Laser Scanner Image Segmentation for Precise Scan Area Selection
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Solution Overview
Problem
Existing laser scanning methods struggle with accurately defining the scanning area, often requiring manual selection of polygons or bounding boxes, which is time-consuming and inefficient for high-accuracy, high-data-volume scanning.
Innovation Solution
An image-based method using machine learning algorithms to automatically segment pixels of interest based on visual coherence, allowing for precise definition of the scanning area by selecting a single pixel and automatically identifying similar pixels, with optional buffer zones to ensure complete coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of polygons or bounding boxes is used to define scanning area, then scanning area can be defined, but it is time-consuming and inefficient
Solution Approach 1:
The system performs automatic segmentation of the scanning area using machine learning algorithms that analyze visual coherence of pixels. The algorithm autonomously identifies and segments the area of interest without requiring manual user input, making the system self-serve the task of scanning area definition while maintaining high precision.
Solution Approach 2:
The manual mechanical process of drawing polygons or bounding boxes is replaced by an automated image processing system using machine learning. The system substitutes human manual operations with algorithmic pixel analysis based on visual coherence, eliminating the time-consuming manual interaction while preserving accurate scanning area definition.
2Productivity
If automatic pixel segmentation based on visual coherence is used, then scanning efficiency is improved, but system complexity increases due to machine learning algorithms
Solution Approach 1:
The machine learning algorithm acts as an intermediary between the raw image data and the scanning system. It processes pixel visual coherence information and translates it into segmented scanning area definitions, serving as a mediator that automates the complex task while presenting a simple interface to the user requiring only minimal input.
3Quantity of substance
If scanning area is limited to area of interest only, then data volume is reduced, but coverage of distributed or partitioned areas may be incomplete
Solution Approach 1:
The system extends the scanning area definition from a single contiguous region to multiple distributed regions by analyzing visual coherence across the entire image space. The algorithm operates in the dimensional space of all pixels, identifying separate coherent areas that may be partitioned or distributed, ensuring complete coverage of all relevant regions while excluding unnecessary areas.
Data Source
AI summary
Image-based method of defining a scanning area with a measuring radiation-based measuring device such as a laser scanner, the method comprising roughly aligning a targeting axis of the measuring device towards an area of interest to be scanned, the area of interest having a coherent visual property, capturing with known orientation to the targeting axis an image covering the area of interest, displaying the image to a user, manually selecting by the user of at least one pixel of a group of pixels representing the area of interest in the image, automatically segmenting the group of pixels based on a similarity to the at least one selected pixel using a machine learning algorithm, and automatically defining the scanning area based on border pixels of the first group of pixels.