Multi-Camera Imaging System Eliminates Specular Reflections
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Solution Overview
Problem
Industrial image capture systems face challenges in obtaining accurate, high-quality images of moving targets due to specular reflection, which distorts surface appearance characteristics and hinders the identification of irregularities such as knots in lumber, leading to inefficiencies in lumber milling processes.
Innovation Solution
A method involving simultaneous raw image acquisition from multiple cameras, followed by flattening and gridizing to correct for illumination and parallax variations, and selective combining to eliminate specular reflections, resulting in enhanced images that accurately portray surface features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to capture images from different angles, then the ability to eliminate specular reflections is improved, but the device complexity increases
Solution Approach 1:
The imaging system is segmented into multiple cameras positioned at different angles, with each camera capturing a specific angular perspective of the target surface. This segmentation allows the system to capture both specular and diffuse reflection components separately, enabling subsequent computational elimination of specular reflections to achieve accurate surface appearance measurement.
Solution Approach 2:
A computational processing system acts as an intermediary between the multiple cameras and the final surface appearance output. This intermediary processes the raw images from multiple cameras, applies flattening corrections for illumination variations, performs gridizing to correct parallax effects, and selectively combines images to eliminate specular reflections, thereby mediating the complex multi-camera input into accurate surface appearance data.
2Productivity
If simultaneous image acquisition from multiple cameras is performed, then the productivity is improved, but the device complexity increases
Solution Approach 1:
Multiple cameras are merged into a single integrated imaging system that simultaneously captures surface appearance data from different angular perspectives. The cameras are synchronized to acquire images at the same moment, and their data streams are combined through a unified processing pipeline that applies flattening, gridizing, and selective combining operations, thereby achieving high-speed productivity improvement while managing complexity through integration.
3Measurement precision
If flattening and gridizing processing is applied to correct illumination and parallax variations, then the measurement precision is improved, but the processing time increases
Solution Approach 1:
Flattening correction coefficients and gridizing transformation parameters are pre-calculated based on the known geometric configuration of the cameras and illumination sources. These correction factors are prepared in advance and applied during the imaging process, allowing rapid correction of illumination variations and parallax effects without requiring iterative computation during real-time processing, thus improving measurement precision while minimizing processing time.
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
The method generates high-quality, distortion-free images that improve the accuracy of surface feature identification, enabling optimal cut placement and reducing waste in lumber processing, while also being applicable to other industrial imaging applications.
Implementation Method 1
All physical targets reflect incident light that falls on a surface in one of two kinds of reflection: specular reflection or diffuse reflection
Implementation Method 2
Areas of specular reflection from the target object appear as overly bright areas on camera images and also obliterate image accuracy regarding surface appearance characteristics
Data Source
AI summary
This invention provides accurate, high quality images for the identification of the surface characteristics of an object, that may be used as an input to suitable industrial process. It involves acquiring a first raw scan of a portion of a target object across a scan line in a scan zone with a first camera and simultaneously acquiring a second raw scan of the same portion of the target object across the scan line in the scan zone with a second camera. The raw scans are converted to digital and then processed with flattening coefficients derived from measurements of variations in illumination. The first and second cameras sets of flattened image data are then gridized to compensate for parallax, make them orthographic sets of image data that can be compared on a pixel-by-pixel basis with a known or measured geometric profile of the target. A selection of enhanced pixel value for a surface coordinate can then be made, based on both sets of data. The obscuring of surface features by specular reflection can thus be effectively eliminated.