Understanding Image Processing Using Artificial Neural Networks
This article delves into the basics of image processing using ANN, very useful for a beginner to gain insight into this technique.
INTRODUCTION TO IMAGE PROCESSING
Nowadays every mobile and computer has the feature of editing and enhancing an Image because Modern technology has made it possible to manipulate an image, we get clicked by applying filters, and can be made even more beautiful.
The main concept behind this new feature of applying filters or editing an image is Image processing. For ages, we are watching television image processing play a prominent role at that time.
Image processing can be further divided into two different types
- Analog image processing and
- Digital image processing.
Analog Image Processing
Analog image processing is performed on analog signals. It includes processing two-dimensional analog signals. In this type of analog processing, the images are changed skillfully color by electrical means by varying the electrical signal.
A common example includes is television broadcasting in older days through the dish antenna systems.
Digital Image Processing
Digital image processing is a technique using which we can manipulate a digital image through an algorithm using a digital computer. This algorithm takes digital images as input and produces the characteristics of images or parameters of an image as output.
An image is a two-dimensional signal. It is defined by the co-ordinate function F(a,b) where a and b are the two co-ordinates defined in the horizontal and vertical directions. The value of F(a,b) at any point is giving the pixel value at that point of an image. A Pixel is most widely used to represent the intensity of a Digital Image.
In other words, an image can be defined by a two-dimensional array particularly arranged in rows and columns.
This image in a coding language is represented as a two-dimensional array of numbers ranging between 0 and 255. Each value between 0 and 255 represents the intensity of the pixel.
APPLICATIONS OF IP IN REAL-LIFE
Digital Image processing features a wide selection of applications in the world. In almost every technical aspect we use digital image processing.
Digital Image processing isn’t just to regulate the spatial resolution of the photographs we capture each day by the camera. It is not just limited to extend the brightness of the photo, Applying the filters, etc. Rather, it’s way more than that in real life.
- In Medical life, Image processing has a very wide range of applications like Diagnosing a disease using X-ray, Thermography, Mammography, Size of the tumor present inside the body, etc.
- Removing noise, background, and Image restoration and sharpening.
- Video processing and live telecasting.
- Pattern recognition
- Image Transmission and Image Encoding
INTRODUCTION TO NEURAL NETWORKS
Neural Networks are just called Artificial Neural Networks (ANN) are generally propelled by Biological Neural Networks that comprise the human cerebrum. An Artificial Neural Network is the assortment of nodes known as Artificial Neurons which are way more similar to the neurons in the human mind. An Artificial neural network contains layers of interconnected hubs or neurons. A neuron in an artificial neural network is a function that accumulates the data and classifies the data as per a particular pattern. An artificial neuron that gets a signal or data at that point processes it and can flag or pass data to different neurons associated with it.
A neural network is a gathering of calculations that attempt to acknowledge the fundamental connection between a bunch of information through a cycle that is exactly similar to the working of the brain.
Neural networks learn or are trained by example problems, every one of which contains a realized information and yield result shaping weighted-likelihood characterizing the connection between those two (information and yield), which are stored within the data structure of the architecture itself.
An artificial neural network trains the example information to distinguish the contrast between the anticipated yield that is we get by applying the algorithm and the targeted yield. That difference is known as an error. The network then adjusts its weighted probability itself to minimize the error. Progressive changes in the network will make the neural network produce an outcome that is path closer to the targeted result. After a necessary number of these changes, the training can be ended dependent on specific rules.
REAL-LIFE APPLICATIONS OF NEURAL NETWORKS
Nowadays everything has become handy from online shopping to handwriting recognition, in each of these neural networks has a prominent role to play. Neural Networks find a considerably great variety of applications in areas where traditional computers don’t perform well.
- Recommended Systems
- Online Shopping
- Searching ex. Google search results in the most relevant ones because of Neural networks
- Fraud Detection and Face Recognition
- Speech and Handwriting Recognition
- Personal assistants like Siri, Google, Alexa, etc.
The above images show the basic architecture of ANN.
IMAGE PROCESSING USING ARTIFICIAL NEURAL NETWORKS
Image processing using artificial neural networks (ANN) has been profitably employed in various fields of activities like Applied science, Mechanics, Geotechnics, Industrial surveillance, Department of Défense, Automobiles, and transport, Image pre-processing, Data reduction, Segmentation, and Pattern recognitions are the processes utilized in managing images with ANN. A picture is often represented as a matrix, each element of the matrix containing color information for a pixel. The matrix is employed as an input file into the neural network. The tiny dimensions of the images, simply and quickly help to find out, establish the dimensions of the vector and therefore the number of input vectors. The transfer function used could be a sigmoidal function. The training rate includes values between [0,1] and therefore the error it’s recommended to be below 0.1.
In the matrix of an image, each value represents the pixel intensity of the image. Each pixel value can be represented as a combination of basic three color intensities: Red, Green, Blue (RGB). The below figure depicts the pixelization of the image.
INPUT TO NEURAL NETWORK ALGORITHM
All the images in the data set are to be scaled to the same size to make the procedure easy and comfortable. Neural networking algorithm accepts an only one-dimensional array as input so, we have to convert two-dimensional matrix representation to a single dimension array. Each input neuron to the algorithm represents the color information and each output neuron corresponds to an image.
Steps involved in Processing the images using ANN
- Image pre-processing: Pre-processing involves conversion to gray-scale, noise removing by applying filters, image smoothening, restoring and, improving images. The output of pre-processing will be the image with the same dimensions as input but an enhanced version.
- Data Reduction or Feature Extraction: Each image has a lot of different pixel values; Out of all those pixel values required ones are extracted as features and these features are given to the input window. The feature extraction can be done by compressing the image, or by edge detection.
- Segmentation: Segmentation involves identifying the region of interest (ROI) by dividing the image into segments.
- Classification: This step involves the classification of objects or images to their respective classes.
REAL-LIFE APPLICATIONS OF IMAGE PROCESSING USING ANN
- Fraud detection or Industrial inspection – To detect fraud and defective products in the industry.
- Department of Défense – To identify the targets in war
- Medical field – Detection of tumors and diagnosing
The above picture shows the application of Image processing using ANN that is face recognition.