Projective Distortion Correction Using Eigenpoints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for correcting projective distortion in digital images are either manual and time-consuming, error-prone, or require complex parameter settings and auxiliary sensors, limiting their applicability, especially in devices like mobile communication devices, and often fail with images containing a mix of text and pictures or non-standard formatting.
Innovation Solution
A method involving image binarization, connected component analysis, horizontal and vertical vanishing point determination using eigenpoints and confidence levels, and projective correction based on these points, which automatically separates text from pictures and adapts to image content, reducing computational expense and user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correction techniques are used to identify and mark corners or parallel lines, then projective distortion correction can be performed, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system automatically detects text regions and calculates vanishing points without requiring manual user input. The algorithm self-corrects projective distortion by identifying text baselines and their convergence points, eliminating the need for users to manually mark corners or lines while maintaining high correction accuracy
Solution Approach 2:
The patent replaces manual mechanical interaction (user marking corners/lines) with an automated computer vision system that uses image processing algorithms to detect text regions, calculate eigenpoints, and determine vanishing points automatically, significantly reducing correction time while maintaining precision
2Extent of automation
If existing automatic correction techniques are used that focus on identifying horizontal and vertical vanishing points, then projective distortion can be corrected, but complicated manual parameter settings are required
Solution Approach 1:
The system automatically adapts to different image contents by detecting text regions and dynamically adjusting parameters. The algorithm self-configures by identifying text baselines and calculating vanishing points based on the actual image content, eliminating the need for users to manually set complex parameters for different document types
Solution Approach 2:
The correction parameters are dynamically adjusted based on the detected text content and image characteristics. The system adapts its vanishing point calculation and correction transformation according to the specific document layout and text formatting, making the algorithm versatile without requiring manual parameter changes
3Reliability
If existing correction techniques are used that assume documents comprise only text in particular formatting, then correction can be performed for standard documents, but the techniques fail when text is not formatted or positioned in the expected manner
Solution Approach 1:
The system is designed to handle diverse document formats and text layouts by using a universal text detection approach. The algorithm can process both formatted and unformatted text, documents with pictures mixed with text, and various text orientations, making it universally applicable across different document types without requiring format-specific configurations
Solution Approach 2:
The algorithm dynamically changes its detection and correction parameters based on the actual text content and layout detected in the image. It adapts to different text formats, positions, and orientations by adjusting its baseline detection and vanishing point calculation methods, maintaining reliability across diverse document formats
4Measurement precision
If existing correction techniques are used that require auxiliary data from sensors, then correction accuracy may be improved, but the techniques cannot be implemented in devices lacking various sensors and processing capabilities
Solution Approach 1:
The patent extracts only the necessary visual information directly from the image content itself, eliminating the dependency on external auxiliary sensors. By focusing on text region detection and vanishing point calculation from image pixels alone, the system achieves accurate correction while being applicable to any device with basic image processing capability
Solution Approach 2:
The algorithm uses simple, computationally efficient image processing operations that can be performed on basic mobile devices without expensive sensors. The method relies on standard image binarization, connected component analysis, and line detection algorithms that are resource-light and can run on devices with limited processing power
Data Source
AI summary
Method, system, device and computer program product for projective correction of an image containing at least one text portion that is distorted by perspective. The method includes a step of image binarization involving binarizing said image. The method includes connected component analysis. Pixel blobs are detected in said at least one text portion of said binarized image in connected component analysis. The method includes horizontal vanishing point determination, including estimating text baselines by means of eigenpoints of said pixel blobs and determining a horizontal vanishing point of said at least one text portion by means of said text baselines. The method also includes vertical vanishing point determination. A vertical vanishing point is determined for said at least one text portion on the basis of vertical features thereof. The method includes projective correction. Perspective in said image is corrected on the basis of said horizontal and vertical vanishing points.