Analog Instrument Pointer Recognition for Accurate Angle Detection
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Solution Overview
Problem
Automatic reading of analog instruments is challenging due to the difficulty in precisely recognizing the pointer contour in images, often confused with irrelevant contours, leading to uncertain results in determining the pointing direction and angle.
Innovation Solution
The solution employs computer vision techniques combined with pattern recognition, using geometric screening and Principal Component Analysis (PCA) to distinguish the pointer contour from background objects, allowing for accurate slope calculation and angle determination without requiring a specific pointer reference template.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional contour detection is used to identify the pointer, then the process is simple, but the recognition accuracy is low due to confusion with irrelevant contours
Solution Approach 1:
The contour screening process is divided into multiple stages: initial contour detection, geometric feature filtering (area, circularity, aspect ratio), position-based filtering (distance from center, angular distribution), and final pointer identification. This segmentation allows systematic elimination of irrelevant contours while preserving the true pointer contour.
Solution Approach 2:
The patent employs multiple geometric parameters (area, circularity, aspect ratio, distance from center, angular position) to characterize and filter contours. By changing and combining these parameters in a multi-stage filtering process, the system achieves high pointer recognition accuracy while managing the complexity of distinguishing the pointer from background contours.
2Measurement precision
If geometric screening alone is used to identify the pointer, then the process is fast, but accuracy is insufficient when multiple candidate contours remain
Solution Approach 1:
Geometric screening is performed as a preliminary action to quickly eliminate obviously irrelevant contours based on area, circularity, and aspect ratio thresholds. This preliminary filtering reduces the candidate set before applying more time-consuming analysis methods, optimizing the balance between speed and accuracy.
Solution Approach 2:
The screening process is dynamic and adaptive: contours are processed through multiple filtering stages, and the system adjusts the strictness of criteria at each stage. When geometric screening leaves multiple candidates, the system dynamically transitions to more sophisticated evaluation methods to identify the true pointer.
3Reliability
If the pointer contour is confused with background contours, then irrelevant objects are included, but the pointing angle determination becomes uncertain
Solution Approach 1:
The patent applies different quality criteria to different regions and aspects of contour evaluation. Position-based filtering examines local spatial relationships (distance from instrument center, angular distribution relative to scale markings). Geometric feature filtering examines local shape characteristics (circularity, aspect ratio) that are specific to pointer morphology versus background objects.
Solution Approach 2:
The multi-parameter screening system serves multiple functions simultaneously: geometric parameters filter by shape, position parameters filter by location, and the combination of these universal criteria creates a robust system that can distinguish pointers from various types of background contours across different instrument types and imaging conditions.
Data Source
AI summary
Systems and processes for identifying a pointer in an image of an analog instrument are provided herein. An instrument contour in the image corresponding to the analog instrument may be identified. A plurality of candidate pointer contours in the image may be identified and screened using one or more geometric property screening techniques including an evaluation of a geometric area, a distance parameter, and/or a gravity center of the plurality of candidate pointer contours. Principal component analysis (PCA) may be performed to select an identified pointer contour from among the reduced plurality of candidate pointer contours. A linear regression model may be applied to pixel points in the contour area of the identified pointer contour and a slope and angle of an associated pointer represented by the identified pointer contour may be determined based on an output of the linear regression model.