Document Contour Detection Using Vanishing Points
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Solution Overview
Problem
Traditional methods for identifying object contours in digital images struggle with ambiguously represented objects, such as those that are partially covered, rotated, crumpled, or affected by optical distortion, especially when dealing with overlapping or misaligned documents.
Innovation Solution
The use of vanishing points in the image to determine and evaluate candidate contours, allowing for enhanced detection of distorted or hidden objects by generating and evaluating candidate polygons that represent the contours of documents, even when they extend beyond the image boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional edge detection techniques are used to identify object contours, then the method is simple and computationally efficient, but the accuracy deteriorates when objects are partially covered, rotated, crumpled, or affected by optical distortion
Solution Approach 1:
The patent segments the contour detection process into multiple stages: generating candidate contours based on seed points, evaluating each candidate using multiple criteria (geometric properties, text recognition results, vanishing point consistency), and selecting the best candidate. This segmentation allows the system to handle ambiguous cases systematically without requiring a single complex algorithm.
Solution Approach 2:
The patent introduces vanishing points as an intermediary element to evaluate candidate contours. By determining vanishing points from the image and checking whether candidate contour lines converge at these vanishing points, the system resolves ambiguity in distorted or perspective-view images without directly complexifying the contour detection algorithm itself.
2Measurement precision
If multiple candidate contours are generated and evaluated to handle ambiguous objects, then the detection accuracy improves, but the computational resources increase
Solution Approach 1:
The patent generates multiple candidate contours (excessive action) but evaluates them using a hierarchical filtering process that eliminates unlikely candidates early. By using quick geometric checks and vanishing point consistency tests before more computationally intensive text recognition, the system processes only necessary candidates in detail, balancing accuracy with computational efficiency.
Solution Approach 2:
The patent changes evaluation parameters dynamically based on image characteristics. For example, it adjusts the number of candidate contours generated, the strictness of geometric constraints, and the weighting of different evaluation criteria based on the detected object type and image quality, optimizing computational resource usage for different scenarios.
3Adaptability or versatility
If vanishing points are used to evaluate candidate contours, then the handling of perspective distortion improves, but the device complexity increases
Solution Approach 1:
The patent determines vanishing points from the image before generating and evaluating candidate contours. By pre-computing the vanishing points and their directions, the system establishes a reference framework that simplifies the subsequent contour evaluation process, as each candidate can be quickly checked against the pre-determined vanishing point constraints rather than computing perspective transformations during evaluation.
Data Source
AI summary
Systems and methods for detecting document contours in a digital image. An example method may involve: receiving an image comprising one or more documents and detecting a set of lines in the image; identifying a plurality of intersection points corresponding to the set of lines; determining a vanishing point based on the plurality of intersection points; generating a candidate polygon based on the set of lines; evaluating the candidate polygon based on a parameter associated with the vanishing point; and indicating the candidate polygon represents a contour of one of the one or more documents.