Dynamic Feature Detection for Pixel Grid Alignment Errors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional digital image analysis methods for feature detection are limited by accidental alignment of physical features with the pixel grid, leading to inaccuracies and unreliable tracking of motion in applications like industrial manufacturing, where precise motion tracking is crucial.
Innovation Solution
Dynamic feature detection systems capture and process multiple images of moving objects or materials to reduce the impact of accidental alignment, providing more accurate and reliable estimates of physical motion by analyzing the appearance of features across various pixel grid alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static feature detection is used on a single digital image, then the detection process is simple and fast, but the accuracy and reliability of feature detection deteriorates due to accidental alignment with the pixel grid
Solution Approach 1:
The patent transitions from static feature detection on a single image to dynamic feature detection that processes multiple images captured during motion. By making the detection process dynamic and considering temporal sequences of images, the system achieves more accurate feature detection that is independent of accidental pixel grid alignment, resolving the contradiction between simple fast detection and accurate detection.
Solution Approach 2:
The patent adds the temporal dimension by processing multiple images captured at different time points during object motion. This transforms the detection from a single 2D image analysis to a 3D analysis incorporating time, allowing the system to distinguish true feature positions from pixel grid artifacts through temporal consistency analysis.
2Measurement precision
If multiple images are captured and processed to reduce accidental alignment effects, then feature detection accuracy improves, but the complexity of the detection system increases
Solution Approach 1:
The patent performs preliminary actions by capturing multiple images during object motion before the actual feature detection analysis. This preprocessing step of accumulating temporal data during motion allows the subsequent detection algorithm to work with pre-processed information, reducing the computational complexity of the main detection task while maintaining high accuracy.
Solution Approach 2:
The patent maintains continuous useful action by capturing images throughout the entire motion sequence rather than taking discrete snapshots. This continuous capture ensures that feature detection benefits from consistent temporal data flow, allowing accurate tracking while optimizing the balance between data collection and processing requirements.
3Ease of manufacture
If static feature detection is used, then the system is simple to implement, but the reliability of motion tracking deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously comparing feature positions across multiple images captured during motion. This feedback loop allows the system to identify and correct errors caused by pixel grid alignment, significantly improving motion tracking reliability while maintaining a relatively simple implementation through iterative refinement of feature positions based on temporal consistency.
Data Source
Figure 1~2
Figure 3~4
Figure 5~7
AI summary
Disclosed are methods and systems for dynamic feature detection of physical features of objects in the field of view of a sensor. Dynamic feature detection substantially reduces the effects of accidental alignment of physical features with the pixel grid of a digital image by using the relative motion of objects or material in and/or through the field of view to capture and process a plurality of images that correspond to a plurality of alignments. Estimates of the position, weight, and other attributes of a feature are based on an analysis of the appearance of the feature as it moves in the field of view and appears at a plurality of pixel grid alignments. The resulting reliability and accuracy is superior to prior art static feature detection systems and methods.