18464224. Model Training Method, Image Edge Detection Method, and Multi-Sensor Calibration Method simplified abstract (Robert Bosch GmbH)
Model Training Method, Image Edge Detection Method, and Multi-Sensor Calibration Method
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Inventor(s)
Model Training Method, Image Edge Detection Method, and Multi-Sensor Calibration Method - A simplified explanation of the abstract
This abstract first appeared for US patent application 18464224 titled 'Model Training Method, Image Edge Detection Method, and Multi-Sensor Calibration Method
Simplified Explanation
The abstract describes a method for model training, image edge detection, multi-sensor calibration, a computer program product, and a computer device. The model is used to generate occlusion relationships between pixel pairs. The model training method involves constructing an initial model, obtaining training images and reference annotation results, using RGB and depth features, and training the model to obtain a well-trained model.
- Model training method:
- Construct initial model - Obtain training images and reference annotation results - Use RGB and depth features - Train model to obtain well-trained model
- Potential Applications:**
- Image processing - Computer vision - Robotics - Autonomous vehicles
- Problems Solved:**
- Accurate edge detection - Multi-sensor calibration - Occlusion relationship generation
- Benefits:**
- Improved image processing accuracy - Enhanced computer vision capabilities - Better calibration for multi-sensor systems
- Potential Commercial Applications:**
- Surveillance systems - Medical imaging devices - Industrial automation - Augmented reality technology
- Possible Prior Art:**
- Previous methods for image edge detection - Existing multi-sensor calibration techniques
- Unanswered Questions:**
- 1. How does the model handle occlusion relationships in complex scenes with multiple objects?**
- The abstract does not provide details on how the model addresses occlusion in complex scenarios where multiple objects overlap. Further information on the model's robustness in such situations would be beneficial.
- 2. Are there any limitations to the training process mentioned in the abstract?**
- The abstract does not mention any potential challenges or limitations that may arise during the model training process. Understanding any constraints or drawbacks of the method would provide a more comprehensive view of its applicability.
Original Abstract Submitted
A method for model training, an image edge detection method, a multi-sensor calibration method, a computer program product, and a computer device is disclosed. The model is used to generate occlusion relationships between pixel pairs. The model training method comprises: constructing an initial model; obtaining multiple training images and reference annotation results for each training image, wherein each training image comprises RGB features and depth features, and the reference annotation results for each training image are annotation results of occlusion relationships between adjacent pixel pairs in said training image generated based on the depth features of said training image; respectively using the RGB features of the multiple training images as inputs to the initial model, using the reference annotation results corresponding to the input training images as outputs of the initial model, and training the initial model to obtain a well-trained model.
