Many of us know about Computer Vision and Image Recognition. But the difference is not clear and the words can be used interchangeably. Computer Vision, developed by many AI developers, allows a computer to imitate human vision, store the discoveries and take actions according to the stored information. Image Recognition, on the other hand, is the analysis of the pixels and patterns of an image displayed so as to recognize the image as a particular object.
We, as humans, can recognize the difference between a cat, dog or an apple. But the process of recognition is hard for a computer in its initial stage.
Image Recognition is the ability of a computer to identify and detect the features of an image or objects. It includes processes of capturing, processing, examining and sympathizing images. Computers use machine vision technology that is powered by the Artificial Intelligence System.
Image Recognition Process
How does it work? How is a computer trained to distinguish between any two images? The following are the steps for image recognition.
Step 1: A number of characteristics called features are extracted from the given image. Pixels, which make up the image, represents a number or set of numbers where the range of itself is called the color depth. The color depth specifies the maximum number of potential colors that can be used in the image.
Step 2: The images are converted into thousands of features, then labelled and grouped in different categories. When more images are added, the computer can be better trained to recognize an image.
Step 3: The computer is trained with pre-labelled images. If the computer is made to recognize an image, the extracted form of this image is entered as an input while the labels are in the output side. The computer is then made to differentiate the image with its features coming from the input with the labels in the output.
Step 4: Once the computer is trained, it can be used to recognize an unknown image. The new image will go through the pixel feature extraction process. Convolutional Neural Networks is widely applied in image classifications, object detection and image recognition.
In the commercial world, major applications of image recognition can be found in face recognition, security and surveillance, object recognition, image analysis in medical field, etc. Image recognition has been adopted in e-commerce, including search and advertising. It helps in an interactive world by making searches easier. The advancements in the automobile industry are made possible by computer vision technology using AI image recognition.
The new AI chip
Recently, a new type of artificial eye, made by the combination of light-sensing electronics with a neural network on a single tiny chip, can perform the image recognition process in nanoseconds, faster than the existing image sensors.
Most image recognition process takes a lot of computing power to work. The huge amount of data collected slows down the recognition process. A sensor that captures and processes and image at the same time makes the process much faster using less power. This new sensor is an exciting new thing which sets the path for the movement of the AI into hardware, making the process quick and efficient.