Computer vision is one of the main goals and subfields of artificial intelligence. It also falls under the greater concept of machine perception together with speech or audio recognition using other subfields of artificial intelligence such as natural language processing.
The general idea behind this AI subfield is to develop and implement methods that would equip machines or computer systems with the capabilities to acquire, process, analyze and understand digital images and even visual information from the real world.
Nevertheless, considering the aforementioned capabilities, the applications of computer vision are present in modern consumer electronic devices and software programs. Further developments would advance other technologies and produce innovations.
The Tasks and Notable Applications of Computer Vision and Their Examples
Remember that computer vision is concerned with enabling machines or computers with visual capabilities similar to the visual perception capabilities of humans or other organisms. This central idea has been present in several applications.
Understanding better the relevance of computer vision and its applications requires understanding its different tasks. Remember that it is involved with acquiring, processing, analyzing, and understanding digital images and visual information from the real world.
Below are the specific tasks:
• Image Detection: This entails classifying a picture into one of several predefined classes or categories. An example would be determining whether an image depicts a cat or a dog or recognizing the facial features of a person.
• Object Detection: Another task of computer vision is to identify the presence and location of one or more objects in an image. An example is the auto-tagging features of social networking sites that allows finding all the faces in a group photo.
• Object Tracking: This entails following and taking note of the movement of an item or object in a series of photos or a video stream. The goal of object tracking is to locate the position of the object in each sequence or video frame.
• Image Segmentation: Partitioning an image into multiple regions falls under a computer vision task called image segmentation. Each region corresponds to a different object or part of an object. This can supplement image detection.
• Position Estimation: This involves estimating the three-dimensional position and orientation of an object in an image or a video stream. Position estimation is essential in applications that require measurements and movements.
• Depth Estimation: Another computer vision task is called depth estimation. This entails estimating the distance of objects from a particular camera or other sensors. This task is used in applications that involve 3D reconstruction.
• Image Enhancement: This entails enhancing the quality of an image whether via a photo editing app or directly from a camera system. Examples of enhancements include eliminating noise, increasing contrast, and correcting the color balance.
• Image Synthesis: Creating new images that have a resemblance to the original or reference images is called image synthesis. This specifically involves combining attributes or adding additional ones to create new and original images.
Advances in the applications of machine learning and the more specific subfield of deep learning using artificial neural networks, in addition to developments in camera and sensor technologies, have brought forth practical applications of computer vision.
Below are the applications:
• Face Recognition Applications: Biometrics based on face detection used in smartphones and other security systems are based on this concept. The auto-tagging features of social networking sites such as Facebook are also based on computer vision. Advanced face recognition technologies supplement photo editing software apps, computational photography, and augmented reality.
• Computational Photography: Computational photography uses algorithms to manipulate and enhance images. Its best example can be found in the camera systems used in modern smartphones that use AI and leverages the capabilities of their AI accelerators to take photos that can rival photos taken using digital single-reflex camera or other professional-grade digital cameras.
• Enhances Augment Reality: Another application of computer vision is that it can improve the different applications of augmented reality. Examples include the use of filters in social media apps such as TikTok and Instagram, photo-enhancing applications of smartphones, immersive video gaming as demonstrated by game titles such as Pokémon Go, and virtual shopping or electronic commerce.
• Generative Artificial Intelligence: Generative AI is a general AI application that automates the creation of new data and content. There are different image-creation applications and services available including DALL-E. Part of their AI models is the use of a collection of images as training datasets to produce new and original images through image recognition and image synthesis techniques.
• Self-Driving Automotive Vehicles: Computer vision and other applications of different AI concepts and models are at the core of self-driving vehicles. These vehicles use object identification algorithms alongside advanced cameras and sensors to evaluate their surroundings in real-time and distinguish objects like people, road signs, obstacles, and other vehicles to safely navigate the road.
• Robotics and Other Machines: Robotics is another one of the fields of artificial intelligence. Computer vision allows robots and other relevant machines to perceive and understand the world around them. These are needed for these devices to make decisions and take actions based on their understanding of the situation while also allowing them to interact with their surroundings and even humans or other robots.
A particular application of computer vision can involve utilizing different computer vision tasks and relevant techniques. Consider the Face ID technology of Apple as an example which uses hardware components such as LiDAR for depth mapping, a camera module for face recognition, and the Neural Engine for machine learning.
Computational photography also uses different computer vision tasks such as image detection, image segmentation, depth estimation, image enhancement, and image synthesis, among others. These are made possible by leveraging the advantages of an AI accelerator, advanced camera sensors, and other hardware accelerators such as a dedicated image signal processor.
The self-driving capabilities of autonomous vehicles are made possible through object recognition, object tracking, three-dimensional or 3D mapping through dept estimation, and position estimation tasks. A particular self-driving vehicle uses different sensors such as cameras, LiDAR, radio detection and ranging or radar, and ultrasonic sensors.