Vanishing Point Detection via Angular Sparsity Evaluation
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Solution Overview
Problem
Conventional vanishing point detecting systems are weak against noise, leading to inaccurate detection due to inclusion of inappropriate line segments, which results in deviation from the true vanishing point position.
Innovation Solution
A vanishing point detecting system that uses a straight line detecting means to identify points where multiple straight lines pass through, calculating an evaluation value for the extent of angle variation, and detecting the point with a high evaluation value as the vanishing point, utilizing statistical values like variance and standard deviation to assess the plausibility of angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional line segment detection methods are used to identify vanishing points, then the detection process is simple, but the detection accuracy deteriorates due to noise and inappropriate line segments
Solution Approach 1:
The invention changes the evaluation parameter from simple line segment intersection to angular distribution characteristics. By evaluating whether angles of multiple straight lines passing through a point are sparsely distributed over a wide range, the system achieves more accurate vanishing point detection while filtering out noise and inappropriate line segments.
Solution Approach 2:
The invention replaces the conventional geometric intersection method with a statistical evaluation method based on angular distribution. Instead of relying on simple line segment intersections, the system uses probability-based assessment of angle sparsity to determine vanishing points, improving robustness against noise.
2Reliability
If multiple line segments are considered for vanishing point estimation, then the estimation can be performed analytically, but false detection increases due to inappropriate line segments
Solution Approach 1:
The invention introduces an evaluation mechanism that assesses the plausibility of each candidate vanishing point based on angular distribution characteristics. This feedback loop allows the system to identify and reject false detections by evaluating whether the angular patterns match expected vanishing point characteristics.
Solution Approach 2:
The system changes from considering only line segment intersections to evaluating the angular distribution pattern of multiple lines. By introducing angular sparsity as a key parameter, the system can distinguish true vanishing points from false detections even when multiple line segments are involved.
Data Source
AI summary
Disclosed is a vanishing point detecting system that includes a straight line detecting unit, a vanishing point detecting unit, and a vanishing point outputting unit. In the vanishing point detecting unit, a vanishing point is detected with one evaluation index of vanishing point plausibility being whether or not angles of plural straight lines passing through a point in question or a vicinity thereof are sparsely distributed over a relatively wide range.