Evaluation of Image Filtering Parameters for Plant Biometrics Improvement Using Machine Learning

Soft Computing and its Engineering Applications - Communications in Computer and Information Science ◽

10.1007/978-981-16-0708-0_25 ◽

2021 ◽

pp. 301-315

Author(s):

Taiwo Olaleye ◽

Oluwasefunmi Arogundade ◽

Cecelia Adenusi ◽

Sanjay Misra ◽

Abosede Bello

Keyword(s):

Machine Learning ◽

Image Filtering

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Biomedical Microscopic Image Processing by Graphs

Advances in Bioinformatics and Biomedical Engineering - Biomedical Image Analysis and Machine Learning Technologies ◽

10.4018/978-1-60566-956-4.ch009 ◽

2010 ◽

pp. 197-213

Author(s):

Vinh-Thong Ta ◽

Olivier Lézoray ◽

Abderrahim Elmoataz

Keyword(s):

Machine Learning ◽

Image Processing ◽

Image Filtering ◽

Microscopic Image ◽

Histological Image ◽

Supervised Clustering ◽

Arbitrary Structure ◽

Microscopic Image Processing ◽

Discrete Domain

The authors present an overview of part of their work on graph-based regularization. Introduced first in order to smooth and filter images, the authors have extended these methods to address semi-supervised clustering and segmentation of any discrete domain that can be represented by a graph of arbitrary structure. This framework unifies, within a same formulation, methods from machine learning and image processing communities. In this chapter, the authors propose to show how these graph-based approaches can lead to a useful set of tools that can be combined altogether to address various image processing problems in pathology such as cytological and histological image filtering, segmentation and classification.

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Super-Resolution Ultrasound Image Filtering with Machine-Learning to Reduce the Localization Error

2019 IEEE International Ultrasonics Symposium (IUS) ◽

10.1109/ultsym.2019.8925861 ◽

2019 ◽

Author(s):

Sevan Harput ◽

Enrico Grisan ◽

Chris Dunsby ◽

Meng-Xing Tang ◽

Long Hin Fong ◽

...

Keyword(s):

Machine Learning ◽

Ultrasound Image ◽

Super Resolution ◽

Image Filtering ◽

Localization Error

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Gabor Filter Selection and Computational Processing for Emotion Recognition

10.33774/miir-2021-5q5j6 ◽

2021 ◽

Author(s):

Erhan Coşkun ◽

Torran Elson ◽

Sean Lim ◽

James Mathews ◽

Gruff Morris ◽

...

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Processing Speed ◽

Cost Efficiency ◽

Learning Algorithm ◽

Gabor Filter ◽

Image Filtering ◽

Local Binary Patterns ◽

Gabor Filters ◽

Machine Learning Algorithm

CrowdEmotion produce software to measure a person's emotions based on analysis of microfacial expressions using a machine learning algorithm to recognize which features correspond with which emotions. The features are derived by applying a bank of Gabor filters to a set of frames. CrowdEmotion needed to improve the accuracy, processing speed and cost-efficiency of the tool. In particular they wanted to know if a subset of the bank of Gabor filters was sufficient, and whether the image filtering stage could be implemented on a GPU. A framework for choosing the optimum set of Gabor filters was established and ways of reducing the dimensionality of this were interrogated. Taking a subset of Local Binary Patterns was found to be fully justified. Meanwhile choosing a gridding pattern is open to interpretation; some suggestions were made about how this choice might be improved.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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