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Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach
Computer Vision and Pattern Recognition · Submitted 31 Jan 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The method gave high accuracy in boundary recognition for a wide range of liquid, solid, granular and powder materials in various glass vessels from everyday life and the chemistry laboratory, such as bottles, jars, Glasses, Chromotography colums and separatory funnels.
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The abstract, sentence by sentence
Detection of boundaries of materials stored in transparent vessels is essential for identifying properties such as liquid level and phase boundaries, which are vital for controlling numerous processes in the industry and chemistry laboratory. This work presents a computer vision method for identifying the boundary of materials in transparent vessels using the graph-cut algorithm. The method receives an image of a transparent vessel containing a material and the contour of the vessel in the image. The boundary of the material in the vessel is found by the graph cut method. In general the method uses the vessel region of the image to create a graph, where pixels are vertices, and the cost of an edge between two pixels is inversely correlated with their intensity difference. The bottom 10% of the vessel region in the image is assumed to correspond to the material phase and defined as the graph and source. The top 10% of the pixels in the vessels are assumed to correspond to the air phase and defined as the graph sink. The minimal cut that splits the resulting graph between the source and sink (hence, material and air) is traced using the max-flow/min-cut approach. This cut corresponds to the boundary of the material in the image. The method gave high accuracy in boundary recognition for a wide range of liquid, solid, granular and powder materials in various glass vessels from everyday life and the chemistry laboratory, such as bottles, jars, Glasses, Chromotography colums and separatory funnels.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Detection of boundaries of materials stored in transparent vessels is essential for identifying properties such as liquid level and phase boundaries, which are vital for controlling numerous processes in the industry and chemistry laboratory. | background 1.00 | 0.05 | 0.05 | asserted 1.00 |
| s1 | This work presents a computer vision method for identifying the boundary of materials in transparent vessels using the graph-cut algorithm. | contribution 0.99 | 0.89 | 0.16 | asserted 1.00 |
| s2 | The method receives an image of a transparent vessel containing a material and the contour of the vessel in the image. | method 1.00 | 0.47 | 0.02 | asserted 0.99 |
| s3 | The boundary of the material in the vessel is found by the graph cut method. | method 0.98 | 0.80 | 0.09 | asserted 1.00 |
| s4 | In general the method uses the vessel region of the image to create a graph, where pixels are vertices, and the cost of an edge between two pixels is inversely correlated with their intensity difference. | method 1.00 | 0.68 | 0.03 | asserted 0.96 |
| s5 | The bottom 10% of the vessel region in the image is assumed to correspond to the material phase and defined as the graph and source. | method 1.00 | 0.65 | 0.03 | asserted 0.96 |
| s6 | The top 10% of the pixels in the vessels are assumed to correspond to the air phase and defined as the graph sink. | method 1.00 | 0.65 | 0.03 | asserted 0.95 |
| s7 | The minimal cut that splits the resulting graph between the source and sink (hence, material and air) is traced using the max-flow/min-cut approach. | method 1.00 | 0.75 | 0.07 | asserted 1.00 |
| s8 | This cut corresponds to the boundary of the material in the image. | method 0.66 | 0.80 | 0.21 | asserted 1.00 |
| s9 | The method gave high accuracy in boundary recognition for a wide range of liquid, solid, granular and powder materials in various glass vessels from everyday life and the chemistry laboratory, such as bottles, jars, Glasses, Chromotography colums and separatory funnels. | result 1.00 | 0.93 | 0.75 | asserted 1.00 |
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The paper
Original abstract
Detection of boundaries of materials stored in transparent vessels is essential for identifying properties such as liquid level and phase boundaries, which are vital for controlling numerous processes in the industry and chemistry laboratory. This work presents a computer vision method for identifying the boundary of materials in transparent vessels using the graph-cut algorithm. The method receives an image of a transparent vessel containing a material and the contour of the vessel in the image. The boundary of the material in the vessel is found by the graph cut method. In general the method uses the vessel region of the image to create a graph, where pixels are vertices, and the cost of an edge between two pixels is inversely correlated with their intensity difference. The bottom 10% of the vessel region in the image is assumed to correspond to the material phase and defined as the graph and source. The top 10% of the pixels in the vessels are assumed to correspond to the air phase and defined as the graph sink. The minimal cut that splits the resulting graph between the source and sink (hence, material and air) is traced using the max-flow/min-cut approach. This cut corresponds to the boundary of the material in the image. The method gave high accuracy in boundary recognition for a wide range of liquid, solid, granular and powder materials in various glass vessels from everyday life and the chemistry laboratory, such as bottles, jars, Glasses, Chromotography colums and separatory funnels.
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3 arXiv papers cite this in 3 sentences
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Cites it as background 1 paper
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Initially, they investigated traditional image processing methods such as edge detection [ref] and graph-cuts [ref] .
Vision-based robot manipulation of transparent liquid containers in a laboratory setting · Vision-based Liquid Volume Estimation · 0.83
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