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How Deep Learning is Breaking the Limitations of Image Processing

Recent advancements in the deep learning based technology are image and speech recognition. Deep learning is a subset of machine learning that is good at recognizing the patterns but requires a large amount of training data. Deep learning has found the application in recognizing the objects in the images.

The neural network is a computational model that works in the same way as the neurons in the human brain. Each neuron takes the input, performs the operations and then passes the output to the next neuron. A deep neural network combines the multiple nonlinear processing layers using the simple elements operating in parallel.

We can train the neural network with the help of training data. Each data layer in the network takes in data from the previous layer transforms it and then passes it to the next level. The network increases the complexity and the detail of what it learned from layer to layer. Here are a few examples of deep learning:

    • It is used in the self-driving cars
    • Used in ATMs to reject a counterfeit banknote.
    • Used in smartphones for the instant translation of the street sign in a foreign language.

Deep learning is suitable for the identification applications like face recognition, text translation, voice recognition etc. A deep neural network combines multiple nonlinear processing layers, several hidden layers, and an output layer. All the layers are interconnected via nodes or the neurons with each hidden node using the output of the previous layer as its input.

Machine Learning for Images

Computers are able to perform the computations on the numbers and are able to interpret the images in the same way as humans do. But we have to somehow convert the images into numbers for the computers to understand. There are basically two common ways to do this in the image processing.

1. Using Greyscale

The first approach is to use the greyscale. The image will be converted to greyscale. The computer will assign each pixel a value based on how dark it is. All the numbers are put into an array and computer does a computation on that array.

2. Using the RGB Values

Colors can also be converted into the RGB values. The computer can then extract the RGB value of each pixel and put the result in an array for interpretation. When the computer interprets the new image, it will convert the image to an array using the same technique, which compares the patterns of the numbers against the already known objects. The computer then allots a confidence score for each class. The class having the highest confidence score is the predicted one.

CNN

CNN is one of the most popular techniques used to improve the accuracy of image classification. Convolution Neural network is a special type of Neural Network that works in the same way like the regular neural network except that it has a convolution layer at the beginning. In CNN, instead of feeding the entire image as an array of numbers, the image is broken up into a number of tiles.

This allows the computer to detect the object regardless of where it is located in the image. Following is a step by step approach to implementing the deep learning for the image processing:

Preprocessing

First, we need to add a bit of variance to the data because the database that we are using is organized and contains little to no noise. We will artificially add noise to the images using a Python library named imgaug. In our database, we are going to add a random combination of the following images:

    • Crop the part of images
    • Flip the image horizontally
    • Adjust the hue, contrast and the situation

Splitting the Dataset

It takes a long time to calculate the gradient of the model using the dataset. Therefore, we will use a small batch of images. The dataset is then divided into the training set containing 50,000 images and the test contains 10,000 images.

Building a Convolutional Neural Network

The process of pre-processing and splitting is done and we can split our dataset by implementing our neural network. We are going to have 3 convolution layers with 2 x 2 max pooling. Max pooling is a technique that is used to reduce the dimensions of the image by taking the maximum pixel value of a grid. This also helps in reducing the overfitting and makes the model more generic.

The parameter identification is a challenging task as we have so many parameters to be adjusted.

Conclusion

In this article, we learned how we can build an artificial convolution neural network that can recognize the images with an accuracy of 78%. We preprocessed the images and make the model more generic, split the datasets into a number of batches and finally build and train the model. Webtunix AI is an artificial intelligence company in India that has worked on numerous image processing projects and have provided solutions to the global clients.