Depth Confidence Map Enhancement via Gradient Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision systems face challenges in generating accurate confidence maps for depth information, as they often misclassify reliable depth information as low confidence and vice versa, leading to inaccuracies in filtering out unreliable data.
Innovation Solution
The proposed solution enhances confidence maps by leveraging image and depth gradients, using threshold functions to identify smooth and non-smooth regions, and optimizing a matrix parameter to minimize errors, thereby improving the alignment of confidence levels with actual image edges and reducing false positives/negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate confidence maps, then the process is simple, but the accuracy of depth information classification deteriorates
Solution Approach 1:
The patent segments the confidence map generation process into multiple components: gradient calculation (image and depth), threshold function application to identify smooth/non-smooth regions, and matrix parameter optimization. This segmentation allows each component to be optimized independently, improving overall accuracy while managing complexity through modular processing steps.
Solution Approach 2:
The patent performs preliminary actions by calculating gradients and identifying smooth/non-smooth regions before final confidence map generation. By pre-processing the image and depth data to identify edge regions and smooth areas, the system prepares optimized input for the confidence calculation, improving accuracy of depth classification while structuring the complexity in a manageable sequence.
2Reliability
If confidence maps are generated to filter depth information, then unreliable data can be excluded, but false positives and negatives increase
Solution Approach 1:
The patent implements feedback by using the calculated gradients and identified smooth/non-smooth regions to adjust and optimize the matrix parameters in the confidence map generation. This feedback loop allows the system to learn from the gradient information and refine the confidence calculations, reducing false positives and negatives while improving the reliability of depth information filtering.
Solution Approach 2:
The patent changes parameters by optimizing the matrix used in confidence map generation based on gradient information and smooth region identification. By adjusting the matrix parameters to account for edge regions and smooth areas, the system improves the accuracy of confidence classification, thereby reducing false positives and negatives in depth information filtering.
3Measurement precision
If gradient analysis and optimization are performed, then confidence map accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by focusing gradient analysis and optimization only on critical regions such as edges and smooth areas, rather than uniformly processing the entire image. By concentrating computational resources on regions that most impact confidence map accuracy, the system improves precision while minimizing unnecessary processing time on less critical areas.
Data Source
AI summary
Methods, systems, articles of manufacture, and apparatus to enhance image depth confidence maps are disclosed. An apparatus includes a gradient analyzer to: generate an image gradient based on a digital image of a scene; and generate a depth gradient based on a depth map associated with the digital image. The depth map is to define depth values corresponding to pixels in the digital image. The apparatus further includes an enhanced confidence map analyzer to determine an enhanced confidence map for the depth map based on the image gradient, the depth gradient, and an initial confidence map for the depth map.