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| October 18
| October 18
| Ashish Shrivastava
| Ashish Shrivastava
|
| Dictionary learning methods for computer vision
|-
|-
| October 25
| October 25
In the latter half, I will discuss the future works as well as open research questions related in the field of computer vision.
In the latter half, I will discuss the future works as well as open research questions related in the field of computer vision.
===TBA===
===Dictionary learning methods for computer vision===
Speaker: [http://www.umiacs.umd.edu/~ashish/ Ashish Shrivastava] -- Date: October 18, 2012
Speaker: [http://www.umiacs.umd.edu/~ashish/ Ashish Shrivastava] -- Date: October 18, 2012
Sparse and redundant signal representations have recently gained much interest in image understanding. This is partly due to the fact that signals or images of interest are often sparse in some dictionary. These dictionaries can be either analytic or they can be learned directly from the data. In fact, it has been observed that learning a dictionary directly from data often leads to improved results in many practical applications such as classification and restoration. In this talk I will give a general overview of dictionary learning methods and talk in detail about my recent work on semi-supervised dictionary learning and non-linear supervised dictionary learning methods.
===Dictionary-based Face Recognition from Video===
===Dictionary-based Face Recognition from Video===
