1.福州大学 先进制造学院, 福建 泉州 362200
2.福州大学 物理与信息工程学院, 福建 福州 350116
3.中国福建光电信息科学与技术实验室, 福建 福州 350116
[ "吴宇航(1998—),女,陕西西安人,硕士研究生,2020年于西安邮电大学获得学士学位,主要从事图像处理、计算机视觉的研究。E-mail:2584402534@ qq.com" ]
[ "林珊玲(1991—),女,福建泉州人,博士,讲师,2020年于福州大学获得博士学位,主要从事显示驱动、图像处理等方面的研究。E-mail:sllin@fzu.edu.cn" ]
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吴宇航, 林珊玲, 林志贤, 等. 基于人像检测的实时图像智能裁剪[J]. 液晶与显示, 2023,38(5):617-624.
WU Yu-hang, LIN Shan-ling, LIN Zhi-xian, et al. Real-time automatic image cropping based on portrait detection[J]. Chinese Journal of Liquid Crystals and Displays, 2023,38(5):617-624.
吴宇航, 林珊玲, 林志贤, 等. 基于人像检测的实时图像智能裁剪[J]. 液晶与显示, 2023,38(5):617-624. DOI: 10.37188/CJLCD.2022-0258.
WU Yu-hang, LIN Shan-ling, LIN Zhi-xian, et al. Real-time automatic image cropping based on portrait detection[J]. Chinese Journal of Liquid Crystals and Displays, 2023,38(5):617-624. DOI: 10.37188/CJLCD.2022-0258.
针对现有图像智能裁剪算法在处理人像图片时,存在误将主要人物整个人或部分身体部位裁减掉等致使图像关键信息缺失和构图不佳的问题,本文提出一种基于人像检测的实时图像智能裁剪方法。该方法将图像裁剪分为人像检测和智能构图两个阶段,旨在将提取的人像坐标信息作为智能裁剪算法的输入,然后结合基于美学的构图法则对图片进行自动构图,确保裁剪结果中人像信息的完整性并提升图片的构图美感。实验结果表明,本文在Center-Net基础上改造的轻量级检测网络,运算量减少了86%,精度提升了3.34%,便于将该裁剪算法应用于移动端设备。整个裁剪算法的FPS达到了77,并且裁剪后的人像信息完整,图片整体构图得到改善。
In order to solve the problem that the existing automatic image cropping algorithm mistakenly cuts off the whole person or part of the body parts of the main figure, which leads to the missing of the key information of the image and the poor composition of the image, this paper proposes a real-time automatic image cropping method based on portrait detection. This method divides image cropping into two stages: portrait detection and intelligent composition. It aims to take the extracted portrait coordinate information as the input of the intelligent cropping algorithm, and then combines the composition rule based on aesthetics to automatically compose the picture, so as to ensure the integrity of the portrait information in the cropping result and improve the composition beauty of the picture. The experimental results show that the lightweight detection network modified on the basis of Center-Net in this paper reduces the computation amount by 86% and improves the accuracy by 3.34%, which is convenient to apply the cropping algorithm to mobile devices. The FPS of the whole cropping algorithm reaches 77, and the cropped portrait information is complete, and the overall composition of the picture is improved.
图像裁剪人像检测智能构图计算机视觉
image croppinghuman detectionimage compositioncomputer vision
SANTELLA A, AGRAWALA M, DECARLO D, et al. Gaze-based interaction for semi-automatic photo cropping [C]//Proceedings of SIGCHI Conference on Human Factors in Computing Systems. Montréal: ACM, 2006: 771-780. doi: 10.1145/1124772.1124886http://dx.doi.org/10.1145/1124772.1124886
STENTIFORD F. Attention based auto image cropping [C]//Proceedings of the 5th International Conference on Computer Vision Systems. Germany: Applied Computer Science Group, Bielefeld University, 2007: 140-148. doi: 10.1109/icip.2006.312482http://dx.doi.org/10.1109/icip.2006.312482
KAO Y Y, WANG C, HUANG K Q. Visual aesthetic quality assessment with a regression model [C]//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City: IEEE, 2015: 1583-1587. doi: 10.1109/icip.2015.7351067http://dx.doi.org/10.1109/icip.2015.7351067
LU X, LIN Z, JIN H L, et al. RAPID: rating pictorial aesthetics using deep learning [C]//Proceedings of the 22nd ACM International Conference on Multimedia. Orlando: ACM, 2014: 457-466. doi: 10.1145/2647868.2654927http://dx.doi.org/10.1145/2647868.2654927
LI D B, WU H K, ZHANG J G, et al. A2-RL: aesthetics aware reinforcement learning for image cropping [C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8193-8201. doi: 10.1109/cvpr.2018.00855http://dx.doi.org/10.1109/cvpr.2018.00855
CHEN Y L, KLOPP J, SUN M, et al. Learning to compose with professional photographs on the web [C]//Proceedings of the 25th ACM International Conference on Multimedia. Mountain: ACM, 2017: 37-45. doi: 10.1145/3123266.3123274http://dx.doi.org/10.1145/3123266.3123274
WEI Z J, ZHANG J M, SHEN X H, et al. Good view hunting: learning photo composition from dense view pairs [C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 5437-5446. doi: 10.1109/cvpr.2018.00570http://dx.doi.org/10.1109/cvpr.2018.00570
YAN B, FAN P, LEI X Y, et al. A real-time apple targets detection method for picking robot based on improved YOLOv5 [J]. Remote Sensing, 2021, 13(9): 1619. doi: 10.3390/rs13091619http://dx.doi.org/10.3390/rs13091619
DUAN K W, BAI S, XIE L X, et al. CenterNet: keypoint triplets for object detection [C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 6568-6577. doi: 10.1109/iccv.2019.00667http://dx.doi.org/10.1109/iccv.2019.00667
XU Z H, YUAN X Y, ZHOU T K, et al. A multichannel optical computing architecture for advanced machine vision [J]. Light: Science & Applications, 2022, 11(1): 255. doi: 10.1038/s41377-022-00945-yhttp://dx.doi.org/10.1038/s41377-022-00945-y
王建林,付雪松,黄展超,等.改进YOLOv2卷积神经网络的多类型合作目标检测[J].光学 精密工程,2020,28(1):251-260. doi: 10.3788/ope.20202801.0251http://dx.doi.org/10.3788/ope.20202801.0251
WANG J L, FU X S, HUANG Z C, et al. Multi-type cooperative targets detection using improved YOLOv2 convolutional neural network [J]. Optics and Precision Engineering, 2020, 28(1): 251-260. (in Chinese). doi: 10.3788/ope.20202801.0251http://dx.doi.org/10.3788/ope.20202801.0251
SANDLER M, HOWARD A, ZHU M L, et al. MobilenetV2: inverted residuals and linear bottlenecks [C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 4510-4520. doi: 10.1109/cvpr.2018.00474http://dx.doi.org/10.1109/cvpr.2018.00474
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