Intel corporation (20240112035). 3D OBJECT RECOGNITION USING 3D CONVOLUTIONAL NEURAL NETWORK WITH DEPTH BASED MULTI-SCALE FILTERS simplified abstract
Contents
- 1 3D OBJECT RECOGNITION USING 3D CONVOLUTIONAL NEURAL NETWORK WITH DEPTH BASED MULTI-SCALE FILTERS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 3D OBJECT RECOGNITION USING 3D CONVOLUTIONAL NEURAL NETWORK WITH DEPTH BASED MULTI-SCALE FILTERS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
3D OBJECT RECOGNITION USING 3D CONVOLUTIONAL NEURAL NETWORK WITH DEPTH BASED MULTI-SCALE FILTERS
Organization Name
Inventor(s)
3D OBJECT RECOGNITION USING 3D CONVOLUTIONAL NEURAL NETWORK WITH DEPTH BASED MULTI-SCALE FILTERS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112035 titled '3D OBJECT RECOGNITION USING 3D CONVOLUTIONAL NEURAL NETWORK WITH DEPTH BASED MULTI-SCALE FILTERS
Simplified Explanation
The patent application discusses techniques for training and implementing convolutional neural networks for object recognition, including using 3D filters of different spatial sizes at the first convolutional layer to generate multi-scale feature maps for object recognition data.
- Applying 3D filters of different spatial sizes at the first convolutional layer
- Generating multi-scale feature maps for object recognition data
- Each feature map has a pathway to fully connected layers
- Object recognition data corresponds to the 3D input image segment
Potential Applications
This technology could be applied in various fields such as autonomous vehicles, surveillance systems, medical imaging, and robotics for object recognition tasks.
Problems Solved
This technology helps improve the accuracy and efficiency of object recognition systems by utilizing multi-scale feature maps generated from 3D filters, enhancing the overall performance of the convolutional neural network.
Benefits
The benefits of this technology include better object recognition accuracy, improved feature extraction, and enhanced performance in complex environments with varying object sizes and shapes.
Potential Commercial Applications
Potential commercial applications of this technology include implementing it in security systems, industrial automation, healthcare diagnostics, and smart city infrastructure for efficient object recognition capabilities.
Possible Prior Art
One possible prior art could be the use of traditional 2D filters in convolutional neural networks for object recognition tasks. However, the innovation in this patent application lies in the utilization of 3D filters of different spatial sizes to generate multi-scale feature maps for improved object recognition.
What are the limitations of this technology in real-world applications?
One limitation of this technology in real-world applications could be the computational complexity and resource requirements for training and implementing convolutional neural networks with 3D filters, especially in resource-constrained environments.
How does this technology compare to existing object recognition systems?
This technology offers a more advanced approach to object recognition by utilizing 3D filters and multi-scale feature maps, which can provide better accuracy and robustness compared to traditional 2D filter-based systems.
Original Abstract Submitted
techniques related to training and implementing convolutional neural networks for object recognition are discussed. such techniques may include applying, at a first convolutional layer of the convolutional neural network, 3d filters of different spatial sizes to an 3d input image segment to generate multi-scale feature maps such that each feature map has a pathway to fully connected layers of the convolutional neural network, which generate object recognition data corresponding to the 3d input image segment.
