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Computer-vision based polyp detection in underwater images

Kristijan Shirgoski (2015) Computer-vision based polyp detection in underwater images. EngD thesis.

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    Object detection is a popular topic in computer vision and machine learning. Numerous approaches have been proposed in literature to address the challenging task of general object detection. The approaches vary conceptually as well as in the level of computational intensity. The goal of this thesis was to develop a pipeline of state-of-the-art algorithms to detect polyps of the Aurelia aurita jellyfish, which are densely spread across corals. In object detection problems, a mandatory task is searching the image for regions of interest, preferably of several sizes. We propose a trained aggregated channel features (ACF) model to do that. In order to later classify these regions, first they need to have some features or characteristics extracted from them. In this thesis, this is performed by a convolutional neural network (CNN) trained on the MNIST dataset. Furthermore, a binary support vector machines (SVM) classifier with linear kernel and L2-regularized logistic regression is used to classify the features and determine the probability of correctly classifying them. It is very likely that several regions are proposed for each ground truth, so the regions must undergo a non-maximum suppression which uses the probability outputs from the logistic regression to group the local regions together, greedily prioritizing based on the probability distribution. The algorithms were trained and tested on 35 images consistent of nearly 40000 rectangle annotations from a newly annotated dataset. We have achieved very promising results and analyzed the strengths and weaknesses of our approach.

    Item Type: Thesis (EngD thesis)
    Keywords: object detection, computer vision, machine learning, ACF, CNN, SVM, regression, non-maximum suppression
    Number of Pages: 68
    Language of Content: English
    Mentor / Comentors:
    Name and SurnameIDFunction
    doc. dr. Matej Kristan4053Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536643523)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3057
    Date Deposited: 08 Sep 2015 13:54
    Last Modified: 17 Nov 2015 13:31
    URI: http://eprints.fri.uni-lj.si/id/eprint/3057

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