Vertex Change Detection for Document Capture Stability
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Solution Overview
Problem
Existing techniques for capturing images of objects, such as documents, often result in blurry or out-of-focus images due to camera and object movement, making it difficult to ensure clarity and stability for data extraction or sharing purposes.
Innovation Solution
A method that involves identifying reference points in a series of images and comparing their positions to determine stability, with image capture only occurring when reference points remain within an acceptable range for a given number of frames, and optionally using accelerometer data to ensure device stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image capture is performed immediately after object detection, then the capture speed is improved, but the image clarity deteriorates due to camera and object movement
Solution Approach 1:
The system performs preliminary stability assessment by tracking reference points across multiple frames before committing to image capture. This preliminary action ensures that only when stability criteria are met does the capture proceed, thus maintaining clarity without significantly delaying capture speed
Solution Approach 2:
The system continuously monitors the position of reference points in successive frames and uses this feedback to determine whether stability thresholds are met. This feedback mechanism allows dynamic adjustment of capture timing to optimize both speed and clarity
2Manufacturing precision
If stability detection is performed using multiple image frames, then the image clarity is improved, but the capture time increases
Solution Approach 1:
The system uses a limited number of consecutive frames (e.g., 3-5 frames) rather than analyzing an excessive number of frames. This partial action approach provides sufficient stability assessment while minimizing the time penalty, achieving a practical balance between clarity and capture time
Solution Approach 2:
The system automatically determines when stability criteria are met without requiring manual user input or intervention. The reference point tracking and stability assessment occur autonomously, reducing the perceived capture time from the user's perspective
3Reliability
If reference point position comparison is performed across multiple frames, then the reliability of image capture is improved, but the computational complexity increases
Solution Approach 1:
The system extracts only the essential reference points (such as corner points or feature points) from each image frame for comparison, rather than analyzing all pixels or features. This extraction approach maintains high reliability in stability detection while significantly reducing computational complexity
Solution Approach 2:
The system focuses computational resources on tracking specific local reference points rather than analyzing the entire image. By concentrating on key reference points that adequately represent object position and orientation, the system achieves reliable stability assessment with reduced computational burden
Data Source
AI summary
Aspects of the present disclosure relate to object-based image capture. Embodiments include identifying a reference point corresponding to an object in an image of a series of images. Embodiments include comparing a position of the reference point in the image to positions of one or more corresponding reference points in one or more previous images in the series of images. Embodiments include determining a total number of images in the series of images. Embodiments include selecting, based on the comparing and the total number of images in the series of images, between: capturing the image; or declining to capture the image.