Artificial Intelligence - Computer Vision

What is Computer Vision?

Computer Vision is a field of artificial intelligence that uses Machine Learning and Neural Networks to teach computers and systems to interpret and extract information from images and videos. This extracted information can be used for identifying and making decisions.

Role of AI in Computer Vision

Computer Vision and Artificial Intelligence complement each other, enhancing their abilities to accomplish development. Although AI lays the foundation for data analysis and decision making, computer vision introduces the aspect of visual understanding to this combination.

Computer Vision is a technology that in combination with AI allows machines to comprehend and analyze visual information, resembling the mechanism of human sight.

How does Computer Vision Work?

Computer vision requires large amounts of data, where it runs analyses of data over and over until it experts in distinguishing and recognizing images. For example, to train a computer recognize a dog in an image, it needs to be fed with vast qualities of different dog breed images to effectively recognize.

The most essential technologies which are used to effectively develop computer visions are Deep Learning, Convolutional Neural Network, and Machine Learning.

Machine Learning uses algorithms and models to enable computers to learn by itself in the context of visual data. The computer has to be fed with enough large data, for it to teach itself and differentiate the images. Convolutional neural Networks (CNN) helps machine learning model by breaking them down to pixels that are given tags or labels. These labels are used to perform convolutions and make predictions. A recurrent Neural Network (RNN) is used in a similar way but for video applications to help computers visualize series of frames and relate to each other.

Hardware of Computer Vision System

Some of the most commonly used hardware components are listed below −

• Power supply
• Image acquisition device such as camera
• A processor
• A software
• A display device for monitoring the system
• Accessories such as camera stands, cables, and connectors

Tasks of Computer Vision

Following is the list of tasks that incorporate computer vision to improve efficiency and productivity −

• Optical Character Reader (OCR): In the domain of computers OCR is a software to convert scanned documents into editable text, which accompanies a scanner.
• Face Detection: Many state-of-the-art cameras come with this feature, which enables to read the face and take the picture of that perfect expression. It is used to let a user access the software on correct match.
• Object Recognition: They are installed in supermarkets, cameras, high-end cars such as BMW, GM, and Volvo.
• Estimating Position: It is estimating position of an object with respect to camera as in position of tumor in humans body.

Application Domains of Computer Vision

Computer Vision has wide range of applications across various fields. Some of them include −

• Agriculture: Companies are employing computer vision in agriculture for sowing and harvesting purposes. Additionally, these solutions are also useful for weeding, detecting plant health, and advanced weather analysis.
• Autonomous Vehicles: Computer vision allows the vehicle to make autonomous decisions. Some of the tasks that engage computer vision are advanced processes such as path planning, driving scene perception, and behavior arbitration.
• Face Recognition: Computer vision can be used in detecting and recognizing faces in public, which is already being implemented in certain jurisdiction.
• Interactive Entertainment: These solutions use computer vision to deliver truly immersive experiences. For example, smart eye wear demonstrates how users can receive information about what they see while looking at it.
• Human Pose Tracking: These use computer vision to process visual inputs and estimate human posture. This is applied in industries such as gaming, robotics, fitness apps, and physical therapy.
• Medical Imagery: Medical systems depend mostly on pattern detection and image classification for diagnoses. Computer vision is mostly deployed in departments of pathology, radiology, and ophthalmology for visual pattern recognition.
• Manufacturing: Computer vision is used in predictive maintenance in their inspection systems, additionally it is used to automate processes.
• Retail Management: Retail stores use computer vision to monitor shopping activity, making predictions to prevent loss, and to make customer friendly.
• Education: Teachers use computer vision solutions to evaluate the learning process, identify disengaged students, and personalize teaching to ensure that they are not left behind.
• Transport: Computer Visions is being increasingly applied to increase transportation efficiency especially to detect traffic signal violators, detect speeding, wrong-side driving violations, and to identify disruptive behaviors.

Challenges of Computer Vision

Despite the advancements in Computer Vision, it faces several other challenges that can affect accurate interpretation of image and video analysis −

• Variability in Images: Images can vary in quality, lighting, angle, and background, making it difficult to analyze.
• Perspective and Scale Variability: Objects can appear differently depending on their distance,angle, or size in relation to the camera. This variability in perspective and scale presents a significant challenge for computer vision systems.
• Contextual Understanding: Computer vision systems often require help with understanding context. They can identify individual objects in an image, but understanding the relationship between them and interpreting the scene can be quite challenging.