Camera Calibration Saddle Point Detection With Image Shifting
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Solution Overview
Problem
Existing camera calibration methods face challenges in accurately detecting saddle points, particularly when they are located on the border between regions of interest, leading to reduced recall and increased computational complexity.
Innovation Solution
A method involving image shifting, detection of saddle points in shifted images, and re-shifting the detected points to the original coordinates, combined with a grid structure and non-maximum suppression, utilizes a neural network to enhance detection accuracy and reduce computational time complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detection is performed on the original image only, then computational complexity is low, but detection recall is reduced (98%) particularly for saddle points on borders
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) with a grid structure, where each ROI is processed independently. This segmentation allows parallel processing of multiple shifted versions of the image, improving detection recall for border points while distributing computational load across multiple regions that can be processed concurrently.
Solution Approach 2:
The method introduces a temporal dimension by processing multiple shifted versions of the image (original image plus K shifted versions). Instead of a single detection pass, the system performs detection across K+1 different temporal states (shift positions), accumulating results to improve recall while maintaining efficiency through parallel execution.
2Reliability
If multiple shifted images are processed, then detection recall improves to 99.9%, but processing time increases
Solution Approach 1:
By segmenting the image into independent ROIs that can be processed in parallel, the method reduces the sequential processing time. Each ROI can be shifted and processed independently, allowing simultaneous computation of multiple shifted versions without proportionally increasing total processing time.
Solution Approach 2:
The image shifting operations are performed as preliminary actions before the actual detection process. By pre-computing the shifted versions and organizing them into a grid structure, the system prepares data in advance for parallel processing, reducing the overall processing time despite multiple shifts.
3Reliability
If saddle points on borders are prioritized, then detection recall improves, but device complexity increases
Solution Approach 1:
The grid-based ROI segmentation naturally handles border points by ensuring that saddle points near boundaries are captured in adjacent ROIs. When multiple shifted versions are processed, border points that fall on ROI boundaries in one shift are captured in interior positions in other shifts, improving detection without requiring special border handling logic.
Solution Approach 2:
The same detection algorithm is applied universally to all ROIs and all shifted versions without modification. The system uses a single multi-functional detection pipeline that handles both interior and border points uniformly across all K+1 shifted images, avoiding the need for separate border-specific detection routines.
Data Source
AI summary
The present disclosure describes systems and techniques of using an algorithm to identify saddle points in camera calibration patterns. In implementations, the algorithm is a machine-learning model. In aspects, a computer-implemented method for determining pre-determined points in an input image includes applying a plurality of shifts to an input image to obtain a plurality of shifted images, detecting pre-determined points in each of the shifted images, re-shifting the detected pre-determined points, and determining the pre-determined points based on the re-shifted detected pre-determined points.