When it comes to image processing and machine learning, it's important to understand how computers represent and interpret visual data. In this post, we'll explore how images are represented as arrays of numerical values, and how these arrays can be used as inputs to neural network models.
To represent a digital image, computers use a 2D array of pixel values. Each pixel in the image is represented by a single value, which corresponds to the intensity or color of that pixel. For example, a grayscale image might use a single value to represent the intensity of each pixel, with lower values representing darker pixels and higher values representing lighter pixels. A color image, on the other hand, might use 3 values (one for each color channel: red, green, and blue) to represent the color of each pixel.
To use an image as input to a neural network model, we need to convert the 2D array of pixel values into a 1D array. This is often done using a process called flattening, where the 2D array is "unrolled" into a single, long list of pixel values.
Once the image has been flattened, it can be fed into the neural network model as a single input. The model will then process this input, using its learned patterns and relationships to make predictions or decisions based on the content of the image.
So, how do neural network models "see" and understand images? Essentially, they learn to recognize patterns and features in the pixel values, using these patterns to make predictions or decisions. For example, a model trained to classify images of animals might learn to recognize the shape of ears, noses, and paws in order to differentiate between different types of animals.
In summary, digital images are represented as arrays of pixel values, which can be flattened and used as input to neural network models. These models "see" images by learning to recognize patterns and features in the pixel values, using these patterns to make predictions or decisions.
I hope this post has provided you with some useful and educational information on how computers "see".
