Auto-Exposure Control Using 3D Region Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional auto-exposure techniques in imaging systems often fail to provide sufficient grayscale information for objects of interest, especially in environments with changing ambient light conditions, leading to poor image quality and inaccurate object detection in machine vision applications.
Innovation Solution
The system uses 3D information to identify a region of interest within a 2D image and applies auto-exposure adjustments specifically to this region, ensuring that light levels fall within the linear range, thereby enhancing grayscale information and detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional auto-exposure techniques are applied to the entire image, then the overall image quality is improved, but the grayscale information for specific objects of interest is insufficient
Solution Approach 1:
The patent divides the image into multiple regions of interest based on 3D depth information. Each region is processed independently with region-specific auto-exposure adjustments, allowing grayscale information to be optimized for specific objects while maintaining overall image quality.
Solution Approach 2:
The patent applies different auto-exposure parameters to different spatial regions of the image based on 3D feature analysis. Regions containing objects of interest receive customized exposure adjustments to ensure sufficient grayscale information, while other regions maintain their original or differently optimized settings.
2Adaptability or versatility
If the linear range is set to cover the entire scene, then all objects are visible, but objects of interest lose grayscale differentiation
Solution Approach 1:
The patent segments the scene into multiple depth-based regions, allowing each region to have its own linear range optimization. This enables the system to maintain broad scene coverage while providing enhanced grayscale differentiation for specific regions containing objects of interest.
Solution Approach 2:
The patent dynamically adjusts acquisition property parameters (exposure time, gain, offset) based on 3D region analysis. By changing these parameters locally for different depth regions, the system optimizes grayscale information for objects of interest while maintaining visibility of the entire scene.
3Device complexity
If auto-exposure is adjusted based on 2D image analysis only, then processing is simple, but detection accuracy deteriorates in varying light conditions
Solution Approach 1:
The patent incorporates 3D depth information from time-of-flight or stereo vision sensors to complement 2D image analysis. This additional dimensional data enables more reliable region segmentation and auto-exposure control, improving detection accuracy in varying lighting conditions without significantly increasing processing 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
This approach results in higher quality images and more accurate detection of objects, such as people passing through doorways, by dynamically adjusting acquisition property settings based on 3D features, even under varying lighting conditions.
Implementation Method 1
Imaging sensors are light sensitive devices that convert light into electrical charges. These devices are typically composed of an array of light sensitive diodes called pixels that convert photons into electrons when exposed to light.
Data Source
AI summary
A system and method of auto-exposure control is provided for image acquisition hardware using three dimensional information to identify a region(s) of interest within an acquired 2D image or images upon which to apply traditional auto-exposure techniques. By performing auto-exposure analysis over the region of interest, the acquisition property settings can be assigned such that the light levels within the region of interest fall within the linear range, producing sufficient grayscale information for identifying particular objects and profiles in subsequently acquired images. For example, in a machine vision application that detects people passing through a doorway, the region of interest can be the portion of the 2D image that generated 3D features of a head and shoulders profile within a 3D model of the doorway scene. With higher quality images, more accurate detection of people candidates within the monitored scene results.