In my opinion, the implementation of computer vision is inseparable from the support of image processing, which in turn relies on the effective application of pattern recognition methods. Pattern recognition, as a key branch of artificial intelligence, is closely related to machine learning, which is the core means of realizing artificial intelligence. Looking at this technological ecosystem, each link actually forms a layered, closely interconnected technological ecosystem. It is within this structure that the success of computer vision applications ultimately serves the optimization and implementation of machine learning models. These interdependent and complementary elements jointly drive the continuous development of the perception capabilities of intelligent systems.

Computer vision aims to use computers to simulate human vision, enabling perception and understanding of the external environment. Specifically, it uses cameras and computing devices to replace the human eye to detect, identify, track, and measure objects. It also processes and converts image signals to make them more suitable for human observation or machine analysis. This field is dedicated to researching relevant theories and technologies to build intelligent systems capable of extracting valuable information from images or multidimensional data.

The ultimate goal of computer vision is to empower computers and robots with human-level visual perception capabilities. Achieving this goal requires integrating multiple processing technologies, including image signal processing, texture and color modeling, geometry and spatial reasoning, and object modeling and semantic understanding. A truly effective vision system must tightly integrate these processes to achieve end-to-end intelligent processing, from pixel to perception.