Camera Parameter Determination from Single Image
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Solution Overview
Problem
Conventional digital image analysis systems face challenges in accuracy, flexibility, and efficiency when determining camera parameters, particularly with single images containing misleading shapes or requiring multiple images for calibration, leading to inaccurate and resource-intensive processing.
Innovation Solution
A camera parameter determination system utilizing a critical edge detection neural network in conjunction with a geometric model to identify vanishing lines and calculate focal length, pitch, roll, and yaw from a single digital image, generating accurate training data and edge maps to enhance image editing and compositing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional convolutional neural networks are used to determine camera parameters, then the system can process images, but the accuracy and precision of parameter determination deteriorates with high error rates
Solution Approach 1:
The patent segments the image processing task into two distinct stages: first, a neural network generates a vanishing line map identifying potential vanishing lines; second, a geometric model analyzes these lines to determine camera parameters. This segmentation allows each component to specialize, with the neural network focusing on line detection and the geometric model focusing on precise parameter calculation, thereby improving overall accuracy while reducing error rates compared to using a single convolutional neural network approach.
Solution Approach 2:
The patent introduces a vanishing line map as an intermediary between the neural network and the final parameter determination. This intermediate representation separates the complex tasks of line detection and parameter calculation, allowing the system to leverage the pattern recognition strength of neural networks while maintaining the geometric precision of traditional computer vision methods, thus resolving the accuracy-reliability contradiction.
2Measurement precision
If geometric approaches are used to analyze edges and shapes, then the system can determine camera parameters, but the robustness deteriorates when images contain misleading shapes or confusing lines
Solution Approach 1:
The patent divides the processing into two specialized stages: the neural network stage handles the complexity of identifying genuine vanishing lines amidst misleading shapes, while the geometric model stage focuses on precise parameter extraction from the filtered vanishing line map. This segmentation enables the system to maintain robustness against misleading shapes while preserving geometric precision.
Solution Approach 2:
The system employs feedback mechanisms where the vanishing line map generated by the neural network is refined and validated before being passed to the geometric model. This feedback loop allows the system to filter out misleading lines and shapes, improving robustness while maintaining the precision of geometric parameter determination.
3Measurement precision
If multiple digital images are used to determine camera parameters, then the system can extract parameters, but the efficiency and processing time deteriorates due to extensive computer resources required
Solution Approach 1:
The patent extracts and utilizes vanishing line information from a single image through the combined neural network and geometric model approach, eliminating the need to process multiple images. This extraction of critical geometric features from one image maintains parameter determination accuracy while dramatically improving processing efficiency by removing the computational burden of analyzing multiple images.
Solution Approach 2:
The system performs preliminary action by using the neural network to pre-identify and map vanishing lines before the geometric model calculates final parameters. This preliminary processing of key features from a single image prepares the data in advance, enabling accurate parameter extraction without requiring multiple images or extensive processing, thus resolving the efficiency-accuracy contradiction.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a critical edge detection neural network and a geometric model to determine camera parameters from a single digital image. In particular, in one or more embodiments, the disclosed systems can train and utilize a critical edge detection neural network to generate a vanishing edge map indicating vanishing lines from the digital image. The system can then utilize the vanishing edge map to more accurately and efficiently determine camera parameters by applying a geometric model to the vanishing edge map. Further, the system can generate ground truth vanishing line data from a set of training digital images for training the critical edge detection neural network.