Estimation of Bubble Size Distribution Based on Power Spectrum

Ilonen, Jarmo; Eerola, Tuomas; Mutikainen, Heikki; Lensu, Lasse; Käyhkö, Jari; Kälviäinen, Heikki

doi:10.1007/978-3-319-12568-8_5

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…3) (Fu et al 2019). As mentioned in the introduction, BubGAN generates more realistic synthetic bubble images for machine learning training (Hass et al 2020) compared to conventional method by applying modelled pixel intensity variation on simple shapes such as circles and ellipses (Ilonen et al 2014 andKarn et al 2015b). The machine learning model used for bubble image analysis in this section is trained on a 2000 synthetic images and their corresponding labels.…”

Section: Assessment Of Proposed Methodsmentioning

confidence: 99%

Machine learning shadowgraph for particle size and shape characterization

Shao

Hong

2020

Meas. Sci. Technol.

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Conventional image processing for a particle shadow image is usually time-consuming and suffers degraded image segmentation when dealing with images consisting of complex-shaped and clustered particles with varying backgrounds. In this paper, we introduce a robust learning-based method using a single convolution neural network for analyzing particle shadow images. Our approach employs a two-channel-output U-net model to generate a binary particle image and a particle centroid image. The binary particle image is subsequently segmented through a marker-controlled watershed approach with the particle centroid image as the marker image. The assessment of this method on both synthetic and experimental bubble images has exhibited a better performance compared to the state-of-art non-machine-learning method. The proposed machine learning shadow image processing approach provides a promising tool for real-time particle image analysis in industrial applications.

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Section: Assessment Of Proposed Methodsmentioning

confidence: 99%

Machine learning shadowgraph for particle size and shape characterization

Shao

Hong

2020

Meas. Sci. Technol.

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“…For example, Poletaev's CNN takes ∼8 seconds to achieve 94%-96% accuracy. One method that merits further investigation is direct estimation of the volume distribution from the power spectrum of the image [8].…”

Section: Related Workmentioning

confidence: 99%

Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis

Levental,

Chard,

Libera

et al. 2020

Preprint

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Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more. FSP instruments are highly dependent on a number of adjustable parameters, including fuel injection rate, fuel-oxygen mixtures, and temperature, which can greatly affect the quality, quantity, and properties of the yielded nanoparticles. Optimizing FSP synthesis requires monitoring, analyzing, characterizing, and modifying experimental conditions. Here, we propose a hybrid CPU-GPU Difference of Gaussians (DoG) method for characterizing the volume distribution of unburnt solution, so as to enable near-real-time optimization and steering of FSP experiments. Comparisons against standard implementations show our method to be an order of magnitude more efficient. This surrogate signal can be deployed as a component of an online end-to-end pipeline that maximizes the synthesis yield.

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“…LBP descriptor transforms an image to an array of integer labels, which can represent the small-scale appearance of the image [132]. The Fourier power spectrum of an image or a region is the square of the magnitude of its Fourier transform [70]. Gabor filters [77] can extract image information in different scales and orientations, so they are one of the most powerful local appearance descriptors.…”

Section: Terminal Setmentioning

confidence: 99%

Genetic Programming for Supervised Figure-ground Image Segmentation

Liang¹

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<p>Figure-ground segmentation is a process of separating regions of interest from unimportant backgrounds. It is essential to various applications in computer vi- sion and image processing, e.g. object tracking and image editing, as they are only interested in certain regions of an image and use figure-ground segmenta- tion as a pre-processing step. Traditional figure-ground segmentation methods often require heavy human workload (e.g. ground truth labeling), and/or rely heavily on human guidance (e.g. locating an initial model), accordingly cannot easily adapt to diverse image domains. Evolutionary computation (EC) is a family of algorithms for global optimi- sation, which are inspired by biological evolution. As an EC technique, genetic programming (GP) can evolve algorithms automatically for complex problems without pre-defining solution models. Compared with other EC techniques, GP is more flexible as it can utilise complex and variable-length representations (e.g. trees) of candidate solutions. It is hypothesised that this flexibility of GP makes it possible to evolve better solutions than those designed by experts. However, there have been limited attempts at applying GP to figure-ground segmentation. In this thesis, GP is enabled to successfully address figure-ground segmentation through evolving well-performing segmentors and generating effective features. The objectives are to investigate various image features as inputs of GP, develop multi-objective approaches, develop feature selection/construction methods, and conduct further evaluations of the proposed GP methods. The following new methods have been developed. Effective terminal sets of GP are investigated for figure-ground segmentation, covering three general types of image features, i.e. colour/brightness, texture and shape features. Results show that texture features are more effective than intensities and shape features as they are discriminative for different materials that foreground and background regions normally belong to (e.g. metal or wood). Two new multi-objective GP methods are proposed to evolve figure-ground segmentors, aiming at producing solutions balanced between the segmentation performance and solution complexity. Compared with a reference method that does not consider complexity and a parsimony pressure based method (a popular bloat control technique), the proposed methods can significantly reduce the solution size while achieving similar segmentation performance based on the Mann- Whitney U-Test at the significance level 5%. GP is introduced for the first time to conduct feature selection for figure- ground segmentation tasks, aiming to maximise the segmentation performance and minimise the number of selected features. The proposed methods produce feature subsets that lead to solutions achieving better segmentation performance with lower features than those of two benchmark methods (i.e. sequential forward selection and sequential backward selection) and the original full feature set. This is due to GP’s high search ability and higher likelihood of finding the global optima. GP is introduced for the first time to construct high-level features from primitive image features, which aims to improve the image segmentation performance, especially on complex images. By considering linear/non-linear interactions of the original features, the proposed methods construct fewer features that achieve better segmentation performance than the original full feature set. This investigation has shown that GP is suited for figure-ground image segmentation for the following reasons. Firstly, the proposed methods can evolve segmentors with useful class characteristic patterns to segment various types of objects. Secondly, the segmentors evolved from one type of foreground object can generalise well on similar objects. Thirdly, both the selected and constructed features of the proposed GP methods are more effective than original features, with the selected/constructed features being better for subsequent tasks. Finally, compared with other segmentation techniques, the major strengths of GP are that it does not require pre-defined problem models, and can be easily adapted to diverse image domains without major parameter tuning or human intervention.</p>

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Estimation of Bubble Size Distribution Based on Power Spectrum

Cited by 7 publications

References 13 publications

Machine learning shadowgraph for particle size and shape characterization

Machine learning shadowgraph for particle size and shape characterization

Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis

Genetic Programming for Supervised Figure-ground Image Segmentation

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