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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

By : Joe Minichino, Joseph Howse
2.1 (7)
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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

2.1 (7)
By: Joe Minichino, Joseph Howse

Overview of this book

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.
Table of Contents (11 chapters)
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6
6. Retrieving Images and Searching Using Image Descriptors
10
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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