3D Image Analysis for Concealed Prohibited Object Detection
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Solution Overview
Problem
Current systems face difficulties in automatically detecting prohibited objects concealed within items, especially when they are obscured by other parts of the item, leading to reduced accuracy and efficiency in security checkpoints.
Innovation Solution
A system and method utilizing a processor and memory circuitry to generate a three-dimensional representation of an item, determine informative clusters of points, and use data on the position and orientation of these clusters to create a two-dimensional image that aids in detecting prohibited objects, even when they are concealed by other parts of the item.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image acquisition methods are used to detect prohibited objects, then the system is simple to operate, but the detection accuracy is reduced when objects are concealed by other parts of the item
Solution Approach 1:
The patent transitions from two-dimensional image analysis to three-dimensional representation processing. By acquiring multiple image slices at different depths and constructing a 3D representation with point clouds and intensity values, the system can detect concealed objects that are obscured in 2D views. The 3D spatial coordinates (x, y, z) and intensity information enable detection of objects hidden behind other item components.
Solution Approach 2:
The patent segments the 3D representation into multiple clusters of points, where each cluster corresponds to a potential object. By dividing the point cloud data into distinct clusters based on spatial proximity and intensity characteristics, the system can identify and analyze individual objects separately, improving detection accuracy while managing computational complexity through localized processing.
2Measurement precision
If multiple image slices are fused to detect concealed objects, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary clustering of points into informative clusters before detailed object analysis. By pre-organizing the 3D point cloud data into clusters based on spatial and intensity characteristics, the system prepares the data structure in advance, enabling faster subsequent processing and detection without requiring fusion of multiple image slices at detection time.
3Measurement precision
If traditional 2D image analysis is used, then processing is fast, but concealed objects made of difficult-to-identify materials cannot be detected
Solution Approach 1:
The patent applies local quality analysis by examining intensity characteristics and spatial distribution within each point cluster individually. Different materials exhibit distinct intensity patterns in the 3D representation, allowing the system to identify concealed objects made of difficult-to-detect materials by analyzing local intensity properties rather than requiring global image fusion, thus maintaining processing speed while improving material identification accuracy.
Data Source
AI summary
There are provided systems and methods comprising obtaining an image of at least a part of an item, obtaining data informative of a pixel intensity of the part of the item in the image, obtaining first data informative of a pixel intensity in an image of a part of a first item associated with a prohibited material, wherein the part of the first item meets a similarity criterion with the part of the item, obtaining second data informative of a pixel intensity in an image of a part of a second item which is not associated with a prohibited material, wherein the part of the second item meets the similarity criterion with the part of the item, and using the first data, the second data and the data to determine whether the part of the item is associated with a concealed prohibited material in the image.