In the rapidly evolving field of computer vision, the cost factor is pivotal in determining the feasibility and adoption of new technologies. Due to their prohibitive costs, cutting-edge computer vision solutions are often rendered impractical or "born dead". This is particularly true in scenarios where the computational resources or specialized hardware required are costly. High costs not only limit the accessibility of these technologies to a broader market but also stifle innovation by restricting research and development to well-funded entities. This creates a significant barrier to entry, preventing smaller companies and researchers from contributing to the field's advancement.
Optimization in computer vision, therefore, emerges as a game changer. By enhancing the efficiency of algorithms and reducing the need for expensive hardware, it democratizes access to advanced computer vision capabilities. Efficient algorithms can run on affordable hardware, broadening the scope of potential applications and users. This not only makes the technology more accessible but also spurs innovation by allowing a diverse range of players to participate in the development and application of computer vision.