Automatic image cropping with Faster_RCNN

Automatic image cropping with Faster_RCNN

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 meth...

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Bibliographic Details
Main Author: Vu, Ha Son
Other Authors: Chia Liang Tien
Format: Final Year Project
Language:English
Published: 2017
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Online Access:http://hdl.handle.net/10356/72799
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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