AI Feature Detector and Descriptor for Robust Image Variance
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Solution Overview
Problem
Existing feature extraction methods in computer vision struggle to achieve robustness against photometric and geometric changes, particularly in 2D and 3D vision systems, due to limited modeling capabilities and reliance on hand-crafted features that are not adaptable to the wide variety of image variances.
Innovation Solution
A hybrid approach combining hand-crafted and learned features in a detector and descriptor pipeline, with joint training and hierarchical context aggregation, to enhance robustness and efficiency in feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-crafted features are used in traditional detector and descriptor pipelines, then computational efficiency is maintained, but robustness against photometric and geometric changes deteriorates
Solution Approach 1:
The patent changes the fundamental parameters of feature extraction by transitioning from hand-crafted fixed algorithms to learned adaptive features through neural networks. The system learns optimal feature representations directly from data, enabling adaptation to various photometric and geometric transformations without requiring complex manual modeling for each transformation type.
Solution Approach 2:
The patent replaces the mechanical system of hand-crafted feature extraction pipelines with a data-driven learning system. Instead of manually designing detectors and descriptors that process images through fixed mathematical operations, the system uses neural networks to automatically learn feature representations, substituting manual engineering with automated learning mechanisms.
2Adaptability or versatility
If fully data-driven learned features are used, then adaptability to image variances improves, but computational efficiency and ease of implementation deteriorates
Solution Approach 1:
The patent applies partial data-driven learning by integrating learned features selectively into specific stages of the feature extraction pipeline rather than completely replacing hand-crafted methods. The system uses learned representations where they provide the most benefit while maintaining traditional efficient algorithms in other stages, achieving a balance between adaptability and computational efficiency.
3Manufacturing precision
If detector and descriptor are trained separately, then training simplicity is maintained, but joint optimization performance deteriorates
Solution Approach 1:
The patent merges the training processes of the detector and descriptor into a unified joint training framework. Instead of training these components separately as independent modules, the system combines them into a single end-to-end training process that optimizes both components simultaneously, allowing gradients to flow through the entire pipeline and enabling coordinated optimization of detection and description functions.
Data Source
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AI summary
An image processor comprising a plurality of modules, the plurality of modules comprising a first module and a second module, wherein the image processor is configured to receive an input image and output a plurality of mathematical descriptors for characteristic regions of the input image, wherein: the first module is configured to implement a first trained artificial intelligence model to detect a set of characteristic regions in the input image; and the second module is configured to implement a second trained artificial intelligence model to determine a mathematical descriptor for each of said set of characteristic regions; wherein the first and second trained artificial intelligence models are collectively trained end to end.