Adaptive ROI Detection for Machine Vision Image Quality
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Solution Overview
Problem
Conventional machine vision systems face inefficiencies due to the use of large, static regions of interest (ROIs), leading to incorrect image quality assessments and excessive processing resources, resulting in low-quality image captures.
Innovation Solution
An adaptive ROI determination method that calculates contrast values, generates histograms, and identifies regions with high-contrast pixels to dynamically adjust imaging parameters, allowing for efficient and accurate image quality assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large, static ROI is used in conventional machine vision systems, then processing coverage is comprehensive, but processing time and resource consumption increase substantially
Solution Approach 1:
The patent divides the image into multiple candidate regions and evaluates each region's contrast characteristics independently. By segmenting the full image into manageable regions and assessing their individual contrast values, the system identifies the most relevant ROI without processing the entire image, thus reducing processing time while maintaining assessment accuracy.
Solution Approach 2:
The patent implements a dynamic ROI determination method that adapts to each captured image by calculating contrast values and generating histograms specific to that image. Unlike static conventional approaches, this dynamic method adjusts the ROI selection based on real-time image characteristics, enabling faster and more accurate identification of the true region of interest.
2Measurement precision
If a large, static ROI is used in conventional machine vision systems, then all areas are covered, but measurement accuracy of the true object of interest decreases
Solution Approach 1:
The patent applies local quality analysis by evaluating contrast characteristics of different regions individually. Each candidate region is assessed based on its own contrast histogram and area under the curve metrics, allowing the system to identify regions with superior local contrast properties that likely contain the true object of interest, thereby improving ROI identification accuracy.
Solution Approach 2:
The patent changes the evaluation parameter from uniform static ROI selection to dynamic contrast-based selection. By introducing contrast value calculations, histogram generation, and area under the curve measurements as selection criteria, the system transforms the ROI determination process into a parameter-driven approach that automatically identifies high-contrast regions containing the true object.
3Reliability
If multiple rounds of image analysis are conducted to evaluate image quality metrics, then comprehensive quality assessment is achieved, but processing resource consumption increases
Solution Approach 1:
The patent performs preliminary contrast analysis and histogram generation on candidate regions before conducting full image quality metric evaluation. By pre-identifying the most promising ROI based on contrast characteristics, the system avoids performing comprehensive multi-round analysis on entire images or irrelevant regions, thus reducing processing resource consumption while maintaining evaluation reliability.
Solution Approach 2:
The patent applies partial action by conducting contrast-based ROI selection on a subset of candidate regions rather than performing exhaustive analysis on the entire image. This partial evaluation approach identifies the true ROI efficiently, allowing subsequent detailed quality metrics to be applied only to the identified region, thereby reducing overall processing resource consumption.
Data Source
AI summary
Systems and methods for adaptively determining a region of interest (ROI) are disclosed herein. An example device includes an imaging assembly and a controller. The imaging assembly captures image data comprising pixel data from a plurality of pixels. The controller calculates a contrast value for each pixel of the plurality of pixels, generates a histogram of contrast values, calculates an area under the curve of the histogram, and determines a contrast value threshold to delineate between high-contrast value pixels and low-contrast value pixels. The controller also identifies a ROI within the image data by locating a region within the image data that (i) satisfies a pre-determined size threshold and (ii) contains a largest number of high-contrast value pixels relative to all other regions that satisfy the pre-determined size threshold, and adjusts imaging parameters of the imaging assembly based on the ROI to capture at least one subsequent image.