3D Human Body Imaging with Mask-Guided Holographic Reconstruction
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Solution Overview
Problem
Existing 3D imaging methods using active multiple-input multiple-output (MIMO) millimeter waves for human body security inspection are inefficient due to the inclusion of non-relevant areas in the imaging frame, leading to slow image reconstruction and excessive computational resources.
Innovation Solution
A 3D imaging method that utilizes a depth camera to extract a human body mask, determines an imaging area based on the mask, and applies a fast backpropagation imaging algorithm to reduce the imaging area and computational load, using a MIMO antenna array to collect holographic data for reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a rectangular frame containing the human body is used as the imaging area, then the entire scanning range is covered, but the image reconstruction speed is slow and computational resources are excessive due to including useless pixel points
Solution Approach 1:
The patent segments the imaging area from the complete rectangular scanning frame by extracting a mask of the human body and its surrounding area. This segmentation isolates the relevant imaging region containing the human body mask from the entire rectangular frame, excluding useless pixel points that do not contribute to detecting dangerous articles. The segmented imaging area is then used for image reconstruction, maintaining measurement precision while improving reconstruction speed by reducing the number of pixels to be processed.
2Measurement precision
If all holographic data is used for each pixel reconstruction in the rectangular frame, then complete coverage is achieved, but the computational amount is huge resulting in wasted time and resources
Solution Approach 1:
The patent extracts the useful subset of holographic data corresponding only to the imaging area containing the human body mask, rather than processing all holographic data for the entire rectangular frame. By taking out only the relevant data portion associated with the segmented imaging area, the system maintains detection accuracy for dangerous articles while significantly reducing computational resource consumption and processing time.
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
This approach reduces computational resources and improves image reconstruction speed by focusing on relevant areas and utilizing a MIMO array with a fast backpropagation algorithm, resulting in high-quality images with fewer dead angles and noise.
Implementation Method 1
a 3D information obtaining step for generating 3D image information by capturing a 3D capture area containing a detected object using a depth camera; an imaging area determining step for determining an imaging area associated with the detected object based on the mask of the detected object; a holographic data obtaining step for collecting data on a holographic data collection area containing the detected object by a holographic data collection device, to generate holographic data
Implementation Method 2
The backpropagation imaging algorithm comprises: calculating, for a pixel point in the imaging area, a sum of distances from the pixel point to each pair of the transmitting and receiving antennas; obtaining a complex reflectance of each pixel point in the imaging area by performing an operation of summing for all of the transmitting and receiving antennas, as well as transmission frequencies of the millimeter waves, based on the holographic data received by the receiving antenna and the sum of distances
Data Source
AI summary
The present application relates to a three-dimensional imaging method and apparatus, and a 3D imaging device. The method comprises generating 3D image information by capturing a 3D capture area containing a detected object using a depth camera; extracting a mask of the detected object from the 3D image information; determining an imaging area associated with the detected object based on the mask of the detected object; collecting data from a holographic data collection area containing the detected object by a holographic data collection device, generating holographic data; and performing image reconstruction on the imaging area based on the holographic data.