Light Source Detection via Shadow Silhouette Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision systems face challenges in detecting and analyzing distinct light or radiation sources in a scene, which is crucial for augmented reality applications where virtual objects need to interact realistically with the environment, including casting shadows similar to real objects.
Innovation Solution
A method and system that involve sensing images of a scene, maintaining a 3D database of object positions, identifying candidate silhouettes, and deriving properties of distinct light sources based on this data, including location, structure, color, luminance, and directionality, to accurately determine the characteristics of light sources affecting the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision systems detect light sources by analyzing shadows, then the accuracy of light source detection is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the shadow analysis process into distinct steps: detecting shadow regions in images, extracting silhouette contours, comparing silhouettes with 3D model projections, and identifying light source characteristics. This segmentation allows each step to be optimized independently while reducing overall system complexity through modular processing.
Solution Approach 2:
The patent uses 3D model projections as an intermediary between the 2D shadow images and the 3D light source characteristics. By projecting 3D objects onto 2D planes and comparing these projections with detected shadow silhouettes, the system indirectly infers light source properties without direct measurement, simplifying the detection process.
2Loss of information
If the system analyzes multiple light sources and their shadows, then the completeness of scene analysis is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing 3D model projections and storing them in a database before actual shadow analysis. During processing, the system only needs to compare detected silhouettes against pre-computed projections, significantly reducing real-time processing time while maintaining complete scene analysis.
Solution Approach 2:
The patent creates 2D projections of 3D models as copies that can be directly compared with detected shadow silhouettes. These projections serve as reference templates that enable rapid identification of light source characteristics without complex real-time 3D rendering, reducing processing time while preserving analysis completeness.
3Manufacturing precision
If the system determines light source properties such as location, structure, color, luminance and directionality, then the accuracy of virtual object rendering is improved, but the computational resources required increase
Solution Approach 1:
The patent replaces complex computational rendering systems with a simpler shadow analysis approach. Instead of calculating complete light field simulations, the system analyzes shadow silhouettes and 3D projections to directly determine light source properties, reducing computational resource requirements while maintaining rendering accuracy.
Solution Approach 2:
The patent changes the approach from computing complete lighting simulations to extracting specific geometric parameters from shadow silhouettes. By focusing on silhouette shape, position, and size rather than full radiometric simulation, the system obtains sufficient parameters for accurate virtual object rendering with reduced computational cost.
Data Source
AI summary
A method and a system for analyzing distinct light or radiation sources affecting a scene are provided. The method may include: sensing at least one image of a scene containing surfaces and objects, wherein the scene is illuminated by at least one distinct radiation or source; maintaining a database of the scene which stores approximate positions of at least some of the objects in the scene; identifying at least one candidate silhouette suspected to be cast by the at least one distinct light or radiation source, deriving properties of the at least one distinct light or radiation source, based on the at least one identified silhouette, based on data derived from the database.