Opencv learning tutorials <1. Introduction>

Opencv is a free and open source C/C ++ library developed by Inter, Which is specialized in image processing and machine vision. Its full name is open source computer vision.

1. visual language MATLAB and opencv can be used for image processing. Which one is better?

MATLAB is a visual mathematical Processing Toolbox for scientific research in colleges and universities. opencv is widely used in image-related project R & D. If you want to do the pattern recognition and machine vision, my mentor suggested that you learn both well, no matter whether you are posting paper or working on a project.

2. opencv is a function library based on C/C ++. What are the advantages of opencv?

Libraries written in C are fast to execute and focus on real-time applications. In books, they can take full advantage of multi-core processors and provide interfaces for python, Matlab, and Ruby. In addition, machine vision is closely related to machine learning, so opencv also provides MLL (Machine Learning Library ),

3. What can we do with opencv?

Video Monitoring, special behavior detection of groups, satellite maps and electronic map stitching, pipeline operations, image calibration, medical image processing, etc.

4. What is computer vision?

Simply put, computer vision is to transform some static images or video files into decision-making or a new expression. For example, "a pair of jitters" or "a laser scan of 1 meter", after conversion, we get a clear picture "or" three people entering the room ".

5. What are the bottlenecks or difficulties in the development of computer vision?

Camera Equipment: computer vision is based on image processing. image acquisition depends on the collection equipment. Therefore, electronic noise or accuracy in the equipment may affect data.

Natural Environment: illumination, reflection, motion, and other conditions

Uncertainty of building a 3D space under two-dimensional data: the shooting angle or distance will greatly affect the appearance of objects under the plane. This is the root cause of Computer Vision disease.

6. How can we overcome computer vision problems?

Decision-making problem: Find the target features or associated target features for the target; ignore unnecessary information based on the upper and lower texts;

Of course, the problem is actually encountered, not imagined.