18554173. 4:2:0 PACKING OF FEATURE MAPS simplified abstract (CANON KABUSHIKI KAISHA)
Contents
4:2:0 PACKING OF FEATURE MAPS
Organization Name
Inventor(s)
Christopher James Rosewarne of Concord (AU)
4:2:0 PACKING OF FEATURE MAPS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18554173 titled '4:2:0 PACKING OF FEATURE MAPS
The abstract describes a method for decoding feature maps from encoded data by decoding a plurality of samples. The feature maps are determined based on images from two groups of samples arranged in two-dimensional arrays.
- Decoding feature maps from encoded data
- Determining feature maps based on images from two groups of samples
- Samples arranged in two-dimensional arrays
- Different two-dimensional arrays for each group of samples
- Plurality of samples decoded from the encoded data
Potential Applications: This technology could be used in image processing, computer vision, and artificial intelligence applications where decoding feature maps is necessary.
Problems Solved: This technology addresses the challenge of efficiently decoding feature maps from encoded data, which is crucial in various image processing tasks.
Benefits: The method allows for accurate decoding of feature maps, leading to improved performance in image processing and computer vision applications.
Commercial Applications: This technology could be valuable in industries such as healthcare (medical imaging analysis), autonomous vehicles (object detection), and security (surveillance systems).
Questions about Decoding Feature Maps from Encoded Data: 1. How does this method improve the efficiency of decoding feature maps? 2. What are the potential limitations of decoding feature maps using this technology?
Frequently Updated Research: Stay updated on advancements in image processing algorithms and computer vision techniques to enhance the decoding of feature maps from encoded data.
Original Abstract Submitted
A method of decoding feature maps from encoded data. A plurality of samples is decoded from the encoded data. The feature maps are determined based on one image from at least a first group of samples arranged in a first two-dimensional array and a second group of samples arranged in a second two-dimensional array, where the second two-dimensional array is different from the first two-dimensional array.
