DEPLOYING COGNITION INTO MEDICAL IMAGING SYSTEMS
By including a cognitive model to a conventional
machine vision system it can inherit the following:
Semantic knowledge abstraction and understanding
Automatic Interpretation of in-evident data and can
reason out of it.
Linguistic representation of acquired pattern
knowledge out of the image.
Curbs of current Vision Systems
Cognitive Vision the new Paradigm
Devising Cognitive Vision
A cognitive vision system can engage in purposive goal-directed
behaviour, adapting to unforeseen changes of the visual
environment, and it can anticipate the occurrence of objects or
The tendency to find links and relations among objects and images.
The tendency to identify a symmetry in an image.
The tendency to perceive a perfect image from partial information
The tendency to use a semantic label to denote an image.
The tendency to classify similar images into a group.
The tendency to identify common meta-figures in an image.
The tendency to be sensitive on borders, changing points, or differences.
Image is converted into grayscale
Image Enhancement using Gaussian filter for
After this normalization, the image will be fairly flat, limited to noise
and blurring in the shadowed region as well as the jaggiest and
The classification method used to distinguish between the emotions.
All these approaches have focused on classifying the six universal
Classifiers are concerned with finding the optimal hyper-plane that
separates the classes in the feature space. The optimal hyper plane
means finding the maximum margin between the classes.
Some commonly used classifiers are:
Support Vector Machines