Automatic image cropping with Faster_RCNN

• المؤلف الرئيسي: Vu, Ha Son
• مؤلفون آخرون: Chia Liang Tien
• التنسيق: Final Year Project
• اللغة: English
• منشور في: 2017
• الموضوعات: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• الوصول للمادة أونلاين: http://hdl.handle.net/10356/72799

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.