Sequential Color Illumination for High-Resolution Image Segmentation
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Solution Overview
Problem
Existing 3D computer-vision systems struggle with inaccurate image segmentation in industrial settings due to the lack of color information in grayscale images captured by black-and-white cameras, which are necessary for high-resolution requirements, compromising the precision needed for robotic manufacturing tasks.
Innovation Solution
A computer-vision system using multiple single-color light sources, such as LEDs, illuminates a scene sequentially to capture pseudo-color grayscale images, which are processed by a deep-learning neural network to generate accurate segmentation results, combined with depth information from structured light for enhanced precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If black-and-white cameras are used to capture high-resolution images, then manufacturing precision is improved, but image segmentation accuracy deteriorates due to lack of color information
Solution Approach 1:
The system performs preliminary colorization on grayscale images before segmentation processing. By applying deep learning-based color prediction algorithms to the grayscale image, the system generates a colorized version that preserves the original high-resolution spatial information while adding synthetic color data. This preliminary action enables subsequent segmentation algorithms to utilize color features without requiring the original color camera input.
Solution Approach 2:
The patent introduces an intermediary deep learning model that acts as a bridge between grayscale images and color information. This intermediary system predicts color values for each pixel based on the grayscale input and learned correlations from training data. The generated color map serves as an intermediary representation that enables color-based segmentation while maintaining the high spatial resolution of the original grayscale image.
2Measurement precision
If color cameras are used to capture RGB images, then segmentation accuracy is improved, but manufacturing precision deteriorates due to lower spatial resolution
Solution Approach 1:
The system performs preliminary colorization on grayscale images before segmentation processing. By applying deep learning-based color prediction algorithms to the grayscale image, the system generates a colorized version that preserves the original high-resolution spatial information while adding synthetic color data. This preliminary action enables subsequent segmentation algorithms to utilize color features without requiring the original color camera input.
Solution Approach 2:
The patent creates a synthetic color copy of the grayscale image through deep learning-based colorization. Instead of using the lower-resolution color camera output, the system generates a high-resolution color representation by learning color distributions from training data and applying them to the high-resolution grayscale input. This copied color information maintains the spatial fidelity of the original grayscale image while providing the color features needed for accurate segmentation.
3Loss of information
If multiple light sources are used to illuminate the scene sequentially, then color information is recovered in grayscale images, but productivity deteriorates due to increased capture time
Solution Approach 1:
The system extracts color information from the grayscale image through deep learning-based colorization. Instead of capturing multiple sequential images under different colored illuminations, the method extracts predicted color values directly from the single grayscale input image. This extraction approach recovers the color information that would otherwise be lost, while avoiding the time penalty of multiple sequential captures.
Solution Approach 2:
The patent replaces the mechanical approach of sequential multi-illumination capture with a computational colorization system. Instead of physically changing light sources and retaking images, the system uses deep learning algorithms to synthesize color information from the single grayscale capture. This substitution eliminates the mechanical steps required for sequential illumination while achieving the same information recovery goal.
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 system achieves improved image segmentation accuracy by incorporating implicit color information into grayscale images, enabling precise identification of objects for robotic grasping tasks, overcoming the limitations of conventional grayscale imaging.
Implementation Method 1
The computer-vision system can include one or more cameras to capture images of a scene and one or more sets of single-color light sources to illuminate the scene, with a respective set of light sources comprising multiple single-color light sources of different colors
Implementation Method 2
The cameras can capture an image of the scene each time the scene is illuminated by a respective single-color light source of a particular color
Data Source
AI summary
One embodiment can provide a computer-vision system. The computer-vision system can include one or more cameras to capture images of a scene and one or more sets of single-color light sources to illuminate the scene, with a respective set of light sources comprising multiple single-color light sources of different colors. The multiple single-color light sources within a given set can be turned on sequentially, one at a time. The cameras can capture an image of the scene each time the scene is illuminated by a respective single-color light source of a particular color.