Depth Image Rotation for Sensor Tilt Correction
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Solution Overview
Problem
Existing depth image processing technologies face accuracy issues in recognizing object motion due to relative tilts between the image sensor and the target object, leading to decreased recognition and tracking performance.
Innovation Solution
An apparatus and method that include a depth image obtainer, object image extractor, relative angle calculator, object image rotator, and refiner to calculate and adjust the relative angle between the image sensor and the target object, rotating the object image to a reference angle and refining it by filling empty spaces using interpolation or filtering methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the image sensor photographs the target object at a tilted angle, then the field of view is expanded, but the object recognition accuracy deteriorates due to relative tilt between sensor and object
Solution Approach 1:
The system performs preliminary angle calculation between the image sensor and target object before recognition processing, then pre-rotates the depth image to correct the tilt. This preliminary correction ensures that subsequent recognition algorithms work with properly oriented images, maintaining high accuracy even when capturing at tilted angles for expanded field of view
Solution Approach 2:
The system dynamically changes the rotation angle parameter of the depth image based on the calculated relative angle between sensor and object. By adjusting this parameter, the system transforms the tilted image into a properly oriented one, resolving the contradiction between expanded field of view and recognition accuracy
2Measurement precision
If the depth image is rotated to correct the relative angle, then the object recognition accuracy is improved, but empty spaces are created in the rotated image
Solution Approach 1:
The system converts the harmful effect of empty spaces created by rotation into a beneficial process by applying interpolation algorithms. These algorithms analyze surrounding pixel data to intelligently fill the empty regions, transforming the information loss problem into an opportunity for noise reduction and image refinement
Solution Approach 2:
The interpolation process acts as an intermediary between the rotated image with empty spaces and the final complete image. This intermediary step generates pixel values for empty regions based on surrounding context, effectively bridging the gap created by rotation without requiring additional physical data
3Measurement precision
If the relative angle calculation uses complex methods like principal component analysis, then the angle estimation accuracy is improved, but the processing complexity increases
Solution Approach 1:
The system implements a dynamic angle calculation approach that adapts to different scenarios. It can switch between simpler methods (suitable for stable environments) and more complex methods like principal component analysis (suitable for dynamic or complex scenes), optimizing the balance between accuracy and processing complexity based on real-time conditions
Solution Approach 2:
The angle calculation process is segmented into multiple stages: initial coarse angle estimation using simpler methods, followed by refined angle calculation using more complex methods only when necessary. This segmentation allows the system to achieve high accuracy while minimizing overall processing complexity by applying complex algorithms selectively
Data Source
AI summary
An apparatus for processing a depth image using a relative angle between an image sensor and a target object includes an object image extractor to extract an object image from the depth image, a relative angle calculator to calculate a relative angle between an image sensor used to photograph the depth image and a target object corresponding to the object image, and an object image rotator to rotate the object image based on the relative angle and a reference angle.