What is computer vision (computer vision)?

Tasks and capabilities of computer vision

Computer vision covers a wide range of tasks, from simple operations on images to complex scenes of interpretation:

• Image Processing (Image Processing): Basic operations to improve image quality, reduce noise, change contrast, detect edges, etc.
• Object Recognition/Detection: Identification and localization of objects of a specific type in an image (e.g., cars, faces, animals).
• Image Classification (Image Classification): Assigning one or more labels to an image to describe its content (e.g., “cat,” “dog,” “landscape”).
• Image Segmentation (Image Segmentation): Dividing an image into regions corresponding to different objects or parts of a scene. This can be semantic segmentation (assigning a label to each pixel) or instance segmentation (distinguishing between different instances of objects of the same type).
• Object Tracking: Monitor the position and movement of objects in video sequences.
• Facial Recognition: Identification or verification of a person’s identity based on his or her face.
• Optical Character Recognition (OCR): Converting images of text (printed or handwritten) into digital text.
• Motion/Activity Analysis: Interpretation of the movement and actions of people or objects in video recordings.
• 3D Reconstruction (3D Reconstruction): Creating 3D models of a scene or objects from 2D images.

Technologies used in computer vision

The basis of modern computer vision is machine learning algorithms, especially deep learning. A key role is played by Convolutional Neural Networks (CNNs), which excel at analyzing visual data. Other techniques such as signal processing, projective geometry, statistics and graph theory are also used.

Applications of computer vision

Computer vision is used in many fields:

• Medical: Analysis of medical images (X-ray, CT, MRI) to aid diagnosis, detect lesions.
• Industry and manufacturing: Automated product quality control, industrial robotics (robot navigation, object grasping), process monitoring.
• Transportation and automotive: driver assistance systems (ADAS), autonomous vehicles (recognition of traffic signs, pedestrians, other vehicles).
• Security and monitoring: video surveillance systems (intruder detection, crowd analysis), access control (facial recognition), biometrics.
• Retail: In-store customer behavior analysis, self-service systems, inventory management.
• Entertainment and social media: Filters and effects in apps (e.g. Snapchat, Instagram), photo tagging, visual content moderation.
• Agriculture: Crop monitoring, plant health assessment, precision agriculture.
• Robotics: Navigation of mobile robots, interaction with the environment.

Challenges and the future

Despite tremendous advances, computer vision still faces challenges, such as reliability of performance under varying lighting conditions, dealing with partial obscuration of objects, interpretation of complex scenes, or ethical issues related to face recognition and surveillance. The future of the field involves the development of even more advanced AI models, integration with other senses (e.g., sound, touch), and the creation of systems capable of deeper understanding of visual context.

Summary

Computer vision is a fascinating and rapidly developing field that gives computers the ability to “see” and interpret the visual world. Thanks to advances in AI, it is finding more and more applications, revolutionizing many industries and aspects of our lives.