MICHELLE GAYLE NEWMAN
Psychologist in State College, PA
License number
Pennsylvania PS008386L
Category
Psychology
Type
Psychologist
Address
Address
State College, PA 16803
Personal information
See more information about MICHELLE GAYLE NEWMAN at radaris.comName
Address
Phone
4904 Morgantown Rd, Point Marion, PA 15474
405 W Shadow Ln, State College, PA 16803
356 Powell Rd, Springfield, PA 19064
511 Payne Hill Rd APT 105, Clairton, PA 15025
5134 Penn St, Philadelphia, PA 19124
Professional information
Professor Of Psychology And Psychiatry At Penn State University
Position:
Professor of Psychology and Psychiatry at Penn State University
Location:
State College, Pennsylvania Area
Industry:
Research
Work:
Penn State University
since Jul 2012
-
Professor of Psychology and Psychiatry
Education:
State University of New York at Stony Brook 1984 - 1992
Doctor of Philosophy (Ph.D.), Clinical Psychology University of Massachusetts, Amherst 1977 - 1981
Bachelor of Applied Science (B.A.Sc.), Psychology
Doctor of Philosophy (Ph.D.), Clinical Psychology University of Massachusetts, Amherst 1977 - 1981
Bachelor of Applied Science (B.A.Sc.), Psychology
Automatically Computing Emotions Aroused From Images Through Shape Modeling
US Patent:
2014004, Feb 20, 2014
Filed:
Aug 9, 2013
Appl. No.:
13/963039
Inventors:
Xin Lu - State College PA, US
Poonam Suryanarayan - San Francisco CA, US
Jia Li - State College PA, US
Michelle Newman - State College PA, US
Poonam Suryanarayan - San Francisco CA, US
Jia Li - State College PA, US
Michelle Newman - State College PA, US
Assignee:
THE PENN STATE RESEARCH FOUNDATION - University Park PA
International Classification:
G06K 9/00, G06T 11/20
US Classification:
345441
Abstract:
Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.