Dr. Aya Saad Profile

Dr. Aya Saad

at Norwegian Univ of Science and Technology

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Publications (4)

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Proceedings Article | 5 March 2022 Paper

MOG: a background extraction approach for data augmentation of time-series images in deep learning segmentation

Jonas Nagell Borgersen, Aya Saad, Annette Stahl

Proceedings Volume 12084, 1208419 (2022) https://doi.org/10.1117/12.2622899

KEYWORDS: Image segmentation, Data modeling, Neural networks, Image processing algorithms and systems, Organisms, Machine vision

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Image segmentation is one of the key components in systems performing computer vision recognition tasks. Various algorithms for image segmentation have been developed in the literature. Among them, more recently, deep learning algorithms have been remarkably successful in performing this task. A downside with deep neural networks for segmentation is that they require a large amount of labeled dataset for training. This prerequisite is one of the main reasons that led researchers to adopt data augmentation approaches in order to minimize manual labeling efforts while maintaining highly accurate results. This paper uses classical non-deep learning methods for background extraction to increase the size of the dataset used to train deep learning attention segmentation algorithms when images are presented as time-series to the model. The method presented adopts the Gaussian mixture-based (MOG2) foreground-background segmentation followed by dilation and erosion to create masks necessary to train the deep learning models. It is applied in the context of planktonic images captured in situ as time series. Various evaluation metrics and visual inspection are used to compare the performance of the deep learning algorithms. Experimental results show higher accuracy achieved by the deep learning algorithms for time-series image attention segmentation when the proposed data augmentation methodology is utilized to increase the training dataset.

Proceedings Article | 5 March 2022 Paper

Robust deep unsupervised learning framework to discover unseen plankton species

Eivind Salvesen, Aya Saad, Annette Stahl

Proceedings Volume 12084, 120840V (2022) https://doi.org/10.1117/12.2622489

KEYWORDS: Feature extraction, Data modeling, Machine learning, Expectation maximization algorithms, Performance modeling, Image classification, Computer programming, Statistical modeling, Detection and tracking algorithms, Machine vision

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Deep convolutional neural networks have proven effective in computer vision, especially in the task of image classification Nevertheless, the success is limited to supervised learning approaches, requiring extensive amounts of labeled training data that impose time-consuming manual efforts. Unsupervised deep learning methods were introduced to overcome this challenge. The gap, however, towards achieving comparable classification accuracy to supervised learning is still significant. This paper presents a deep learning framework for images of planktonic organisms with no ground truth or manually labeled data. This work combines feature extraction methods using state-of-the-art unsupervised training schemes with clustering algorithms to minimize the labeling effort while improving the classification process based on essential features learned by the deep learning model. The models utilized in the framework are tested over existing planktonic data sets. Empirical results show that unsupervised approaches that cluster the data based on the deep learning model’s feature space representations improve the classification task and can identify classes that have not been seen during the learning process.

Proceedings Article | 30 June 2021 Paper

A combined informative and representative active learning approach for plankton taxa labeling

Martin Lund Haug, Aya Saad, Annette Stahl

Proceedings Volume 11878, 118781Q (2021) https://doi.org/10.1117/12.2601096

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With an ever-increasing amount of image data, the manual labeling process has become the bottleneck in many machine learning applications. Plankton taxa labeling is especially a challenge due to its complex nature, and the manual labeling effort places a large burden on the domain experts. The Active Learning (AL) paradigm is a promising research direction adopted in the literature to minimize the manual labeling effort exerted by domain experts. Many approaches for AL have been proposed over the recent years to improve the labeling task by supporting the construction of large datasets suitable to train machine learning models while minimizing human involvement in the process. Our empirical study suggests that many modern active learning methods fail to incorporate both the samples that represent the statistical pattern of the data and the samples in which the machine learning model is not confident about. Inspired by these limitations, we propose an algorithm that combines these two types of sampling in order to capture the data distribution of the whole feature space, prevent redundant sampling from correlated uncertainty queries and finetune the inter-class decision boundary. Our experiments show that the proposed method outperforms each of the methods separately. Further, it also proves to be efficient on both the CIFAR dataset and the more complex Kaggle plankton dataset.

Proceedings Article | 4 January 2021 Paper

An instance segmentation framework for in-situ plankton taxa assessment

Aya Saad, Sondre Bergrum, Annette Stahl

Proceedings Volume 11605, 1160511 (2021) https://doi.org/10.1117/12.2587693

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In this paper, we propose a deep learning instance segmentation framework for particle extraction of microscopic images that aims at calculating planktonic species distribution and concentration in-situ. The framework comprises three essential functional tasks on in-situ time-series images collected from an autonomous underwater vehicle: 1) manual labeling of the captured images, 2) object localization, segmentation, and identification, and 3) class distribution and planktonic organisms concentration calculation. Our proposed framework is based on the mask R-CNN architecture provided by the Detectron2 library developed by Facebook Artificial Intelligence Research (FAIR) for instance segmentation. Due to its modular design, we compare the performance of different networks by alternating the backbone sub-network in order to choose the most suitable architecture for the task of instance and semantic segmentation. We compile a custom annotated dataset from planktonic time-series images and train the different models over this dataset to perform the instance semantic segmentation. Evaluation results of the proposed framework, utilizing the best performing deep learning architecture along with the new annotated dataset, show better performance in terms of speed and accuracy of both in-situ segmentation and classification compared to traditional segmentation methods. In addition, we observe a significant improvement in the object classification quality when we train the model over our newly annotated dataset instead of training it over the dataset generated from the traditional methods. The inferred data from our novel instance segmentation framework, which provides the particle class distribution and concentration, can then be used to assist in constructing a dynamic probability density map of planktonic communities dispersion and abundance.

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