Camera with Blood Optical Imaging Algorithm Module
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Solution Overview
Problem
Traditional blood vessel detection methods face challenges in obtaining high-quality, dynamic images due to the loss of detail information from digitization and compression processes in camera output, leading to insufficient imaging accuracy.
Innovation Solution
A camera with blood optical imaging function, comprising a lens, image sensor, main control module, and blood optical imaging algorithm module, which uses AI chip control for exposure, white balance, and aperture control, and employs a specific algorithm to extract dynamic blood vessel images from RGB signals, ensuring accurate and detailed imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera output with digitization and compression is used, then the device complexity is reduced, but the loss of detail information increases leading to insufficient imaging accuracy
Solution Approach 1:
The patent extracts only the necessary blood vessel image information from the full-color RGB image data captured by the camera. The blood vessel image extraction unit processes the complete original image data to isolate and extract blood vessel images, obtaining the required diagnostic information while avoiding unnecessary data transmission and processing of non-blood vessel areas.
Solution Approach 2:
The patent performs blood vessel image extraction directly at the camera end before image transmission. The blood vessel image extraction unit processes the original RGB image data captured by the image sensor to extract blood vessel images in advance, so that only the extracted blood vessel images need to be transmitted and processed further, rather than transmitting complete high-resolution images for later processing.
2Measurement precision
If complete original RGB data is transmitted and processed, then the imaging accuracy is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the image processing function into two parts: the camera captures complete original RGB image data, and a separate blood vessel image extraction unit processes this data to extract blood vessel images. This segmentation allows the camera to focus on high-quality data capture while the extraction unit handles the complex processing, distributing the computational burden.
Solution Approach 2:
The patent introduces a blood vessel image extraction unit as an intermediary between the camera's image sensor and the final output. This extraction unit acts as a mediator that receives complete original image data, processes it to extract blood vessel images, and outputs the refined results, thereby improving imaging accuracy without requiring the entire system to handle complete high-resolution data throughout.
3Productivity
If post-processing of camera output is used, then the device complexity is reduced, but the productivity and completeness of dynamic blood vessel imaging decreases
Solution Approach 1:
The patent performs blood vessel image extraction in advance at the camera end before transmission. The blood vessel image extraction unit processes the original image data to extract blood vessel images preliminarily, so that subsequent processing and transmission involve only the extracted images rather than complete high-resolution images, improving efficiency without sacrificing detail.
Solution Approach 2:
The patent extracts blood vessel image information from the complete original image data at the camera end using the blood vessel image extraction unit. This extraction allows the system to focus computational resources on processing only the relevant blood vessel information rather than processing entire high-resolution images, improving productivity for dynamic imaging.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The camera achieves improved detection accuracy and quality by directly acquiring original RGB data, enabling precise recording of fine pixel changes and remote detection of moving blood optical images.
Implementation Method 1
When visible light irradiates on the skin, the hemoglobin in the subcutaneous vessels will absorb some of the light rays, and thus, when the blood volume in the vessels changes, the intensity of light rays as absorbed will change correspondingly, causing a corresponding change to the intensity of light rays reflected by the skin.
Data Source
AI summary
A camera having blood optical imaging function, comprising: a lens, an image sensor, a main control module, a blood optical imaging algorithm module and an IP network interface module; the lens is located at the image acquiring position of the camera; the output end of the image sensor are in electric connection with input ends of the main control module and the blood optical imaging algorithm module, respectively; the main control module is in electric connection with the blood optical imaging algorithm module and the IP network interface module, respectively. The blood optical imaging algorithm is directly applied to the camera, so that the algorithm module can directly acquire original RGB data from the sensor, improving the accuracy of detection and the quality of blood vessel imaging, and achieving remote detection of the blood optical images of a face, while detecting the blood optical images of a moving face.