The application of computer vision, the research direction of computer vision

Artificial Intelligence Computer Vision Engine: Clothing

Most people rely on vision to cook, cross obstacles, read street signs, see** and other tasks, vision is the most informative sense, such as how the world around us, how to interact with the world, so for half a century, computer scientists have been trying to make computers have vision, so it was born"Computer vision"this field.

Computer vision is the study of how computers can understand and analyze digital images. The goal of computer vision is to enable computers to process and understand visual information in the same way that humans do. Computer vision includes many different subfields and directions, and here are some of the main ones:

Image Recognition and Classification: This direction focuses on how to identify objects, scenes, or concepts in images, dividing images into different categories.

Object Detection: The task of object detection is to locate the location of a specific object in an image and identify it with a bounding box.

Face recognition eggplant chain: Face recognition focuses on detecting and recognizing faces in images, and is used in security monitoring, social networking and other fields.

Semantic segmentation: The task of semantic segmentation is to assign each pixel in an image to a category, so as to achieve semantic understanding of each region in the image.

Instance segmentation: The instance segmentation task not only needs to classify the objects in the image, but also needs to distinguish between different instances of the same category.

Pose Estimation: Pose estimation focuses on detecting and identifying key points of the human body from images, and is used for action recognition, motion analysis, etc.

Optical Flow Estimation: Optical flow estimation is the study of how to estimate the direction of motion and the speed of an object from an image sequence.

Image Compositing and Style Transfer: This direction focuses on how to apply one style to another image or generate a new image based on given conditions.

Image Restoration and Enhancement: Image Restoration and Enhancement studies how to remove noise, blur, and other defects in an image to improve image quality.

Visual Question Answering (VQA): Visual question answering is a task that combines computer vision and natural language processing techniques and requires the system to ask questions related to images based on input images.

Image generation: Image generation focuses on how to generate new, photorealistic images using deep learning models.

These directions have a wide range of applications in many fields, such as autonomous driving, medical image analysis, drones, intelligent surveillance, virtual reality (VR) and augmented reality (AR). With the development of deep learning and neural networks, computer vision technology continues to make breakthroughs, which is of great value for many practical application scenarios.

There's a lot of computer vision.

Downstream missions can all play a military role.

For example, super-resolution technology is used to increase the resolution of distant blurry images;

Another example is the use of deep convolutional neural networks.

The applications of computer vision are:

Face recognition. Face recognition technology has been relatively mature and has been applied in many places, and the accuracy rate of face recognition is currently higher than the recognition accuracy of the human eye.

2.Unmanned. Unmanned driving, also known as autonomous driving, is currently an important research direction in the field of artificial intelligence, allowing cars to drive autonomously or assist drivers to drive and improve the safety of driving operations.

Computer vision plays a very key role in unmanned driving, such as road recognition, road sign recognition, traffic light recognition, pedestrian recognition and other things that need to be paid attention to in the ordinary driving process. In addition, it also includes 3D reconstruction and autonomous navigation, which can be reconstructed through lidar or vision sensors to assist the car in autonomous positioning and navigation, and make reasonable path planning and related decisions.

3.Medical image processing. Common medical imaging, such as B-ultrasound, MRI, X-ray, etc. With the development of AI technology, there are also some AI diagnosis functions, and AI analyzes the possibility of related diseases based on the characteristics of the image.

4.Industrial testing. Common medical imaging, such as B-ultrasound, magnetic resonance, X-ray, etc. With the development of AI technology, there are also some AI diagnosis functions, and AI analyzes the possibility of related diseases based on the characteristics of the image.

5.Intelligent map recognition. Intelligent map recognition is a common application of computer vision in our lives.

When you see a paper document and want to convert it into an electronic document, you can directly take a picture of the document and use relevant software for text recognition, which can automatically convert the text in the image into an electronic document, and even automatically translate it into other language families. When you see a piece of clothing or an item, and want to find him on the Internet** and other relevant information, enter it directly**, search for pictures, and soon find a lot of places where it appears and a lot of similar**. There are even some interesting functions that can directly tell you what the object is, or roughly determine the approximate age of the portrait in the portrait.

6.Other. There are many applications of computer vision, and with the development of technology, there will be more and more application fields.

The application in the industrial field and the application in robotics will not be repeated here. I believe that with the continuous development of computer vision technology, our lives can become more and more intelligent, intelligent and convenient.

Computer vision refers to the analysis, understanding, processing, and recognition of images in the real world by computer systems through digital image processing, pattern recognition and other technologies. The application of computer vision technology is very wide, and the following are some of the main fields and applications:

Medical diagnosis: Through medical image analysis technology, it can automatically identify and analyze medical images such as X-ray, CT, and MRI to assist doctors in disease diagnosis.

Autonomous driving: Real-time processing and analysis of sensor data such as cameras, radars, and lidars enables vehicles to autonomously perceive their surroundings, make decisions, and drive autonomously.

Human-computer interaction: Recognize and analyze human movements, expressions, etc. through cameras, depth cameras and other devices to realize the naturalization and intelligence of human-computer interaction.

Industrial manufacturing: Inspect, classify, and sort products on the production line through machine vision systems to improve production efficiency and quality.

Agricultural intelligence: Through the processing and analysis of farmland images, the growth status of crops, diseases and pests can be monitored and identified, and farmers can be assisted in precision agricultural management.

Urban intelligence: Through the processing and analysis of images of urban scenes, various applications such as urban traffic supervision, intelligent parking, and urban management can be realized.

