Join

Computer vision courses

309 Courses

California Institute of Technology XuetangX

无人驾驶技术概论

本课程由北京联合大学机器人学院院长李德毅院士指导，通过多年无人驾驶技术研究的积累，形成了独具特色的教学内容。 （1） 面向产业热点，解决企业的人才需求。 无人驾驶技术迅速发展需要大量优秀的人才。本课程紧跟企业需要，结合现有专业知识，深入浅出地讲述无人驾驶技术的基本原理，引导学生在这一多学科交叉领域中进行研究。 （2） 以科研实践为基础，推动知识传授。.
course image
California Institute of Technology XuetangX

图像采集与处理

Join our comprehensive Chinese language course to gain in-depth knowledge of image acquisition and processing. Delve into essential topics such as camera sensor technology and lens selection, paired with sophisticated image preprocessing methods like enhancement and edge detection. Learn various filtering techniques and explore object detect.
course image
California Institute of Technology XuetangX

机器人技术基础

机器人技术基础课程是机械设计制造及其自动化专业的一门专业方向课。该课程主要讲述机器人运动学、动力学和轨迹规划的基本概念，常见机器人结构及误差修正方法、图像识别与语音识别基本原理，串联式机器人系统设计计算。 本课程旨在于培养学生阅读、分析机器人系统组成、工作原理及特点的能力，以及串联式机器人的工程设计能力和对常规机器人系统的分析能力，使学生具备使用、.
course image
California Institute of Technology XuetangX

智能视觉技术应用

智能视觉技术应用课程旨在培养学生掌握机器视觉中相机光源连接与调试、采集图像、掌握相机的取像等理论知识。学生将学习标定板的使用及VisionPro视觉软件基础工具的应用，并掌握程序生成向导以生成应用程序，熟练运用视觉技术解决实际工程问题。 随着工业自动化技术的发展，视觉技术对提高生产效率、降低生产成本和保证产品质量日益重要。学习该课程有助于培养具备创新及实.
course image
California Institute of Technology XuetangX

清华大学计算机科学与技术系六十周年系庆学术报告（三）人工智能

六十周年系庆学术报告是清华大学计算机系建系六十周年的庆祝活动之一。组织这一系列学术报告的目的是：通过邀请国内外学术界与产业界知名的学者、专家做专题演讲，介绍国际前沿研究及产业发展动向，分享他们的战略思考与成果，实现在交流中找到差距，明确方向，从而促进清华大学计算机学科进一步向前发展。 六十周年系庆学术报告是一场学术盛宴。报告的演讲者都是各个领域的.
course image
California Institute of Technology XuetangX

Introduction to Digital Media Technology

Digital media technology is a dynamic interdisciplinary and technical field that seamlessly integrates digital information processing technology, computer technology, digital communication, and network technology. By utilizing modern computing and communication tools, it enables comprehensive processing of text, sound, graphics, images, video, a.
course image
California Institute of Technology Coursera

Getting Started with Generative AI API

Discover the expansive world of generative AI with our specialized course on leveraging Python for various AI functionalities, including text, image, and code generation. Harness the power of OpenAI's industry-leading API to explore a wide spectrum of developmental possibilities tailored to your needs. Each course module concludes with a pra.
course image
California Institute of Technology Microsoft Learn

GitHub responsible AI

Module 1: Introduction to AI Workloads and Azure AI Services. Explore Microsoft's Responsible AI policies and learn about AI solutions and practices for ethical AI. Module 2: Using Azure AI Face. Gain skills in detecting and analyzing faces using Azure AI Face service. Module 3: Foundations of Generative AI. Understand large language mo.
California Institute of Technology XuetangX

数字化精密测量与智能化

数字化精密测量与智能化技术是实现高精度、高性能高端装备制造的核心所在。本课程通过将理论教学与实验教学相结合，专业知识与思想道德教育相互融合，以及前沿科技和科研成果的整合，旨在培养高端仪器领域的领军人才。 提供者: XuetangX 相关领域: 人工智能课程, 计算机视觉课程, 传感器融合课程
course image
California Institute of Technology Udemy

