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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

The structure of an ANN


Here's a visual representation of a neural network:

As you can see from the figure, there are three distinct layers in a neural network: Input layer, Hidden layer (or middle), and Output layer.

There can be more than one hidden layer; however, one hidden layer would be enough to resolve the majority of real-life problems.

Network layers by example

How do we determine the network's topology, and how many neurons to create for each layer? Let's make this determination layer by layer.

The input layer

The input layer defines the number of inputs into the network. For example, let's say you want to create an ANN, which will help you determine what animal you're looking at given a description of its attributes. Let's fix these attributes to weight, length, and teeth. That's a set of three attributes; our network will need to contain three input nodes.

The output layer

The output layer is equal to the number of classes we identified. Continuing with the preceding example of an animal...

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