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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC Riverside Electronic Theses and Dissertations (2024)

Scene understanding is an important aspect of computer vision, encompassing a variety of tasks such as image classification, object detection, scene graph generation, and action recognition. With the advances of deep learning models, scene understanding has seen significant improvements in accuracy, robustness, and efficiency, enabling more sophisticated and reliable applications in automated sports analytics, visual relationship detection, and dynamic neural networks. Deep learning techniques that perform on standardized benchmarks often struggle when applied in real-world scenarios where the environment variables are often unconstrained and diverse, resulting in challenges that can hinder their accuracy and generalizability. This thesis delves into the evolution of scene understanding through deep learning, and presents novel approaches for specialized tasks like jersey number detection, scene graph generation, and dynamically throttleable neural networks. The developed techniques enhance the deep model’s ability to learn robust and transferable representations, enabling better generalization across diverse visual domains. Both theory and experimentation will be presented.

    Cover page: Deep Learning Approaches for Scene UnderstandingCreative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC Riverside Previously Published Works (2021)

    Significance

    Automated understanding of human embryonic stem cell (hESC) videos is essential for the quantified analysis and classification of various states of hESCs and their health for diverse applications in regenerative medicine.

    Aim

    This paper aims to develop an ensemble method and bagging of deep learning classifiers as a model for hESC classification on a video dataset collected using a phase contrast microscope.

    Approach

    The paper describes a deep learning-based random network (RandNet) with an autoencoded feature extractor for the classification of hESCs into six different classes, namely, (1) cell clusters, (2) debris, (3) unattached cells, (4) attached cells, (5) dynamically blebbing cells, and (6) apoptotically blebbing cells. The approach uses unlabeled data to pre-train the autoencoder network and fine-tunes it using the available annotated data.

    Results

    The proposed approach achieves a classification accuracy of 97.23  ±  0.94  %   and outperforms the state-of-the-art methods. Additionally, the approach has a very low training cost compared with the other deep-learning-based approaches, and it can be used as a tool for annotating new videos, saving enormous hours of manual labor.

    Conclusions

    RandNet is an efficient and effective method that uses a combination of subnetworks trained using both labeled and unlabeled data to classify hESC images.
      Cover page: Human embryonic stem cell classification: random network with autoencoded feature extractor
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