Cognitive vision

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Cognitive vision

  1. 1. Cognitive Vision DEPLOYING COGNITION INTO MEDICAL IMAGING SYSTEMS
  2. 2. Objectives  By including a cognitive model to a conventional machine vision system it can inherit the following: • Semantic knowledge abstraction and understanding of data. • Automatic Interpretation of in-evident data and can reason out of it. • Linguistic representation of acquired pattern knowledge out of the image.
  3. 3. Curbs of current Vision Systems  Detection  Localization  Recognition  Understanding
  4. 4. Cognitive Vision the new Paradigm Sensing Processing Control
  5. 5. 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 events. Association The tendency to find links and relations among objects and images. Symmetry The tendency to identify a symmetry in an image. Perfection The tendency to perceive a perfect image from partial information Abstraction The tendency to use a semantic label to denote an image. Categorization The tendency to classify similar images into a group. Analysis The tendency to identify common meta-figures in an image. Appreciation The tendency to be sensitive on borders, changing points, or differences.
  6. 6. Methodology PreProcessing •Noise Supression •Deblurring •Image Enhancement •Edge Detection Data Reduction •Compression •Feature Extraction Segmentation •Texture Segregation •Colour Recognition •Clustering of Feature Space Object Recognition •Template Matching •Feature Based Image Understanding •Scene Analysis •Object Arrangement
  7. 7. PreProcessing  Image is converted into grayscale  Gamma correction  Image Enhancement using Gaussian filter for    Sharpness Low Aliasing After this normalization, the image will be fairly flat, limited to noise and blurring in the shadowed region as well as the jaggiest and aliasing.
  8. 8. Tumor Pose Identification Sequence of Images , Camera Parameters, Static Tumor Geometry Stochastic Filtering Pose Parameters [Rotation , Scale, Translation]
  9. 9. Classifiers  The classification method used to distinguish between the emotions. All these approaches have focused on classifying the six universal emotions  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: 1. Adaboost 2. Support Vector Machines 3. Multilayer Perceptron
  10. 10. Medical Diagnosis System Laser Annealing  Gallbladder removal  Appendectomy  Hernia repair  Colon resection  Cystectomy  Prostatectomy  Laparoscopic-assisted vaginal hysterectomy (LAVH)  Lobectomy  Oophoroectomy (ovary removal)
  11. 11. Cognitive Vision Aided MDS Medical Data Inputs Cognitive Vision Control Inputs from CVS Inverse Kinematics

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