DocumentCode
2443487
Title
SHOSLIF: a framework for object recognition from images
Author
Weng, John
Author_Institution
Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA
Volume
7
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
4204
Abstract
A new framework called self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) is introduced for recognizing and segmenting real-world objects from images. It addresses critical problems in real-world recognition including visual attention, feature representation efficiency, shape variation in unsegmented data (including size, position and orientation), decision optimality, and geometric inference
Keywords
image segmentation; inference mechanisms; learning (artificial intelligence); neural nets; object recognition; self-adjusting systems; SHOSLIF; decision optimality; feature representation efficiency; geometric inference; image recognition; image segmentation; object recognition; self-organizing hierarchical optimal subspace learning; shape variation; subspace inference; visual attention; Backpropagation; Computer science; Face detection; Humans; Image recognition; Image segmentation; Neural networks; Object recognition; Pattern recognition; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374940
Filename
374940
Link To Document