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...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
2017
|
| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/72799 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | 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. |
|---|