Image Depth Information for Illumination Boundary Identification
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Solution Overview
Problem
Current computer technologies face challenges in accurately distinguishing between illumination boundaries and material boundaries in images, which is crucial for representing physical phenomena in the visual world.
Innovation Solution
The method utilizes image depth information from stereo imagery to identify and differentiate between illumination and material boundaries by analyzing spectral ratios and disparity maps, employing tokens and Nth order tokens to improve accuracy and efficiency in image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to distinguish illumination and material boundaries, then the processing is simpler, but the accuracy of boundary identification deteriorates
Solution Approach 1:
The image processing is segmented into multiple stages: token generation from image data, token region graph construction, illumination boundary identification using spectral ratios, and material boundary identification. This segmentation allows complex processing to be broken down into manageable steps, improving accuracy without overwhelming system complexity
Solution Approach 2:
Token structures are introduced as intermediary representations between raw image data and boundary identification results. Tokens capture local image characteristics and are used as intermediaries in the graph structure, enabling more accurate boundary detection while managing computational complexity through hierarchical processing
2Reliability
If depth information from stereo imagery is incorporated, then the accuracy of physical phenomena representation is improved, but the data processing complexity increases
Solution Approach 1:
Depth information from stereo imagery is merged with color image data during token generation. The token structure integrates both depth and color information, allowing simultaneous processing of multiple data types to improve physical phenomena representation without requiring separate processing pipelines
Solution Approach 2:
The patent transitions from 2D image processing to 3D processing by incorporating depth information as an additional dimension. This is achieved through stereo imagery processing that generates depth maps, which are then integrated into the token structure, enabling more accurate representation of physical phenomena in three-dimensional space
3Measurement precision
If spectral ratios and disparity maps are analyzed in detail, then the differentiation between illumination and material boundaries is improved, but the computational time increases
Solution Approach 1:
Tokens are generated in advance as preliminary processing structures that capture essential image characteristics. These pre-computed tokens are then reused in boundary identification, avoiding redundant calculations and reducing overall computational time while maintaining accuracy in spectral ratio and disparity map analysis
Solution Approach 2:
The analysis focuses computational effort on local regions where boundaries are likely to occur, using token region graphs to identify and prioritize areas of interest. This localized approach to spectral ratio and disparity map analysis improves boundary differentiation accuracy while reducing unnecessary computations in uniform regions
Data Source
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Figure 3A
AI summary
In a first exemplary embodiment of the present invention, an automated, computerized method is provided for determining illumination information in an image. According to a feature of the present invention, the method comprises the steps of identifying depth information in the image, identifying spatio-spectral information for the image, as a function of the depth information and utilizing the spatio-spectral information to identify illumination flux in the image.