lecture 6: Linear and Generalized Linear Classifiers
October 10
NO LECTURE due to ECCV 2012
October 17
Paper Discussion:
"Object Recognition with Informative Features and Linear Classification"
"Learning a Classification Model for Segmentation" Video lecture
October 24
Lecture 7: Support Vector Machines
October 31
Paper Discussion:
"Histograms of Oriented Gradients for Human Detection"
"Beyond Bags of Features: Spatial Pyramid Matching
for Recognizing Natural Scene Categories"
November 7
Lecture 8: Bagging and Boosting
November 14
Paper Discussion:
"Rapid Object Detection using a Boosted Cascade of Simple Features"
"Detecting Pedestrians Using Patterns of Motion and Appearance"
November 21
Lecture 9: Cross Validation
Lecture 10: Neural Networks
November 28
Finish Lecture 10
Paper Discussion:
"Image denoising: Can plain Neural Networks compete with BM3D?"
Lecture 11: Unsupervised Learning
December 5
Finish Lecture 11
Paper Discussion:
"Discovering objects and their location in images"
December 11
Student paper presentations from 1:30 to 4 in MC 316
List of Students Presenting
December 13
Student paper presentations from 1:30 to 4 in MC 320
List of Students Presenting
January 9
Student final project presentations from 1:30 to 3:30 in MC 316
List of Students Presenting
January 11
Student final project presentations from 10:30 to 12:30 in MC 300
List of Students Presenting
January 14
Student final project presentations from 1:30 to 3:30 in MC 316
List of Students Presenting