Visual algorithm engineers have to learn the contents of the mathematical foundations, programming languages, computer vision fundamentals, deep learning algorithms, machine learning algorithms and so on.
1, mathematical foundations: including linear algebra, calculus, probability theory, statistics, etc., these mathematical foundations are the basis for a deep understanding of computer vision algorithms.
2, programming language: master at least one programming language, such as Python, C++, etc., and skillfully use the programming language for algorithm implementation.
3, basic computer vision: understand the basic concepts of computer vision, such as image processing, image segmentation, target detection, object tracking, three-dimensional reconstruction.
4, deep learning algorithms: familiar with deep learning algorithms, such as convolutional neural networks, recurrent neural networks, etc., and be able to use deep learning algorithms to perform tasks such as image categorization, target detection, and semantic segmentation.
5, machine learning algorithms: understanding of machine learning algorithms, such as support vector machines, decision trees, etc., able to use machine learning algorithms to solve.
An algorithm is a clear set of instructions for solving a problem; if an algorithm is flawed or unsuitable for a problem, executing it will not solve the problem. Different algorithms may accomplish the same task in different amounts of time, space, or efficiency.
Understanding the Development and Implementation of Algorithms and the Difference between Algorithms in Computer Vision
1. Developing algorithms in computer vision refers to the research and design of new algorithms to solve problems in the field of computer vision. This process includes theoretical research, algorithm design, algorithm implementation and experimental verification, and requires deep knowledge of mathematics, computer science and physics and other disciplines.
2. Implementing algorithms in computer vision refers to implementing existing algorithms into a computer program that realizes the function of the algorithm. This process is concerned with the implementation and optimization of the program and requires knowledge of computer programming and computer systems.
3. The difference between the two is that algorithm development focuses on the research and design of the algorithm, and the implemented algorithm may not be the final solution; whereas algorithm implementation focuses on implementing the algorithm into a runnable program and optimizing its performance to achieve the purpose of the final solution.