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Learning OpenCV 3 Computer Vision with Python (Update)

You're reading from   Learning OpenCV 3 Computer Vision with Python (Update) Unleash the power of computer vision with Python using OpenCV

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781785283840
Length 266 pages
Edition 1st Edition
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Table of Contents (11) Chapters Close

Preface 1. Setting Up OpenCV FREE CHAPTER 2. Handling Files, Cameras, and GUIs 3. Processing Images with OpenCV 3 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Detecting and Recognizing Objects 8. Tracking Objects 9. Neural Networks with OpenCV – an Introduction Index

Possible improvements and potential applications

We have illustrated how to build an ANN, feed it training data, and use it for classification. There are a number of aspects we can improve, depending on the task at hand, and a number of potential applications of our new-found knowledge.

Improvements

There are a number of improvements that can be applied to this approach, some of which we have already discussed:

  • For example, you could enlarge your dataset and iterate more times, until a performance peak is reached
  • You could also experiment with the several activation functions (cv2.ml.ANN_MLP_SIGMOID_SYM is not the only one; there is also cv2.ml.ANN_MLP_IDENTITY and cv2.ml.ANN_MLP_GAUSSIAN)
  • You could utilize different training flags (cv2.ml.ANN_MLP_UPDATE_WEIGHTS, cv2.ml.ANN_MLP_NO_INPUT_SCALE, cv2.ml.ANN_MLP_NO_OUTPUT_SCALE), and training methods (back propagation or resilient back propagation)

Aside from that, bear in mind one of the mantras of software development: there is no single best technology...

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