For the last several years, convolutional neural network (CNN) based ob- ject detection systems have used a regression technique to predict improved object bounding boxes based on an initial proposal using low-level image features extracted from the CNN. In spite of its prevalence, there is little critical analysis of bounding-box regression or in-depth performance evaluation. This thesis surveys an array of techniques and parameter settings in order to further optimize bounding-box regression and provide guidance for its implementation. I refute a claim regarding training procedure, and demonstrate the effectiveness of using principal component analysis to handle unwieldy numbers of features produced by very deep CNNs.
|Commitee:||Liu, Feng, Massey, Barton|
|School:||Portland State University|
|School Location:||United States -- Oregon|
|Source:||MAI 57/01M(E), Masters Abstracts International|
|Keywords:||Computer vision, Convolutional neural network, Localization, Machine learning, Object detection|
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