Page Position Estimation via Spine Endpoint Tracking
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Solution Overview
Problem
Augmented reality systems face challenges in reliably estimating the position and orientation of fiduciary markers, especially when they become distorted during the turning of book pages, leading to disruptions in image augmentation and user experience.
Innovation Solution
The system employs fiduciary markings on each page of a book, using a video camera and entertainment device like the PlayStation 3 to detect and track these markings, and applies edge detection and Hough transforms to estimate the angular position of turning pages, even in conditions affected by motion blur, by analyzing regions centered on the book's spine endpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fiduciary markers are used for position estimation in augmented reality systems, then the system can track object position and orientation, but the markers become distorted during page turning which reduces tracking reliability
Solution Approach 1:
The patent divides the book page into multiple regions and uses multiple fiduciary markers distributed across different pages. When one marker is distorted during page turning, other markers on different pages remain visible and usable, allowing the system to maintain position estimation through the undistorted markers.
Solution Approach 2:
The patent introduces a virtual book model as an intermediary that represents the physical book's state. This virtual model tracks page positions and orientations through computational geometry rather than relying solely on direct visual detection of physical markers, allowing the system to infer marker positions even when they are distorted.
2Duration of action of moving object
If the system tracks fiduciary markers during page turning, then continuous position estimation is possible, but motion blur affects the accuracy of marker detection
Solution Approach 1:
The patent performs preliminary actions by pre-defining the expected positions and orientations of fiduciary markers on each page of the virtual book model. During tracking, the system compares actual marker positions against these pre-defined expectations, allowing it to compensate for motion blur and maintain accurate position estimation even when markers are not perfectly detected.
Solution Approach 2:
The system uses feedback from multiple sources including the virtual book model's expected marker positions, the current camera image, and the previously estimated book state. This multi-source feedback allows the system to correct for detection errors caused by motion blur and maintain continuous accurate tracking throughout the page turning process.
Data Source
Figure 1~2A
Figure 2B
Figure 3
AI summary
A method of estimating the angular position of a turning leaf in an image of a book comprises the steps of capturing a video image frame of the book, estimating the position of at least a first endpoint of the spine of the book, applying an edge detection operation to the video image frame of the book to generate an edge image, defining a first region having its lower boundary centred upon the first endpoint of the spine of the book, applying a Hough transform to the first region of the edge image to obtain a plurality of line estimates, and rejecting line estimates that do not substantially intersect with an estimated endpoint of the spine of the book; and for line estimates that are not rejected, detecting one or more clusters of angles of said line estimates with respect to an estimated endpoint of the spine of the book, and generating a respective average angle from the or each cluster of angles; then selecting a respective average angle from the available average angles, and estimating the angular position of the turning leaf in the image of the book responsive to the currently selected average angle.