Using curvature to distinguish between surface reflections and vessel contents in computer vision based recognition of materials in transparent vessels

TL;DR

This paper introduces a curvature-based method to differentiate between surface reflections and contents in transparent vessels, improving material recognition accuracy in computer vision applications.

Contribution

The work proposes a novel curvature-based approach leveraging vessel symmetry to distinguish surface reflections from internal contents in transparent containers.

Findings

Enhanced accuracy in material recognition within transparent vessels.

Effective identification of vessel surface reflections using curvature analysis.

Improved robustness of computer vision systems in industrial and laboratory settings.

Abstract

The recognition of materials and objects inside transparent containers using computer vision has a wide range of applications, ranging from industrial bottles filling to the automation of chemistry laboratory. One of the main challenges in such recognition is the ability to distinguish between image features resulting from the vessels surface and image features resulting from the material inside the vessel. Reflections and the functional parts of a vessels surface can create strong edges that can be mistakenly identified as corresponding to the vessel contents, and cause recognition errors. The ability to evaluate whether a specific edge in an image stems from the vessels surface or from its contents can considerably improve the ability to identify materials inside transparent vessels. This work will suggest a method for such evaluation, based on the following two assumptions: 1) Areas of high curvature on the vessel surface are likely to cause strong edges due to changes in reflectivity, as is the appearance of functional parts (e.g. corks or valves). 2) Most transparent vessels (bottles, glasses) have high symmetry (cylindrical). As a result the curvature angle of the vessels surface at each point of the image is similar to the curvature angle of the contour line of the vessel in the same row in the image. These assumptions, allow the identification of image regions with strong edges corresponding to the vessel surface reflections. Combining this method with existing image analysis methods for detecting materials inside transparent containers allows considerable improvement in accuracy.