What does computer vision need to learn?

What does computer vision need to learn?

Learning computer vision needs knowledge reserve;

1, knowledge of image processing. Image processing generally includes: optical imaging basis, color, filter, image local features, image texture, image matching and so on.

2. Stereo vision knowledge. Stereo vision generally includes camera geometric model, binocular vision, recovering object structure from motion, three-dimensional reconstruction technology and so on.

3. Knowledge of artificial intelligence. Artificial intelligence generally includes: scene understanding and analysis, pattern recognition, image search, data mining, deep learning and so on.

4. Other disciplines related to computer vision include machine vision, digital image processing, medical imaging, photogrammetry, sensors, etc.

With a solid foundation of deep learning, we can really enter the study of computer vision professional knowledge.

Data usage in deep learning

Data is the input of deep learning system, which plays a vital role in the landing of deep learning algorithm! If there is no arrangement of ImageNet data sets with more than one million images, the landing process of deep learning computer vision algorithm will definitely be delayed!

With the maturity of various basic CV algorithms, the key to determining whether the model can go online depends largely on the quality of data and the correct use of data! The difference between you and a big factory is often not the advanced algorithm, but the amount of data! But it is easy to be ignored, especially for learners who lack practical experience in the industry.