Image Editor Occlusion Detection and Cropping
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Solution Overview
Problem
Modern consumer electronic devices often capture unintended occlusions, such as operator fingers or clothing, due to their small form factor, leading to the need for an automatic editing system that can detect and remove these from image and video data.
Innovation Solution
An automated image editor that detects occlusions by analyzing motion, blurriness, location, and brightness, and redefines the image area to remove these occlusions while maintaining the aspect ratio and maximizing the visual importance of the remaining content, using a processor and memory system to perform cropping operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If the device uses a small form factor housing, then the device is more compact and portable, but operator fingers and clothing may accidentally occlude the optical path between the camera system and the intended subject
Solution Approach 1:
The system performs preliminary occlusion detection by analyzing motion, blurriness, location, and brightness characteristics of detected objects before final image processing. This preliminary identification allows the system to proactively remove occluding elements from the captured image data, preventing them from degrading the final image quality.
Solution Approach 2:
The system extracts and removes occluding objects from the captured image data by identifying regions with occlusion characteristics (motion patterns, blur, location, brightness) and excising those regions from the final image, thereby separating the harmful occlusion elements from the desired subject content.
2Object-affected harmful factors
If the system crops the image to remove occlusions, then the occlusion is removed from the image, but the aspect ratio may be distorted or important content may be lost
Solution Approach 1:
The system transitions from simple rectangular cropping to a more sophisticated approach by defining a redefined image area that can have non-standard boundaries. This allows the system to remove occluding regions while preserving the overall aspect ratio by strategically selecting which regions to exclude and how to reframe the remaining content within the original aspect ratio constraints.
Solution Approach 2:
The system applies different processing qualities to different regions of the image. Occluded regions are completely removed, while regions containing important content are preserved and potentially enhanced. The cropping window is strategically positioned to maximize the preservation of high-importance content while excluding occluded areas, creating a locally optimized solution for each region.
3Measurement precision
If the system analyzes multiple parameters (motion, blurriness, location, brightness) to detect occlusions, then detection accuracy is improved, but processing complexity and time increase
Solution Approach 1:
The system implements a multi-parameter analysis approach where it evaluates motion, blurriness, location, and brightness characteristics to detect occlusions. By analyzing multiple parameters simultaneously, the system achieves higher detection accuracy and reduces false positives, as occluding objects typically exhibit specific patterns across multiple parameters rather than just one.
Data Source
AI summary
Techniques for cropping images containing an occlusion are presented. A method for image editing is presented comprising, when an occlusion is detected in an original digital image, determining an area occupied by the occlusion, assigning importance scores to different content elements of the original digital image, defining a cropping window around an area of the original digital image that does not include the area occupied by the occlusion and that is based on the importance scores, and cropping the original digital image to the cropping window.