Image Depth Estimation for Consistent Feature Filtering
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Solution Overview
Problem
Automated image editing tools often produce 'false positives' when filtering image data, leading to undesirable artifacts by assigning different filtering parameters to different elements of a common image feature, such as a human face, resulting in inconsistent image adjustments.
Innovation Solution
The technique involves identifying Regions of Interest (ROIs) within an image, assigning a base depth to one or more of these ROIs, and applying parameter gradients to other image content based on their relative depth and spatial distance from the ROI, ensuring consistent filtering and adjustment across image elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated filtering tools are applied to image data, then filtering efficiency is improved, but false positives occur leading to inconsistent adjustments on common image features
Solution Approach 1:
The patent segments the image into multiple depth layers using depth estimation techniques, allowing different filtering parameters to be applied to different depth segments. This resolves the contradiction by enabling automated filtering while maintaining consistency within each depth segment, preventing false positives on common features that span multiple segments.
Solution Approach 2:
The patent changes the parameter space by introducing depth estimation as an additional dimension for controlling filtering behavior. By adjusting filtering parameters based on depth layer assignments, the system achieves both automated processing efficiency and adjustment consistency, as elements at the same depth receive consistent treatment regardless of their spatial location.
2Adaptability or versatility
If different filtering parameters are assigned to different portions of an image, then localized control is improved, but artifacts appear when filtered and unfiltered portions belong to common elements
Solution Approach 1:
The patent adds a depth dimension to the traditional two-dimensional image space, creating a three-dimensional parameter space for filtering control. By assigning elements to depth layers based on their spatial and contextual relationships, the system maintains localized control where needed while preventing artifacts through consistent depth-based parameter assignment across element boundaries.
Solution Approach 2:
The patent introduces depth estimation as an intermediary layer between the image content and filtering parameters. This intermediary assigns depth values to image elements, which then serve as the basis for consistent parameter assignment, mediating between localized control requirements and artifact prevention by ensuring that common elements receive uniform treatment through their shared depth classification.
Data Source
AI summary
Techniques are described for automated analysis and filtering of image data. Image data is analyzed to identify regions of interest (ROIs) within the image content. The image data also may have depth estimates applied to content therein. One or more of the ROIs may be designated to possess a base depth, representing a depth of image content against which depths of other content may be compared. Moreover, the depth of the image content within a spatial area of an ROI may be set to be a consistent value, regardless of depth estimates that may have been assigned from other sources. Thereafter, other elements of image content may be assigned content adjustment values in gradients based on their relative depth in image content as compared to the base depth and, optionally, based on their spatial distance from the designated ROI. Image content may be adjusted based on the content adjustment values.