Two-Stage Pattern Detection for Endoscopic Camera Calibration
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Solution Overview
Problem
Current camera calibration methods for endoscopic cameras with wide-angle lenses face challenges in handling strong distortions, image blur, noise, and partially visible targets, making them unreliable for robust chessboard detection.
Innovation Solution
A two-stage detection method is employed, starting with an initial detection of a 3x3 chessboard portion to estimate region parameters, followed by feature detection in adjacent regions using morphable templates and homography matrices to refine the pattern's position and orientation, effectively handling distortions and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional single-stage detection methods are used, then the detection process is simple, but the detection reliability deteriorates under strong distortions, image blur, and noise
Solution Approach 1:
The detection process is divided into two distinct stages: an initial detection stage that identifies a first region with at least two homogeneous areas to estimate region parameters, followed by a second detection stage that identifies a second region adjacent to the first region to detect precise features. This segmentation of the detection process into multiple stages improves reliability under difficult conditions while managing complexity through structured organization of detection tasks.
Solution Approach 2:
The initial detection stage performs preliminary estimation of region parameters (position, orientation, distortion) by detecting the first region with homogeneous areas before the final feature detection. This preliminary action provides a foundation for the second stage, enabling more accurate feature detection in adjacent regions even when images suffer from strong distortions, blur, or noise.
2Measurement precision
If feature detection is performed in all regions, then the measurement precision is improved, but the loss of time increases due to processing the entire image
Solution Approach 1:
The image processing is segmented into two stages: first, a coarse detection of a first region with homogeneous areas provides initial parameter estimates; second, precise feature detection is performed only in a second region adjacent to the first region. This spatial and computational segmentation concentrates processing resources on critical areas, achieving high measurement precision for position and orientation while minimizing overall detection time by avoiding exhaustive processing of the entire image.
Solution Approach 2:
Instead of performing feature detection across the entire image, the method applies partial action by focusing computational effort on a second region adjacent to the initially detected first region. This partial processing approach achieves sufficient measurement precision for calibration purposes while significantly reducing the time loss associated with processing all image regions.
3Reliability
If robust detection under difficult conditions is achieved, then the reliability is improved, but the device complexity increases due to multiple detection stages
Solution Approach 1:
The detection algorithm is segmented into two functional stages: initial detection of homogeneous areas for parameter estimation, and subsequent feature detection in adjacent regions for precise pattern identification. This segmentation enables robust detection under strong distortions, blur, and noise by breaking down the complex task into manageable steps, each optimized for specific detection goals while maintaining overall algorithmic structure.
Solution Approach 2:
The initial detection stage performs preliminary estimation of region parameters including position, orientation, and distortion characteristics. This preliminary action establishes a foundation that guides the second detection stage, enabling robust feature detection in adjacent regions even under difficult imaging conditions. The preliminary parameter estimates reduce the complexity of the second stage by providing constraints and guidance for feature search.
Data Source
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AI summary
Embodiments of the present invention refer to a method for detecting a known pattern having homogeneous areas. The method comprises the steps of taking an image of the known pattern or, of at least a portion of the known pattern, and performing detection. Detection comprises at least two steps; namely, an initial detection of a first region of the image and the detection of a feature of the known pattern within a second region of the image. The second region is arranged adjacent to the first region. The initial detection has the purpose to estimate at least one region parameter, such as a position of the region of the pattern, and/or orientation of the region or distortion of same. Starting from this, the second region can be selected in which a feature, such as a corner of the checkerboard is detectable. This second detection step has the purpose to obtain at least one region parameter such as the position of a feature within the second region.