Display Parameter Adjustment for Patient Feature-Point Detection
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Solution Overview
Problem
Inaccurate detection of feature points in patient images due to varying lighting conditions, such as dark images or backlit environments, and difficulty in identifying vital sign detection areas, especially with dark skin tones or tilted faces, hinder effective telemedicine diagnostics.
Innovation Solution
A display device that adjusts display parameters based on image analysis, identifies feature points and an omega shape to correct image brightness and posture, and communicates with external devices for illuminance adjustments to enhance feature point detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image capture is performed in varying lighting conditions, then telemedicine diagnosis can be conducted remotely, but feature point detection accuracy deteriorates due to dark or backlit images
Solution Approach 1:
The system performs preliminary image brightness correction and preprocessing before feature point detection. By adjusting display parameters and correcting image brightness in advance, the system ensures that feature points can be accurately detected even when the original image was captured in dark or backlit conditions
Solution Approach 2:
The system changes image display parameters such as brightness, contrast, and gamma correction to optimize the visual presentation of corrected images. This allows feature points to be more clearly visible and detectable across varying lighting conditions without requiring perfect original image quality
2Adaptability or versatility
If feature point detection is performed on dark skin tones or tilted faces, then telemedicine can serve diverse patients, but identification of vital sign detection areas becomes inaccurate
Solution Approach 1:
The system applies localized brightness correction to specific regions of the image, particularly focusing on the facial area where feature points and vital sign detection areas are located. This targeted approach ensures that dark skin tones and tilted faces receive appropriate brightness enhancement in the relevant regions without over-processing the entire image
Solution Approach 2:
The system uses detected feature points and omega shapes to provide feedback for iterative refinement of the detection process. By continuously refining the identification of facial landmarks and vital sign areas based on detected features, the system improves accuracy for diverse patient presentations including dark skin tones and tilted faces
3Measurement precision
If image brightness is adjusted to correct dark or backlit conditions, then feature point detection improves, but image display parameters become complex
Solution Approach 1:
The system automatically detects lighting conditions and self-adjusts display parameters without requiring manual intervention. The processor analyzes the captured image characteristics and autonomously applies appropriate brightness correction and display parameter adjustments, simplifying the user experience while maintaining detection accuracy
Data Source
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AI summary
A display device includes: a display; communication circuitry configured to communicate with an external device; and at least one memory storing one or more instructions; and at least one processor configured to execute the one or more instructions, wherein the one or more instructions, when executed by the at least one processor, cause the display device to: display an image on the display based on image information received through the communication circuitry, identify, in the image, one or more feature points of a patient and an omega shape of the patient, identify, based on the one or more feature points and the omega shape, whether the image may include a predetermined detection area associated with biometric information of the patient, and based on identifying that the image may include the predetermined detection area, adjust a display parameter of at least one of the display or the image.