Cognex In-Sight Machine Vision Industrial Development SCADA

Embark on an enriching journey to master Cognex In-Sight Machine Vision Systems through this expertly crafted programming guide. Offered by Udemy, this course focuses on leveraging Easy Builder for advanced PLC Automation and SCADA Development HMI. Perfect for enthusiasts and professionals alike, the content spans across vital categorie.
course image

“Computer vision student” sounds like a quote from science fiction, don’t you think? In fact, a computer vision engineer is a profession that, although it has not yet become the most widespread, is already rapidly gaining popularity and offers high salaries even at the start of a career.

What is computer vision and what does its developer do?

A computer vision engineer is a specialist who teaches computers to extract information from images. In particular, automatically recognize objects or gestures in images and videos. If a person can visually determine something (for example, find a defect in a product), a computer can also be trained to do this - and thus save time and resources, simplifying many processes.

Developments in the field of computer vision courses are used in a wide variety of companies whose products are related to images or video. This includes the production of self-driving cars, helping doctors interpret MRI images when searching for tumors, and even facial recognition in the subway to identify violators of the self-isolation regime. Computer vision specialists help many e-commerce businesses reduce the burden of moderation: for example, when an ad service like Avito fights trolls who upload pictures with inappropriate content.

Computer vision specialists after computer vision courses are called differently: developers, engineers, and researchers (computer vision scientist). Essentially, a computer vision specialist is more of an engineer who uses mathematics and programming as working tools. So, globally, a computer vision engineer, a computer vision scientist, a computer vision developer and a technical vision developer are one and the same thing.

What does a computer vision developer actually do?

As a rule, the day of such a specialist begins with a stand-up with the team. He then writes code to train neural networks, preprocesses data, and analyzes experiments. A computer vision developer can work alone or in a team, where everyone performs part of a larger task.

As for working tools, the Python language is usually used to write code for experiments, and the Tensorflow or Pytorch frameworks are used to train neural networks. The work also involves special libraries for image processing such as OpenCV. For high-load projects, the C++ language can also be used, since anything written in it is executed many times faster.

Computer vision is a young, dynamically developing field at the intersection of science and engineering, in which there are still more experiments than ready-made solutions. To grow, a specialist here needs to constantly learn. But it is the novelty and non-standard nature of the tasks, as well as the opportunity to create something truly innovative, that brings many people into this profession.

What do they teach in computer vision classes at AI Education?

Training at the best computer vision course typically consists of three modules: creating infrastructure, basics of machine learning and studying computer vision.

The first block at a computer vision online course can be called introductory. Since specialists in the field of computer vision rely on knowledge of mathematics and programming when solving problems, at the start they will have to study from scratch or brush up on topics from higher mathematics, mathematical analysis and linear algebra, as well as work with the Python language. Don’t worry if your knowledge is limited to school mathematics, which was “long ago and not true”: we will help you improve the necessary topics in the first module, so that in the future all students can move through the program at the same rhythm.

The second module is entirely devoted to machine learning. It helps solve computer vision problems faster and easier. For example, for facial recognition, you can expertly describe facial features based on questions that are asked when compiling an identikit. Or you can feed the algorithm a lot of photographic portraits with markings about which face belongs to whom, and then the algorithm itself will learn to extract features by which faces can be identified. In the future, if you need to determine who is in the photo, the algorithm will only need a database of portraits. If there is a photo of the person you need, the system itself will easily find him.

In the second module you will examine probability theory and mathematical statistics. Students will practice solving problems using fundamental algorithms and data structures in Python, become familiar with Python libraries for Data Science (NumPy, Matplotlib), as well as machine learning algorithms.

Finally, in the third module at this machine vision course you will analyze the main tasks of computer vision, we will work with mathematical morphology and the OpenCV and PIL libraries!