Increasing operator performance on image analysis
Increasing operator performance on image analysis
How Human Perception Shapes Image Analysis Performance
In this blog, Femke Lettinga, Senior Consultant, explores how human perception particularly color and shape recognition, affects security operators' image analysis. The blog unveils cognitive factors that must be accounted for in training and interface design.
Authors
Femke Lettinga
Senior Consultant
Femke@pointfwd.com
Blog highlights
Color cues: blue for dense items (e.g. metal), orange for organic, green for mixed materials. Hue differences affect perception.
Shape perception: contours and Gestalt principles shape object identification, with CT’s transparency adding complexity.
Design and training must align with perceptual realities, ensuring image clarity, interface usability, and effective operator performance.
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Airport security stakeholders have the desire to create a seamless passenger flow, to relieve passengers from perceived stress during security, and most importantly to maintain a high security level. To achieve this, factors like type of screening equipment, number of security lanes, a (dynamic) staffing model and a differing passenger type are of influence. One critical piece in the puzzle towards an improved screening process still is the operator and its performance in human-essential tasks like x-ray image analysis. But how to achieve and maintain high performance standards in screening for prohibited objects?
Whereas our previous articles on the transition to EDS CB with CT machines focused on the component of initial training, in this blog post we share our experience on how image analysis can increase in performance. We look at three factors – Human Perception, Checkpoint Environment and CONOP & legislation - that influence screener performance the most and start in this blog with the influence of Human Perception.
The influence of human perception on operator screening performance
Operators evaluate images based on shape and color. The shape gives information about the type of object and the color presents information on the type of material. Together, shape and color provide the information that is needed to identify an object.
The perception of object color
How is an understanding of perception of object color relevant related to operator performance on image analysis? The colors that are presented in X-ray and CT images are shown with a saturation level corresponding to the density of the material. The color distinction between organic and metal material is easy to detect. However, the difference between blue and green is less distinctive because the hue level difference is much smaller. Combinations of colors can cause ‘blurred’ images, making it more difficult to detect the contours of objects. The result; operators must use the functionalities of the operator interface or that they are unable to identify objects and therefore reject the image.
Although the colors (hue) for the different material types are the same for most machine suppliers, there are differences that influence the image quality, such as color saturation and brightness. Specifically related to the 3D image, the colors that are represented for metal and combined material are much closer to each other in hue, but also due to the transparency of the image which brings less saturation and less brightness.
Perception of object color thus plays an essential role in the image quality. And image quality has a significant impact on the operator performance. It is therefore key to adjust the operator training accordingly to the specific operator interface. More about this topic in the next blog on the impact of the Checkpoint Environment on operator performance of image analysis.
The perception of object shape
Our hypothesis: it is more difficult to identify a clear image in comparison to the identification of a threat. To detect shapes it is important to understand how we perceive contours that outline and define the shape of any particular form. The feature detection theory explains we do so through specialized detector cells that respond to certain characteristics of the image and to no others. Some detectors respond to curves, others to straight edges, some to contours angled upward others to contours angled downward and so forth.
Gestalt principles explain how similarity, proximity, continuity and closure influence our perception. Law of continuity and law of closure are most applicable regarding image analysis. Operators must be able to identify all objects prior to decision-making. Analyzing CT images is more challenging in this perspective because lines can merge and influence the perception of the shape. Therefore, it could be stated it is more difficult to identify a clear image in comparison to the identification of a threat.
The conclusion can be demonstrated by a quick exercise of Neissers’ (1964) research on visual search. In this research participants were presented pages of characters, 50 lines of printed letters, with four to six letters per line. Their task was to scan the page as rapidly as possible to find the one occurrence of a specific letter. In other tasks people were asked to find the line without a certain character.
Test yourself:
Use lists below of letters to identify the lines that is to be searched for. make sure to time as you find the targets. Notice how hard it is to find a line without a specified letter and to find a letter that is physically similar to the distractor letters in the display.
Be aware of human perception for enhancement
Understanding how image analysis performance is being affected by human perception, by factors such color and shape, provides for insights that can be helpful in operation. Being aware of the impact can for example help to facilitate optimal development and execution of operator training programs.
It furthermore provides help for airports and security companies looking to critically assess deployed computer based training software and various manipulation functionality, or have knowledge prior to acquisition of such applications. It might even help to further structure on-the-job training, as well as CONOP and SOP definition.