Colored Artifact Detection in Background-Replaced Images
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Solution Overview
Problem
Existing image processing systems fail to effectively detect and remove colored artifacts resulting from background replacement processing, leading to unsatisfactory final image products.
Innovation Solution
A system and method for detecting colored artifacts by generating an image mask, identifying pixel changes, clustering changed pixels in a predetermined color range, and highlighting clusters exceeding a density threshold, allowing manual review and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If background replacement processing is performed on images, then the background color or scene can be changed to meet customer requirements, but colored artifacts are introduced in the final image product
Solution Approach 1:
The system performs preliminary detection of colored artifacts using image processing algorithms before the final image is delivered to the customer. By identifying and flagging artifacts in advance through automated analysis of color distributions and edge regions, the system enables preventive quality control rather than reactive correction.
Solution Approach 2:
The system provides feedback to photographers and quality control personnel by displaying detected colored artifacts with visual indicators and density metrics. This feedback loop allows for iterative review and correction of images, ensuring that artifacts are identified and addressed before final delivery.
2Reliability
If manual inspection of all images for colored artifacts is performed, then quality control is ensured, but time consumption increases
Solution Approach 1:
The system segments the image analysis process into automated detection and manual review components. Automated algorithms first screen all images for colored artifacts using color thresholding and density calculation, then only images exceeding the density threshold are forwarded for manual inspection. This segmentation dramatically reduces the manual inspection workload while maintaining quality control.
Solution Approach 2:
The system performs self-service quality screening through automated detection algorithms that independently analyze images for colored artifacts. The automated system calculates artifact density, compares it against predetermined thresholds, and makes initial triage decisions about which images require manual review, reducing the burden on human operators.
3Productivity
If automated detection algorithms are used to identify colored artifacts, then processing speed increases, but detection accuracy may decrease
Solution Approach 1:
The automated detection algorithm applies different analysis criteria to different regions of the image based on their visual characteristics. Edge regions and areas with high color saturation are analyzed more thoroughly, while uniform low-saturation regions are processed more lightly. This local quality approach maintains high detection accuracy in critical areas while preserving overall processing speed.
Solution Approach 2:
The system dynamically adjusts detection parameters such as color threshold values and density cutoffs based on the specific characteristics of each image. By adapting parameters to the image content rather than using fixed thresholds, the system maintains high accuracy across diverse image types while preserving automated processing efficiency.
4Productivity
If images with colored artifacts are sent to customers, then productivity is maintained, but customer satisfaction decreases
Solution Approach 1:
The system performs preliminary detection and classification of colored artifacts before image delivery. By automatically identifying images with artifact density exceeding the threshold and flagging them for review, the system ensures that only quality-approved images are sent to customers, maintaining both productivity and quality standards.
Solution Approach 2:
The system establishes a feedback mechanism where detected artifacts and manual review results are fed back into the delivery decision-making process. This feedback loop ensures that images with unacceptable artifact levels are rejected before delivery, while efficient automated triage maintains high overall delivery productivity.
Data Source
AI summary
Methods and systems for detecting colored artifacts on an image of subject are disclosed. An image may be received, wherein the image has undergone background replacement processing. The image may include colored artifacts as a result of errors in background replacement processing. A density of colored artifacts on the image may be determined, and in response to a determination that the image has a density of colored artifacts that exceeds a predetermined threshold, the image may be displayed on a user interface. The user interface may include a selectable element that, in response to being selected, causes the colored artifacts to be highlighted on the image displayed on the user interface. Colored artifacts on the image may then be corrected to improve the image quality before the image is sent to a customer as a final image product.