Image Processor Plane Estimation for Faster Workpiece Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying a workpiece on images captured by a camera require extensive processing due to variations in shape, size, color, and posture, which complicates the identification process.
Innovation Solution
An image processor that includes modules for extracting a partial image range, detecting distances, estimating planes and angles, and identifying objects based on stored object shapes, reducing the processing required for object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to identify workpieces, then identification accuracy can be maintained, but processing time and computational load increase significantly
Solution Approach 1:
The patent divides the image processing task into multiple segments: extracting interest points, clustering them into candidate sets, selecting representative points, and generating simplified contours. This segmentation reduces computational complexity by processing only critical features rather than the entire image, thereby reducing processing time while maintaining identification accuracy.
Solution Approach 2:
The patent extracts only the necessary information from the image - specifically interest points and their spatial relationships - while discarding redundant visual data. By taking out only the essential features for identification, the system reduces processing load while preserving the accuracy needed for workpiece recognition.
2Reliability
If comprehensive image processing is performed to handle various workpiece shapes and postures, then identification reliability improves, but device complexity increases
Solution Approach 1:
The patent creates a universal identification framework that handles multiple workpiece shapes, sizes, and postures through a single set of operations. The interest point extraction and clustering mechanisms work generally across different object types, eliminating the need for shape-specific processing algorithms and reducing overall system complexity.
Solution Approach 2:
The patent adapts to different workpiece characteristics by dynamically adjusting processing parameters such as interest point detection thresholds, clustering criteria, and contour generation settings. This parameter-based adaptation allows the system to maintain high reliability across diverse workpieces without increasing structural complexity.
3Measurement precision
If detailed contour analysis is performed to identify objects, then identification accuracy improves, but processing speed decreases
Solution Approach 1:
The patent performs preliminary actions by pre-extracting interest points and pre-clustering them into candidate sets before final identification. This preliminary processing organizes the data structure in advance, allowing the final contour generation and identification steps to proceed more quickly, thus improving processing speed without sacrificing accuracy.
Data Source
AI summary
An image processor includes memory configured to store the shape of an object, extracting circuitry configured to extract a second image of a target range from a first image of the object, distance detecting circuitry configured to process the second image to detect distances from at least three parts projected within the target range to the camera, plane estimating circuitry configured to estimate a plane projected within the target range using the distances based on the at least three parts, angle detecting circuitry configured to detect an angle of the plane with respect to an optical axis of the camera, contour estimating circuitry configured to estimate, based on the shape of the object and the angle of the plane, a contour of the object projected on the first image, and identifying circuitry configured to identify the object on the first image based on the contour.