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Title: Automatic image cropping with Faster_RCNN
Authors: Vu, Ha Son
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2017
Abstract: Convolutional Neural Networks have been proven useful in many computer vision tasks such as image classification and object detection. On the other hand, automatic image cropping remains a challenging task given its subjective nature. This project explored the performance of an object detection method, Faster R-CNN, in doing automatic image cropping task to enhance image composition. The focus of the study is on three common compositional rules: Leading Lines, Space-to-move and Symmetry/Reflection. The final model was subsequently used to build a web application that helped inexperienced photographers to do cropping to enhance their image composition according to the three chosen rules.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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