Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications

TL;DR

This paper presents a computer vision method for recognizing liquid surfaces and phase boundaries in transparent vessels, crucial for chemical analysis and laboratory techniques, without prior knowledge of the number of phases.

Contribution

It introduces a general, curve-scanning approach that detects liquid interfaces in images, applicable to both liquid-air and liquid-liquid boundaries, enhancing automation in chemical laboratory analysis.

Findings

Effective recognition of liquid surfaces in various containers

Applicable to both liquid-air and liquid-liquid interfaces

No prior phase knowledge required

Abstract

The ability to recognize the liquid surface and the liquid level in transparent containers is perhaps the most commonly used evaluation method when dealing with fluids. Such recognition is essential in determining the liquid volume, fill level, phase boundaries and phase separation in various fluid systems. The recognition of liquid surfaces is particularly important in solution chemistry, where it is essential to many laboratory techniques (e.g., extraction, distillation, titration). A general method for the recognition of interfaces between liquid and air or between phase-separating liquids could have a wide range of applications and contribute to the understanding of the visual properties of such interfaces. This work examines a computer vision method for the recognition of liquid surfaces and liquid levels in various transparent containers. The method can be applied to recognition of both liquid-air and liquid-liquid surfaces. No prior knowledge of the number of phases is required. The method receives the image of the liquid container and the boundaries of the container in the image and scans all possible curves that could correspond to the outlines of liquid surfaces in the image. The method then compares each curve to the image to rate its correspondence with the outline of the real liquid surface by examining various image properties in the area surrounding each point of the curve. The image properties that were found to give the best indication of the liquid surface are the relative intensity change, the edge density change and the gradient direction relative to the curve normal.