Conical Beam Pose Estimation for Fast Feature Detection
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Solution Overview
Problem
Conventional feature detection processes in image processing are either time-consuming and computationally expensive due to their reliance on pose-invariant methods or slow because they require iterative operations optimized for specific poses, making them unsuitable for rapid scanning of multiple objects.
Innovation Solution
A feature detection system that projects a diverging conical beam of light onto an object surface to determine its pose through the shape of the resulting illumination pattern, allowing for quick and accurate feature detection without the need for computationally expensive pose-invariant processes or complex 3D modeling techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pose-invariant feature detection processes (e.g., SIFT, SURF) are used, then feature detection accuracy is improved, but processing time and computational resources increase excessively
Solution Approach 1:
The system performs preliminary pose estimation using a simplified conical beam projection method before conducting feature detection. By pre-determining the pose parameters (distance, tilt angle, orientation) through the illumination pattern analysis, the subsequent feature detection can be optimized for that specific pose rather than using computationally expensive pose-invariant methods, thus resolving the contradiction between accuracy and processing time
Solution Approach 2:
The patent replaces complex computational mechanics (pose-invariant detection algorithms like SIFT and SURF) with an optical-mechanical approach (conical beam projection and pattern analysis). The physical projection of light and analysis of the resulting illumination pattern provides pose information more efficiently than traditional image processing methods, reducing computational burden while maintaining detection accuracy
2Device complexity
If iterative feature detection processes optimized for specific poses are used, then computational resources are reduced, but processing speed remains slow for rapid scanning applications
Solution Approach 1:
The conical beam projection and illumination pattern analysis perform the pose estimation action beforehand, enabling the feature detection to proceed directly with pose-optimized algorithms without iterative adjustments. This preliminary determination of pose parameters (distance, tilt, orientation) eliminates the need for repeated detection cycles, thereby increasing scanning speed while keeping computational resources manageable
Solution Approach 2:
The system changes the parameter representation from raw image data to derived pose parameters (distance, tilt angle, orientation) through the illumination pattern analysis. By transforming the problem space to work with these extracted parameters rather than performing iterative searches on full images, the system achieves faster processing suitable for rapid scanning applications
3Measurement precision
If 3D modeling techniques with depth sensing cameras (e.g., Kinect) are used to establish object pose, then pose determination accuracy is improved, but system cost and technical complexity increase
Solution Approach 1:
The patent extracts only the essential pose information (distance, tilt angle, orientation) needed for feature detection from the illumination pattern, rather than performing full 3D scene reconstruction. By taking out only the necessary parameters from the complex 3D modeling process and discarding unnecessary computational steps, the system achieves pose determination accuracy sufficient for feature detection without the high cost and complexity of depth sensing cameras
Solution Approach 2:
The system uses a simple, inexpensive conical beam projection method with basic illumination pattern analysis instead of expensive depth sensing hardware. The approach treats the pose estimation as a temporary, single-purpose function rather than building a comprehensive 3D modeling system, achieving the needed pose accuracy at minimal cost and technical complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables fast and resource-efficient feature detection by providing pose information that counter-distorts imagery, allowing for accurate recognition of features like barcodes or watermarks without the costs and complexity of prior art 3D modeling approaches.
Implementation Method 1
a diverging, conical, beam of light is projected from a source, such as an LED, onto an object surface. If the object surface squarely faces the axis of the conical beam, the beam of light will project a perfect circle on the object surface. If the surface is inclined, the beam of light will result in an ellipse-shaped illumination pattern.
Data Source
AI summary
Arrangements are detailed to process imagery of an object, captured by a camera, based on contextual data that at least partially characterizes a condition of the object when the imagery was captured. Contextual data can be obtained directly by a sensor or can be derived by pre-processing the captured imagery. The captured imagery can be processed to detect features such as digital watermarks, fingerprints, barcodes, etc. A great number of other features and arrangements are also detailed.