In general, the application field of computer vision is very wide, and with the continuous development and maturity of artificial intelligence technology, its application scenarios will become more and more diversified and popular.

Computer vision has applications in face recognition, security, agriculture, industry, medical field, unmanned driving and other scenarios.

Computer vision is a science that studies how to make the machine "see", and further, it refers to the machine vision of using cameras and computers instead of the human eye to identify, track and measure the target, and further do graphic processing, so that the computer processing becomes more suitable for the human eye to observe or transmit to the instrument for detection.

As a scientific discipline, computer vision studies the theories and techniques related to the establishment of artificial intelligence systems that can obtain 'information' from images or multi-dimensional data. The information referred to here refers to information that Shannon defines and can be used to help make a "decision".

Because perception can be seen as extracting information from sensory signals, computer vision can also be seen as the science of studying how artificial systems can "perceive" from images or multidimensional data.

Analysis: Vision is an integral part of various intelligent autonomous systems in various application fields, such as manufacturing, inspection, document analysis, medical diagnosis, and military fields.

Because of its importance, some advanced countries, such as the United States, have listed the study of computer vision as a major fundamental problem in science and engineering, the so-called grand challenge, which has a broad impact on the economy and science. The challenge of computer vision is to develop visual capabilities for computers and robots that are comparable to those of humans.

Machine vision requires image signals, texture and color modeling, processing and reasoning, and object modeling. A capable vision system should have all of these processes tightly integrated. As a discipline, computer vision began in the early 60s, but many important advances in the fundamental research of computer vision were made in the 80s.

Computer vision is closely related to human vision, and having a correct understanding of human vision will be very beneficial to the study of computer vision. To do this, we'll start with human vision.

Computer vision has a wide range of applications in many fields and industries, and here are some common computer vision application scenarios:

Autonomous driving: Computer vision technology is widely used in autonomous vehicles, such as detecting roads, pedestrian benches, vehicles, etc., to achieve environmental perception, navigation, and control.

Medical image analysis: Computer vision technology is used to analyze medical images, such as CT, MRI, X-ray, etc., to assist doctors in disease diagnosis and lesion localization.

Industrial inspection: Computer vision is used in industrial production lines for automated inspection, such as product quality inspection, defect identification, etc., to improve production efficiency and quality.

Drones: Computer vision technology is used in drones for environmental perception, target tracking, autonomous navigation, and more.

Rough training of face recognition: Face recognition technology is widely used in scenarios such as smartphone unlocking, payment verification, and attendance systems.

Virtual Reality (VR) and Augmented Reality (AR): Computer vision technology is applied in VR and AR to scene reconstruction, tracking, interaction, and more.

Smart home: Computer vision technology is used in the field of smart home to face recognition, door locks, surveillance cameras and other devices.

Sports Analytics: Computer vision technology is used for athlete posture recognition, motion analysis, game replay, and more.

Robotics: Computer vision technology is applied in the field of robotics for environmental perception, target recognition, autonomous navigation, etc.

Culture and entertainment: Computer vision technology is used in animation production, film and television special effects, game development, etc. in the field of culture and entertainment.

Computer vision applications include: autonomous driving, industrial manufacturing, medical diagnosis, security monitoring, etc.

1. Autonomous driving:

Computer vision autonomous driving refers to the use of computer vision technology and various sensors to realize the vehicle to automatically perceive the surrounding environment, and make decisions and control operations. Its core is to convert images or data into numerical signals, analyze and process them through computer algorithms, obtain the road, vehicles, pedestrians and other conditions in front of the vehicle, and make corresponding decisions based on this.

Computer vision autonomous driving technology mainly includes three parts: scene perception, decision-making operation, and control instructions. Scene perception includes Lila Collision using sensors such as radar, lidar, and camera to obtain environmental information, decision-making operations include specific decisions on vehicle control based on the collected environmental information, and control instructions are to convert computer-processed decision-making into operation instructions.

2. Industrial manufacturing

The application of computer vision in industrial manufacturing is mainly to use computer vision technology to detect and analyze the shape, size, structure, surface defects and other aspects of objects in real time, so as to achieve automated and intelligent production.

Intelligent detection is one of the main tasks of computer vision applied in industrial manufacturing, which is the detection and identification of product surfaces through the combination of machine vision algorithms and sensors. For example, computer vision technology is used to inspect products on the production line for a series of quality, size, shape and appearance defects, so as to ensure the quality and consistency of products.

3. Medical diagnosis:

Computer vision medical diagnosis refers to the process of using computer vision technology to analyze and identify medical image data, so as to diagnose and improve the disease. At present, medical imaging data has become an important part of medical diagnosis and treatment, such as CT, MRI, ultrasound and other imaging examinations can provide doctors with a large amount of patient data.

The application of computer vision medical diagnosis in the medical field involves many aspects, such as tumor detection, segmentation and localization, classification, quantification and analysis of tissues, organs and lesions, surgery and adjuvancy, disease and progress monitoring.

4. Security monitoring:

Computer vision security monitoring refers to the use of computer vision technology to monitor and analyze the process, including image recognition, motion detection, object tracking, behavior analysis, etc. It aims to use image processing technology and artificial intelligence algorithms to help people carry out surveillance work more efficiently and safely, improve safety and reduce human misjudgment.

In the field of security monitoring, computer vision technology can be applied to multiple scenarios. For example, for the monitoring of public places such as shopping malls, subway stations, airports, etc., abnormal behavior analysis can be carried out through computer vision technology, and automatic alarms. In addition, computer vision monitoring can also be applied to the field of transportation, such as traffic accident monitoring, vehicle license plate recognition, etc.