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Motion Analysis In Still Images

Tharra, Reema
http://rave.ohiolink.edu/etdc/view?acc_num=dayton170232521919022

Abstract Details

2023, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
In the vast and intricate research area of computer vision, image classification has numerous real-world applications. Major study areas include the introduction of images in real-world applications like social entertainment, security, and healthcare. In this research, we provide a novel method for identifying optical illusions in images. Motion analysis is one of the means of describing various types of illusion images. For this proposed method we built a dataset and trained a neural network for classification. Using the dataset of 600 illusion images, the approach trains a complex deep neural network to investigate the effects of patterns, colors, and forms on visual perception. This network has a pre-trained model that is used to identify motion illusions in images. The images are mostly classified under motion illusion images and still non-illusion images. For the pre-trained model training, a comparative method was also utilized for state-of-the-art models. These models and mainly VGG16, ResNet-50, and a self-built Convolution Neural Network (CNN). We achieved good results on the above-mentioned training models. This research may eventually lead to the development of a new field of illusion detection study.
Ju Shen (Committee Member)
Tam V. Nguyen (Advisor)
Tom Ongwere (Committee Member)
41 p.
Computer Science
MotionAnalysis; StillImages; ComputerVision; ImageClassification; OpticalIllusions; NeuralNetworks; VisualPerception; PatternRecognition; DeepLearning; VGG16 ResNet-50; ConvolutionalNeuralNetworks; IllusionDetection; ImageProcessing; PatternAnalysis; MachineLearning; ComparativeAnalysis; VisualIllusions; ImageRecognition; ComputationalModels; ArtificialIntelligence

Recommended Citations

Citations

  • Tharra, R. (2023). Motion Analysis In Still Images [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton170232521919022

    APA Style (7th edition)

  • Tharra, Reema. Motion Analysis In Still Images. 2023. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton170232521919022.

    MLA Style (8th edition)

  • Tharra, Reema. "Motion Analysis In Still Images." Master's thesis, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton170232521919022

    Chicago Manual of Style (17th edition)

dayton170232521919022
22
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This open access ETD is published by University of Dayton and OhioLINK.