| Title: |
Bayesian perception and representation of visual motion [electronic resource] / Eero Simoncelli. |
| Author(s)/Name(s): |
Simoncelli, Eero. |
| Publisher: |
[Bethesda, Md. : National Institutes of Health, 2008] |
| Related Names: |
National Institutes of Health (U.S.) |
| Series: |
NIH neuroscience seminar series |
| Language: |
eng |
| Electronic Links: |
http://videocast.nih.gov/launch.asp?14347 |
| MeSH Subjects: |
Motion Perception |
|
Models, Neurological |
| Summary: |
(CIT): Dr Simoncelli’s research addresses a variety of basic issues in the analysis and representation of visual images. The work is interdisciplinary, spanning computational neuroscience, image processing, and computer vision. It is motivated by three broad goals: 1) construction of mathematical theories for the representation of visual information, including statistical models of visual images. 2) development of functional models for biological visual processing. Recent developments include a new functional model that unifies the properties of direction selective neurons in two visual areas (V1 and MT) and 3) creation of novel algorithms for image processing and computer vision applications. NIH Neuroscience Seminar Series. |
| Notes: |
Title from title screen (viewed July 2, 2008). |
|
Streaming video (1 hr., 11 min., 41 sec. : sd., col.). |
|
Mode of access: World Wide Web. |
|
Open-captioned. |
| NLM Unique ID: |
101470349 |
| Other ID Numbers: |
(DNLM)CIT:14347 |
