Fully Convolutional Network for Joint Keypoint Detection and Description
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Solution Overview
Problem
Current imaging systems face challenges in jointly detecting and describing keypoints in images efficiently, as they often require separate detection and description processes, which can be inefficient and less accurate.
Innovation Solution
A system utilizing a fully convolutional network configured to concurrently generate descriptors, reliability scores, and repeatability scores for pixels, with a scoring module to select the most reliable and repeatable keypoints, and a keypoint list module to generate a list of keypoint locations and descriptors, enhancing the detection and description process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If separate detection and description processes are used, then the system structure is simple, but the processing efficiency is low and accuracy is reduced
Solution Approach 1:
The patent merges the keypoint detection process and the descriptor generation process into a single unified neural network model. The network simultaneously outputs keypoint locations, reliability scores, and descriptors in one forward pass, eliminating the need for separate processing stages and thereby improving processing efficiency while maintaining a coherent system structure.
Solution Approach 2:
The unified neural network performs multiple functions concurrently: it detects keypoint locations, generates reliability scores for each keypoint, and creates descriptors for matching. This multi-functional approach allows a single system to handle both detection and description tasks, improving overall productivity without requiring multiple specialized components.
2Measurement precision
If separate detection and description processes are used, then each process can be optimized independently, but the overall accuracy is reduced
Solution Approach 1:
By combining detection and description in a single unified network, the model can leverage shared feature representations and contextual information across both tasks. The joint training process allows the network to learn features that are simultaneously useful for both accurate keypoint localization and robust descriptor generation, thereby improving overall accuracy while reducing information loss.
Solution Approach 2:
The unified network employs a joint loss function that provides feedback signals to optimize both detection and description simultaneously. The reliability scores generated during detection feed into the selection process, which in turn influences descriptor quality, creating a feedback loop that enhances overall feature quality and matching accuracy.
Data Source
AI summary
A system for detecting and describing keypoints in images is described. A camera is configured to capture an image including a plurality of pixels. A fully convolutional network is configured to jointly and concurrently: generate descriptors for each of the pixels, respectively; generate reliability scores for each of the pixels, respectively; and generate repeatability scores for each of the pixels, respectively. A scoring module is configured to generate scores for the pixels, respectively, based on the reliability scores and the repeatability scores of the pixels, respectively. A keypoint list module is configured to: select X of the pixels having the X highest scores, where X is an integer greater than 1; and generate a keypoint list including: locations of the selected X pixels; and the descriptors of the selected X pixels.