# Comparative Analysis of Measurement Tools in the Cognex D900 Vision System
## 2 Background and Literature Review
### Image Recognition Process
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Machine Vision** & **Computer Vision** \\ \hline \multirow{3}{*}{Definition} & A specialized application of computer vision for industrial use, focused on image processing and analysis based on predefined rules. & A broader field within AI, focused on automated image analysis across various domains. \\ \hline \multirow{2}{*}{Typical Applications} & Object inspection, defect detection, process control, robot guidance in industrial settings. & Object detection, image recognition, face detection, virtual and augmented reality. \\ \hline \multirow{3}{*}{System Components} & Typically includes a camera, lens, processor, and software integrated into a larger mechanical system. & Can operate independently with or without a camera, often processing stored or synthetic images. \\ \hline \multirow{2}{*}{Real-Time Processing} & Designed for real-time image capture and decision making. & Not necessarily real-time; can work with pre-recorded images. \\ \hline \multirow{2}{*}{Focus} & Specific features or critical parts of an object for rapid decision making. & Extraction and interpretation of comprehensive data from images or videos. \\ \hline \multirow{2}{*}{Relation to Each Other} & A practical subset of computer vision applied to industrial tasks. & The broader field that includes and encompasses machine vision. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key differences between machine vision and computer vision [13; 14; 15; 16; 17; 18; 19; 20]. learned from, allowing it to recognize and classify objects with a certain degree of accuracy. The effectiveness of this stage depends on the quality of the training data and the sophistication of the classification algorithms. * Interpretation: The interpretation stage involves analyzing the classification results to make sense of the recognized objects within the context of the application. For instance, in a quality control scenario, the system may evaluate whether a product meets the specified standards based on its classification. This step is where the system's decisions are contextualized, making the data meaningful and actionable for the intended application. * Action: Based on the interpretation, the system is then capable of taking appropriate actions. These actions can vary depending on the application, such as rejecting a defective product from the production line, sorting objects into different categories, or triggering alerts that signal the need for human intervention. The action stage is where the system's analysis directly influences real-world operations, ensuring that the appropriate response is taken based on the visual data. * Communication of results: Finally, the vision system communicates the results of its analysis and actions to other systems within the enterprise. This can include production management systems, databases, or user interfaces, ensuring that the relevant information is available to those who need it. The effective communication of results is essential for integrating the vision system's insights into broader workflows, allowing for seamless operation and coordination within the enterprise. Each of these steps can be more or less complicated depending on the complexity of the task and the specific requirements of the application. Modern vision systems often use advanced machine learning and artificial intelligence algorithms to improve the accuracy and reliability of the image recognition process. The general scheme of the image recognition method is shown in Figure 1.