Mobile Image Stabilization via Corner Dispersion Analysis
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Solution Overview
Problem
Capturing high-quality images of rectangular objects using mobile device cameras is challenging due to stabilization issues and distortion, making it difficult to automatically capture clear images of documents or cards without user intervention.
Innovation Solution
The method involves capturing low-resolution images, detecting edges to form a quadrangle, calculating corner coordinates, assessing dispersion, and automatically capturing high-resolution images when stabilization is confirmed, with geometric transformation to correct the image shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually captures images using a mobile device camera, then the user can control the capture process, but the ease of operation deteriorates due to difficulty in capturing high-quality images consistently
Solution Approach 1:
The system automatically detects the document in the captured image, determines its boundaries, and performs geometric transformation without requiring user intervention. The processor identifies the quadrangle formed by document edges and automatically corrects the image, making the system self-sufficient and eliminating the need for manual quality control operations.
Solution Approach 2:
The patent replaces manual mechanical operations (user holding device steady, manually framing shot) with automated computational processes. The system uses image processing algorithms to detect edges, calculate corner coordinates, assess stabilization through dispersion analysis, and perform geometric transformations, substituting user skill with automated image processing.
2Productivity
If the mobile device camera captures images quickly, then the productivity improves, but the stability deteriorates due to motion blur and distortion
Solution Approach 1:
The system captures multiple low-resolution images and uses dispersion calculation to provide feedback on stabilization quality. By analyzing the variation of corner coordinates across multiple frames, the system determines whether the device is sufficiently stable before triggering high-resolution capture, creating a feedback loop that ensures image quality while maintaining efficiency.
Solution Approach 2:
The system performs preliminary stabilization assessment by capturing and analyzing low-resolution images before committing to a high-resolution capture. This preliminary action allows the system to verify that the device is stable enough for quality imaging, preventing wasted high-resolution captures of unstable scenes.
3Measurement precision
If the system captures high-resolution images continuously, then the image quality improves, but the energy consumption increases
Solution Approach 1:
The system uses a partial action approach by capturing multiple low-resolution images for stabilization assessment rather than continuously capturing high-resolution images. Only when stabilization criteria are met does the system switch to high-resolution capture, reducing overall energy consumption while maintaining image quality standards.
Solution Approach 2:
The system dynamically changes the resolution parameter based on stabilization conditions. When the device is unstable, it captures low-resolution images for assessment; when stable, it switches to high-resolution capture. This parameter adjustment optimizes energy usage by avoiding unnecessary high-resolution captures during unstable periods.
4Manufacturing precision
If the system performs geometric transformation to correct image shape, then the manufacturing precision improves, but the device complexity increases
Solution Approach 1:
The geometric transformation process is segmented into distinct computational steps: edge detection to identify document boundaries, corner coordinate calculation to locate vertices, dispersion analysis to assess stabilization, and transformation application to correct the image. This segmentation makes the complex processing manageable and systematic.
Data Source
AI summary
The systems and methods of the present disclosure enable a user to use a mobile device to automatically capture a high resolution image of a rectangular object. The methods include capturing a low resolution image of the rectangular object and detecting edges of the rectangular object in the low resolution image, where the edges form a quadrangle, calculating a coordinate of each corner of the quadrangle, calculating an average coordinate of each corner of the quadrangle in a most recent predetermined number of low resolution images, calculating a dispersion of each corner of the quadrangle in the most recent predetermined number of low resolution images from a corresponding coordinate of each calculated average coordinate, determining whether the dispersion of each corner of the quadrangle is less than a predetermined value, capturing a high resolution image of the rectangular object when it is determined that the dispersion of each corner of the quadrangle is less than the predetermined value, and geometrically transforming the quadrangle of the rectangular object in the high resolution image into a rectangle.