COMPUTER VISION

INTRODUCTION

Computer vision which is abbreviated as CV, is defined as a technique that helps in developing some techniques further to help “see” and understand the content of digital images such as photographs and videos computers. Computer vision is that the science and technology of machines that see. It helps in training computers to visually understand the visual world. Using digital images from cameras and videos, our machines can easily and accurately identify the objects and can give a reaction to what they see.

Computer vision is working with millions of calculations in order to recognize patterns and to have the same accuracy as the human eye. Patterns are often seen physically or are often observed mathematically by applying algorithms.

HISTORY

Early experiments in computer vision happened within the 1950s, using a number of the primary neural networks to detect the sides of an object and to sort simple objects into categories like circles and squares. The primary commercial use of computer vision interpreted typed or handwritten text using optical character recognition in the 1970s. This advancement was wont to interpret transcription for the blind.

In the late 1950s and early 1960s, to mimic human vision systems and to ask computers what they see was the goal of image analysis. Also, using x-rays, image analysis had been completed manually, MPIs or hi-res space photography. Nasa’s map of the moon took the lead with digital image processing, but until 1969 it wasn’t fully accepted. As computer vision evolved, programming algorithms were created to unravel individual challenges. Machines became better at doing the work of vision recognition with repetition. There has been a huge improvement of deep learning techniques and technology over the years,. We now have the power to program supercomputers to coach themselves, self-improve over time and supply capabilities to businesses as online applications.

HOW COMPUTER VISION SYSTEM WORK?

Computer vision may be a field that has methods for analyzing, processing, acquiring and understanding images and, generally, high-dimensional data from the important world so as to supply numerical or symbolic information.

You can say computer vision is employed for deep learning to research the various sorts of data sets through annotated images showing the thing of interest in a picture It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.

This process depends subject to use of varied software techniques and algorithms, which are allowing the computers to acknowledge the patterns altogether the elements that relate to those labels and make the model predictions accurately in the future. Computer vision is often only utilized only with image processing through machine learning.

THE BREAKDOWN OF COMPUTER VISION

Images are broken down into pixels, and pixels are smallest unit of information that makes up the picture, which are considered to be the elements of the picture.

Computer vision isn’t almost converting an image into pixels then trying to form sense of what’s within the picture through those pixels. You have to understand the bigger picture of how to extract information from those pixels and interpret what they represent.

IMAGE CLASSIFICATION AND SEGMENTATION

When a computer classifies an image in a certain category is simple explanation of the classification of a picture. In the picture below the classification of the primary object would be sheep. The localization or location is identified by the box surrounding the thing within the picture.

Object detection detects instances of semantic objects of a particular class. The picture below has 3 sheep within the picture. Classifying them (boxes) as sheep1, sheep2, and sheep3

Every pixel belongs to a particular class. In the picture below the classes is road, grass or sheep. Pixels within the class are represented by the same color. (Grass is green, road is gray, and sheep is orange). This describes semantic segmentation.

With instance segmentation different objects of an equivalent class have different colors. (sheep3 = orange, sheep2 = dark yellow, sheep1 = bright yellow)

ADVANTAGES OF COMPUTER VISION

· It is simple and fast process- It allows the clients and industries to check. Also, it gives them access to their products. It’s possible because of the existence of Computer Vision in fast computers.

· It leads to reliability of the machines and actions — Computers and cameras don’t have the human factor of tiredness, which is eliminated in them. The efficiency is typically an equivalent , it doesn’t depend upon external factors like illness or sentimental status.

· It maintains accuracy and precision of Computer Imagining, and Computer Vision will ensure a better accuracy on the final product.

· It has wide range of uses such as mentioned above- We can see the same computer system in several different fields and activities. Also, in factories with warehouse tracking and shipping of supplies, and within the medical industry through scanned images, among other multiple options.

· It reduces the costs of observing and developing solutions to our problems — Time and error rate are reduced in the process of Computer Imagining. It reduces the cost of hire and train special staff to do the activities that computer will do as hundreds of workers.

· Computer vision allows self-driving cars to make sense of their surroundings. Cameras capture video from many angles around the car and give it to computer vision software, which then processes the images in real-time to find the extremities of roads, detect other cars, objects and pedestrians read traffic signs. The self-driving car can then steer its way on streets and highways, (hopefully) safely drive its passengers to their destination avoid hitting obstacles.

· Computer vision also plays an important role in facial recognition applications, the technology that enables computers to match images of people’s faces to their identities. Computer vision algorithms detect countenance in images and compare them with databases of face profiles.

DISADVANTAGES OF COMPUTER VISION

· It requires specialists to work upon — There is a huge necessity of specialist related to the field of Machine Learning and Artificial Intelligence. A professional can take full advantage of Computer Vision. that knows how those devices work.

· Spoiling: elimination of human factor can be good in some cases. But when the machine or device fails, it doesn’t announce or anticipate that problem. But a person can tell beforehand when the someone won’t come.

· Failing in image processing: It is highly probable that Computer Vision and image processing will fail, when the device fails because of a virus or other software issues. But if we don’t solve the matter, the functions of the device can disappear. It can freeze the entire production in the case of warehouses.

· A computer with the Internet is one of the greatest tools in history to learn about anything. It’s simple to become overly reliant on a computer and other electronic devices. For example, for finding spelling errors a spell checker is a great tool.

COMPUTER VISION APPLICATIONS

There are many computer vision applications get in the market. Below are just a few:

· It helps in Automatic inspection i.e. image-based automated inspection, e.g., in manufacturing applications

· It helps to Assist humans in identification tasks i.e. to identify object/species using their properties, e.g., a species identification system

· It is used in Controlling processes (in a way of monitoring robots), e.g., an industrial robot

· It is used in Detecting events, e.g., for visual surveillance or people counting

· It is used for Modeling objects or environments (using drones can analyses about climatic factors that leads to change in vegetation, etc.), e.g., medical image analysis or topographical modeling.

· It helps in Navigation, e.g., by an autonomous vehicle or mobile robot

· It helps in Organizing information, e.g., for indexing databases of images and image sequences

· Facebook uses facial recognition (“DeepFace”) when automatically tagging photos that are posted to your profile. After negative feedback from many audiences thanks to privacy, Facebook only allows the recognition is for opt into it.

· Cars — Computer vision is a hot topic in the car industry. Companies like Tesla and Google are building self-driving cars. Cars today have Adaptive/Dynamic cruise control that has the power to take care of a secure distance from the vehicles ahead.