Learn Computer Vision and Image Processing in LabVIEW

🌟 Learn Computer Vision and Image Processing in LabVIEW 🌟 Note! The course price will increase to $200 as of 1st February 2019 from $190. The price will increase regularly due to updated content. Get this course while it is still at a low price!

🎉 Latest Update: Course Updated For January 2019 🎉 OVER 3040+ SATISFIED STUDENTS HAVE ALREADY ENROLLED IN THIS COURSE!

Introduction to Computer Vision and Image Processing in LabVIEW

Are you ready to unlock the potential of computer vision? With this comprehensive course, you'll dive into the fascinating world of image processing using LabVIEW and its powerful Vision Development Toolkit. Whether you're a beginner programmer or looking to expand your skills, this course is designed to guide you through every step of creating vision-based applications from scratch.

Course Breakdown:

• LabVIEW Vision Development Toolkit Download and Installation 🔍

  • Get started by setting up the necessary tools on your system.

• Basic Feature Detection 🎨

  • Learn the fundamentals of detecting basic features in images.

• Circle, Color and Edge Detection Algorithms 🖌️

  • Explore various algorithms to detect circles, colors, and edges within images.

• Advance Feature Detection 🚀

  • Master pattern matching, object tracking, Optical Character Recognition (OCR), BarCodes, and more.

A Powerful Skill at Your Fingertips 🛠️ Learning the fundamentals of Image processing with LabVIEW is not only easy but also very powerful. With excellent documentation and its role as a prototyping tool for vision-based algorithms, LabVIEW is an invaluable asset for anyone interested in computer and machine vision. The demand for these skills is growing, and mastering this will give you a strong foundation to adapt to other tools like OpenCV, Matlab, or SimpleCV.

Content and Overview:

This course consists of 26 lectures totaling over 4 hours of content, designed to take you from zero to hero in computer vision concepts and image processing algorithms using LabVIEW. Each chapter ends with practical exercises to help you apply what you've learned by creating your own vision-based apps.

Practical Applications You Will Master:

1. Counting M&Ms in an Image 🍫
2. Color Segmentation and Tracking 🎨
3. Coin Blob Detection 💰
4. Blob Range Estimation 📏
5. Lane Detection and Ruler Width Measurement 🛣️
6. Pattern or Template Matching to Detect Complex Objects 🔍
7. Object Tracking Over Time 🎬
8. Bar code Recognition ☑️
9. Optical Character Recognition (OCR) 📝

By the end of this course, you'll have a deep understanding of how to apply these algorithms in real-world scenarios and be equipped with the knowledge to create functional image processing apps.

What You Get:

• Working files, datasets, and code samples to follow along with.
• A verifiable certificate of completion.
• Full Udemy 30 Day Money Back Guarantee for a risk-free learning experience.

📚 Join us in this journey to master Computer Vision and Image Processing with LabVIEW! 🚀

See you inside the course, where we'll turn your interest into expertise. Let's embark on this exciting learning adventure together!

📚 Course Overview

Rating: 4.40/5 (Based on recent reviews)

Course Highlights and Pros

✅ Comprehensive Applications: Learners appreciated the course for providing practical examples and applications of image processing and computer vision with LabVIEW, which are highly valuable for beginners. (Review 1 & Review 6)

✅ Beginner-Friendly: The course is considered suitable for those new to LabVIEW, offering a gentle introduction to the subject matter. (Review 2 & Review 7)

✅ Practical Orientation: Some reviews suggested that real-time applications or recorded videos implementing the examples would greatly enhance understanding and practical application. (Review 2)

✅ Resourceful: The resources provided in the course were commended for being clear and helpful in grasping the concepts. (Review 8)

✅ Interesting Topics: The application of computer vision to automated vehicles and Advanced Driver-Assistance Systems (ADAS) was found particularly engaging by some users. (Review 9)

✅ Rare Content: The course is recognized for filling a gap in the market, as high-quality self-paced content on LabVIEW-based machine vision and image processing is scarce. (Review 10)

🔧 ### Areas for Improvement

❌ Depth of Explanation: Some users felt that the course lacked depth, especially when complex subprogramas were introduced without proper development context. (Review 11)

❌ Over-Reliance on Slides: The overuse of slides and insufficient practical hands-on exercises was a point of criticism. Users suggested more in-depth explanations and exercises. (Review 4 & Review 12)

❌ Basic Level Content: A few reviews indicated that the content was too basic or on an elementary level, which may not be ideal for intermediate or advanced learners. (Review 3 & Review 13)

❌ Lack of Detailed Theory and Background: Some users felt that the course did not provide enough theoretical background or detailed explanations of the theory behind the practical applications. (Review 2 & Review 14)

❌ Incomplete Exercises: The absence of complete exercises, especially for designing real-time systems, was another area where users felt the course could improve. (Review 5)

❌ Miscellaneous Concerns: Some learners pointed out that 70% of the content did not discuss LabVIEW, which they found disappointing and a waste of their time. (Review 13)

Learner Experience Summary

The majority of learners found the course to be highly recommendable for those interested in learning about machine vision and image processing using LabVIEW. It is appreciated for its practical approach and introduction to tools and applications. However, some learners expressed that more in-depth theory, detailed explanations, hands-on exercises, and real-world applications would enhance the learning experience. The course is particularly valuable for beginners and those who have limited resources for self-paced learning on this topic. Despite these areas for improvement, the overall sentiment appears to be positive, with many learners indicating that they found value in the course content.