A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Real-Time Estimation of Human Body Posture from Monocular Thermal Images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The social camera: a case-study in contextual image recommendation
Proceedings of the 16th international conference on Intelligent user interfaces
Automatically detecting protruding objects when shooting environmental portraits
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Hi-index | 12.05 |
Good photos result from careful attention to various elements such as shutter speed, aperture, light exposure and composition. Over the years, there have been interests to develop digital still cameras with ''composition advising'' functions. However, not many composition-related functions have been realized in current digital cameras. This study proposed an approach for automatic detection of unintentional dissection lines which often degrade the aesthetics of an environmental portrait photo. The algorithm includes modules for face detection, ROI estimation, morphology filtering, edge detection and straight line detection. Experimental evaluations conducted to verify the performance of the algorithm show that the detection rate and false detection rate of general dissection lines are 80.87% and 33.61%, respectively.