As an important type of process data, images have been widely used in user-interface design and process monitoring because they can perform non-invasive analysis on the processes or products with low costs. However, an important issue with images is that they usually contain redundant information which can interfere with searching for the target item. For example, the process diagrams exhibited in the user-interface usually contain a large amount of equipment such as tanks and columns, and it is difficult for the user to find target items such as the reactor. A related topic is how to measure the negative influence of excessive items on the target searching, and based on the result we can improve the user-interface design by reducing the number and changing dyes of items. Another example is related to the flare analysis where the multivariate image analysis (MIA) has been established and widely used . Isolating the flare flames in the images from the environment can be performed manually by visual detection. However, it is not quite efficient. Because the flare flames usually have distinct perceptual quality which makes them stand out from the environment, we can certainly apply advanced methods from computer science community to perform such a task automatically.
In this work, we apply methods including the visual saliency detection  and clutter analysis  from computer science community to analyze the process images to improve the user-interface design and facilitate the flare flames detection.
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