Line-Scan Imaging Chamber for Small Aquatic Organism Classification
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Solution Overview
Problem
Conventional instruments fail to capture high-resolution images of small aquatic organisms less than 50 mm in size, lack color camera technology, and are inflexible for field or lab use, lacking means for controlled sample flow and taxonomical classification, and require preserved samples.
Innovation Solution
An image acquisition system with a fluid flow chamber, line scan camera, and illuminating assembly, including transparent windows, diffusers, and back light windows, capable of capturing colored images of aquatic organisms in real-time, and utilizing machine learning for taxonomical classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image acquisition instruments are used, then images of aquatic organisms can be captured, but the resolution is insufficient for small organisms less than 50 mm in size
Solution Approach 1:
The patent implements a specialized imaging chamber with controlled lighting and magnification optics specifically designed for small organisms. The system uses a combination of macro lenses and controlled illumination fields to achieve high resolution images of organisms less than 50mm, applying local quality enhancement where it is most needed rather than using general-purpose imaging equipment.
Solution Approach 2:
The system changes key imaging parameters including magnification level, lighting intensity, and flow rate to optimize image quality for small organisms. The imaging chamber allows adjustment of these parameters to capture diagnostic features of tiny aquatic organisms that would be invisible or indistinct with conventional imaging settings.
2Productivity
If conventional sampling instruments are used, then sample collection can be performed, but labor and processing time are excessive
Solution Approach 1:
The patent replaces manual mechanical sorting and identification processes with an automated imaging system. The instrument captures images of organisms as they pass through the flow chamber, and machine learning algorithms automatically classify them by taxon, replacing the need for manual laboratory processing and significantly reducing both labor and time requirements.
Solution Approach 2:
The system performs self-service classification through integrated machine learning models that automatically identify and categorize organisms from captured images. This eliminates the need for expert taxonomists to manually examine each sample, allowing the instrument to service itself in terms of data processing and classification.
3Productivity
If conventional instruments are used, then sample processing can be done, but costs are high
Solution Approach 1:
The patent extracts the classification function from expensive manual laboratory processes and integrates it into an automated instrument. By taking out the need for manual expertise and laboratory infrastructure, the system reduces operational costs while maintaining or improving processing capacity.
4Adaptability or versatility
If conventional instruments are used, then imaging can be performed, but flexibility for field or lab use is limited
Solution Approach 1:
The patent designs the imaging instrument with universal applicability for both field and laboratory use. The system includes features such as portable power options, ruggedized housing, and adaptable sampling interfaces that allow the same instrument to function effectively in diverse environments, eliminating the need for separate field and lab equipment.
5Measurement precision
If conventional instruments are used, then sample analysis can be performed, but taxonomical classification capability is insufficient
Solution Approach 1:
The patent replaces complex manual taxonomical classification processes with machine learning-based automated classification. The system uses image recognition algorithms trained on taxonomic data to automatically identify organisms to appropriate taxonomic levels, achieving high classification accuracy without requiring complex manual procedures or expert intervention.
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
Enables efficient sampling and classification of small aquatic organisms, reducing labor and costs, and providing high-resolution images for accurate taxonomical identification.
Implementation Method 1
The image acquisition system may comprise an illuminating assembly comprising a first-surface mirror
Implementation Method 2
a diffuser element may be connected to one of the transparent windows other than the proximal transparent window to create an even field of light falling on the transparent windows
Implementation Method 3
a back light window provides a surface to the diffuser
Data Source
AI summary
Improved methods and systems for image acquisition of small aquatic organisms at high resolution are disclosed. The methods and systems disclosed herein provide taxonomical classification of the small aquatic organisms through captured images. They also provide a platform that allows easy extension and/or adaptation and/or customization of properties of the instrument for specific imaging and sampling requirements based on the environment where the sampling is done, the amount of water that requires to be sampled, the speed with which sampling needs to be carried out, and the specific nature of organisms that need to be sampled around or below a 50 mm size range.