**6. Future of machine vision measurement technology**

The goal of machine vision research is to provide computers with humanlike perception capabilities so that they can sense the environment, understand the sensed data, take appropriate actions, and learn from this experience in order to enhance future performance. The field has evolved from the application of classical pattern recognition and image processing methods to advanced techniques in image understanding like model-based and knowledge-based vision.

In recent years, there has been an increased demand for machine vision systems to address "real-world" problems. The current state-of-the-art in machine vision needs significant advancements to deal with real-world applications, such as agricultural navigation systems, target recognition, manufacturing, photo interpretation, remote sensing, etc. It is widely understood that many of these applications require vision algorithms and systems to work under partial occlusion, possibly under high clutter, low contrast, and changing environmental conditions. This requires that the vision techniques should be robust and flexible to optimize performance in a given scenario (Sebe et al., 2005 ).
