1. Computer Vision Scene Classification Using Neural Nets and a Knowledge Base Daniel Vevang

2. Object Detection

3. Object Detection Training

4. Object Detection Positive Samples Object Detection Training

5. Object Detection Positive SamplesNegative Samples Object Detection Training

6. Object Detection Positive SamplesNegative SamplesVector Data Object Detection Training

7. Object Detection Positive SamplesNegative SamplesVector Data XML Haarcascade tree Object Detection Training

8. Object Detection Positive SamplesNegative SamplesVector Data XML Haarcascade tree OpenCV Output: Object location and scale from an image. Object Detection Training

9. Scene Detection Object Detection Data: location and scale

10. Scene Detection Object Detection Data: location and scale Kohonen Network Scene Detection

11. Object Detection Data: location and scale Kohonen Network Scene Detection NN Training Input and Output Data

12. Scene Detection Object Detection Data: location and scale Kohonen Network Scene Detection NN Training Input and Output Data Trained Kohonen Net

13. Scene Detection Object Detection Data: location and scale Kohonen Network Scene Detection NN Training Input and Output Data Trained Kohonen NetKnowledge Base

14. Tools: OpenCV  Diverse set of computer vision tools

15. Objectmarker  GUI for Creating a text file of bounding box coordinates for a database of images  Additional scripting tools for creating haar xml cascades.  Eyepatch: Advanced scripting tool for training object detectors.  Warning! Stability Issues!  GUI for Creating a text file of bounding box coordinates for a database of images  Additional scripting tools for creating haar xml cascades.  Eyepatch: Advanced scripting tool for training object detectors.  Warning! Stability Issues!

16. Kohonen Net Implementation  Code modified from Karsten Kutsa  Still in the process of creating the data model for Neural Net input.  Currently looking to create 8 input nodes for each image (8*5 images) for 40 images total.  Code modified from Karsten Kutsa  Still in the process of creating the data model for Neural Net input.  Currently looking to create 8 input nodes for each image (8*5 images) for 40 images total.

17. Kohenen Net Implementation for detected images A-E Example input ABCDE 1.00.0 1.00.0 1.00.0 1.00.0 1.0 0.0

18. Parameters to work with  Learning rate for Kohonen layer  Learning rate for output layer  Learning rate for step sizes  Smoothing factor for score deltas  Parameter for width of neighborhood  Learning rate for Kohonen layer  Learning rate for output layer  Learning rate for step sizes  Smoothing factor for score deltas  Parameter for width of neighborhood

19. Additional data to consider  x y location  scale of each object  Multiples of the same object  x y location  scale of each object  Multiples of the same object

20. Knowledge base  Possible implementation of Narl to augment the performance of the Neural Net.