Projective Correction via Vanishing Point Detection
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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 data, making them inefficient and computationally expensive, especially for small devices like mobile communication devices, and often fail when images contain a combination of text and pictures or non-standard formatted text.
Innovation Solution
A method that performs image binarization, connected component analysis, horizontal and vertical vanishing point determination using eigenpoints and confidence levels, and projective correction based on these points, with adaptive threshold settings and RANSAC algorithm for robustness, to correct perspective distortion in images without the need for auxiliary data or manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual correction techniques are used to identify and mark corners or parallel lines, then projective distortion correction can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system automatically detects text regions and determines vanishing points without requiring manual user input. The algorithm self-corrects projective distortion by identifying horizontal and vertical vanishing points from the image content itself, eliminating the need for users to manually mark corners or parallel lines while maintaining high correction accuracy
Solution Approach 2:
The patent replaces manual mechanical operations (user clicking and marking points) with an automated computer vision system that uses image processing algorithms to detect text regions and calculate vanishing points, significantly reducing correction time while maintaining reliability
2Extent of automation
If existing automatic correction techniques are used, then projective distortion can be corrected, but complicated manual parameter settings are required
Solution Approach 1:
The system automatically determines all necessary parameters including text region locations, vanishing point coordinates, and correction transformation parameters without requiring manual user input. The algorithm self-configures by detecting horizontal and vertical vanishing points from the image content and automatically generates the projective correction parameters
Solution Approach 2:
The patent dynamically adjusts correction parameters based on the detected vanishing points and text region characteristics. Instead of using fixed parameters, the system calculates optimal correction parameters adaptively from the image content, eliminating the need for complicated manual parameter settings
3Reliability
If existing correction techniques are used, then distortion correction may be achieved, but computational costs are high making implementation difficult in small devices
Solution Approach 1:
The patent extracts only the essential information needed for correction by focusing specifically on detecting text regions and determining vanishing points from meaningful image content. This selective extraction approach avoids processing the entire image with high computational complexity, reducing energy consumption while maintaining correction effectiveness
Solution Approach 2:
The system segments the image processing task into distinct stages: text region detection, vanishing point determination, and correction parameter calculation. This segmentation allows each stage to use optimized algorithms appropriate to its specific function, reducing overall computational energy consumption compared to applying a single complex correction algorithm to the entire image
4Adaptability or versatility
If existing techniques are used, then correction may work for standard formatted text, but they fail when text is not formatted or positioned in a particular manner
Solution Approach 1:
The system dynamically adapts to different text formats and positions by detecting vanishing points directly from the image content rather than assuming fixed text layouts. The algorithm adjusts its detection and correction parameters based on the actual text region characteristics found in the image, enabling it to handle various text formats including non-standard positioning and formatting
Solution Approach 2:
The patent creates a universal correction method that works across different text formats, positions, and orientations by using vanishing point detection from image content rather than relying on specific text formatting assumptions. This multi-functional approach enables the system to correct projective distortion in diverse document types including those with pictures, non-standard text positioning, and various formatting styles
Data Source
AI summary
Method, system, device and computer program product for determining vanishing point candidates of a text portion in an image document distorted by perspective. The method includes the steps of image binarization, connected component analysis, estimating a number of text lines in a Cartesian coordinate system, transforming the text lines to data points in a homogenous coordinate system, assigning a confidence level to the data points, grouping a number of data points into a priority sample array, clustering the data points in the priority sample array into a number of sample groups and assigning a group confidence value to each sample group. A RANSAC algorithm is applied to determine among the data points a set of inliers, initiated with the sample group having the highest group confidence value. A vanishing point candidate is determined from the text lines corresponding to the set of inliers.