2D Depth Imaging for Object Characteristic Distance
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Solution Overview
Problem
Existing 3D point cloud systems for object characterization are time-consuming and require costly, inconvenient hardware, necessitating more efficient and cost-effective methods for determining characteristic distances using 2D images.
Innovation Solution
A method involving accessing a 2D image with depth values, determining the object's contour, identifying an anchor pixel, and calculating the length of a target segment based on pixel lengths and depth values, utilizing machine learning and digital frameworks to refine the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D point cloud systems are used for object characterization, then measurement precision is improved, but device complexity and hardware costs increase
Solution Approach 1:
The patent uses 2D images as simplified copies of 3D point cloud data to achieve object characterization. Instead of requiring complex 3D scanning hardware, the system processes 2D image data containing depth information, which serves as a lighter alternative that maintains sufficient measurement precision for determining characteristic distances.
Solution Approach 2:
The patent replaces mechanical 3D scanning systems with a computational approach using 2D image processing. By substituting complex mechanical depth sensors with standard imaging devices combined with algorithmic depth extraction, the system reduces hardware complexity while preserving measurement capabilities.
2Measurement precision
If 3D point cloud processing is used, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential information needed for characteristic distance determination from the full 3D point cloud data. By identifying and processing only relevant features (contour points, anchor pixels, target segments) rather than the entire point cloud, the system achieves measurement precision while significantly reducing processing time.
Solution Approach 2:
The patent segments the object representation into discrete, processable units: contour extraction, anchor pixel identification, and target segment determination. This segmentation allows the system to process only necessary portions of the data structure, improving processing efficiency while maintaining measurement accuracy.
3Measurement precision
If specialized 3D hardware is used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent makes the system universal by using standard 2D imaging devices that are widely available and easy to operate, rather than specialized 3D hardware. The same imaging device can perform both 2D photography and 3D characterization tasks, improving ease of operation while maintaining measurement precision through computational methods.
Data Source
AI summary
Methods of and systems for determining a characteristic distance of an object-of-interest. The method includes accessing a two-dimensional (2D) image of the object-of-interest, the 2D image comprising a plurality of pixels, each pixel being associated with a depth value indicative of a distance between an image sensing device that captured the 2D image and an entity present in the 2D image on the pixel, determining a contour of the object-of-interest on the 2D image, the contour defining an outline of the object-of-interest on the 2D image, determining an anchor pixel of the object-of-interest based on the contour thereof, determining a target segment on the 2D image and determining a length of the target segment based on a pixel length thereof and the depth value of a given pixel of the 2D image, the length of the target segment being the characteristic distance of the object-of-interest.