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Journal articles on the topic 'Object Based Video Inpainting'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Chincholkar, Abhijeet A., and Salim A. Chavan. "Intelligent Object-Based Video Inpainting Approach For Fast Video Repairing." International Journal of Scientific Research 3, no. 4 (2012): 1–3. http://dx.doi.org/10.15373/22778179/apr2014/175.

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2

Vijay Venkatesh, M., Sen-ching Samson Cheung, and Jian Zhao. "Efficient object-based video inpainting." Pattern Recognition Letters 30, no. 2 (2009): 168–79. http://dx.doi.org/10.1016/j.patrec.2008.03.011.

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3

Siddique, Ashraf, and Seungkyu Lee. "Object-Wise Video Editing." Applied Sciences 11, no. 2 (2021): 671. http://dx.doi.org/10.3390/app11020671.

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Beyond time frame editing in video data, object level video editing is a challenging task; such as object removal in a video or viewpoint changes. These tasks involve dynamic object segmentation, novel view video synthesis and background inpainting. Background inpainting is a task of the reconstruction of unseen regions presented by object removal or viewpoint change. In this paper, we propose a video editing method including foreground object removal background inpainting and novel view video synthesis under challenging conditions such as complex visual pattern, occlusion, overlaid clutter an

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4

U.Gaikwad, Vaishali, and P. V. kulkarni. "Exemplar-based Video Inpainting for Occluded Objects." International Journal of Computer Applications 81, no. 13 (2013): 28–30. http://dx.doi.org/10.5120/14075-2419.

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5

Voronin, Viacheslav, Vladimir Marchuk, Sergey Makov, Vladimir Mladenovic, and Yigang Cen. "Spatio-temporal image inpainting for video applications." Serbian Journal of Electrical Engineering 14, no. 2 (2017): 229–44. http://dx.doi.org/10.2298/sjee170116004v.

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Video inpainting or completion is a vital video improvement technique used to repair or edit digital videos. This paper describes a framework for temporally consistent video completion. The proposed method allows to remove dynamic objects or restore missing or tainted regions presented in a video sequence by utilizing spatial and temporal information from neighboring scenes. Masking algorithm is used for detection of scratches or damaged portions in video frames. The algorithm iteratively performs the following operations: achieve frame; update the scene model; update positions of moving objec

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6

LU, SIJUN, JIAN ZHANG, and DAVID DAGAN FENG. "DETECTING GHOST AND LEFT OBJECTS IN SURVEILLANCE VIDEO." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 07 (2009): 1503–25. http://dx.doi.org/10.1142/s021800140900765x.

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This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computational power in background modeling and object tracking in video surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of regions in the input image; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on cont

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7

Favorskaya, M. N., V. V. Buryachenko, A. G. Zotin, and A. I. Pakhirka. "VIDEO COMPLETION IN DIGITAL STABILIZATION TASK USING PSEUDO-PANORAMIC TECHNIQUE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 83–90. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-83-2017.

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Video completion is a necessary stage after stabilization of a non-stationary video sequence, if it is desirable to make the resolution of the stabilized frames equalled the resolution of the original frames. Usually the cropped stabilized frames lose 10-20% of area that means the worse visibility of the reconstructed scenes. The extension of a view of field may appear due to the pan-tilt-zoom unwanted camera movement. Our approach deals with a preparing of pseudo-panoramic key frame during a stabilization stage as a pre-processing step for the following inpainting. It is based on a multi-laye

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8

Tudavekar, Gajanan, Sanjay R. Patil, and Santosh S. Saraf. "Dual-tree complex wavelet transform and super-resolution based video inpainting application to object removal and error concealment." CAAI Transactions on Intelligence Technology 5, no. 4 (2020): 314–19. http://dx.doi.org/10.1049/trit.2019.0045.

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9

Ghanbari Talouki, A., M. Majdi, and S. A. Edalatpanah. "Video Inpainting Using a Contour-Based Method in Presence of More than One Moving Objects." INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING AND MANAGEMENT 2, no. 2 (2017): 37. http://dx.doi.org/10.24999/ijoaem/02020013.

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10

Yin, Jianxia, Shimeng Huang, Lei Lei, and Jing Yao. "Intelligent Monitoring Method of Short-Distance Swimming Physical Function Fatigue Limit Mobile Calculation." Wireless Communications and Mobile Computing 2021 (May 26, 2021): 1–6. http://dx.doi.org/10.1155/2021/9919231.

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The detection and classification of moving targets have always been a key technology in intelligent video surveillance. Current detection and classification algorithms for moving targets still face many difficulties, mainly because of the complexity of the monitoring environment and the limitations of target characteristics. Therefore, this article conducts corresponding research on moving target detection and classification in intelligent video surveillance. According to the Gaussian Mixture Background Model and Frame Difference Method, this paper proposes a moving target detection method bas

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11

Horng, Mong-Fong, Hsu-Yang Kung, Chi-Hua Chen, and Feng-Jang Hwang. "Deep Learning Applications with Practical Measured Results in Electronics Industries." Electronics 9, no. 3 (2020): 501. http://dx.doi.org/10.3390/electronics9030501.

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This editorial introduces the Special Issue, entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries”, of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) “A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction

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12

Koochari, A., and M. Soryani. "Video object inpainting: a scale-robust method." Imaging Science Journal 60, no. 5 (2012): 272–84. http://dx.doi.org/10.1179/1743131x11y.0000000047.

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13

Shimamura, Ryo, Qi Feng, Yuki Koyama, et al. "Audio–visual object removal in 360-degree videos." Visual Computer 36, no. 10-12 (2020): 2117–28. http://dx.doi.org/10.1007/s00371-020-01918-1.

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Abstract We present a novel concept audio–visual object removal in 360-degree videos, in which a target object in a 360-degree video is removed in both the visual and auditory domains synchronously. Previous methods have solely focused on the visual aspect of object removal using video inpainting techniques, resulting in videos with unreasonable remaining sounds corresponding to the removed objects. We propose a solution which incorporates direction acquired during the video inpainting process into the audio removal process. More specifically, our method identifies the sound corresponding to t

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14

Koochari, Abbas, and Mohsen Soryani. "Exemplar-based video inpainting with large patches." Journal of Zhejiang University SCIENCE C 11, no. 4 (2010): 270–77. http://dx.doi.org/10.1631/jzus.c0910308.

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15

Mosleh, Ali, Nizar Bouguila, and A. Ben Hamza. "Bandlet-based sparsity regularization in video inpainting." Journal of Visual Communication and Image Representation 25, no. 5 (2014): 855–63. http://dx.doi.org/10.1016/j.jvcir.2014.01.007.

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16

Zhang, Rong, Wei Li, Peng Wang, et al. "AutoRemover: Automatic Object Removal for Autonomous Driving Videos." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12853–61. http://dx.doi.org/10.1609/aaai.v34i07.6982.

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Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm AutoRemover, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage

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17

Ling, Chih-Hung, Chia-Wen Lin, Chih-Wen Su, Yong-Sheng Chen, and Hong-Yuan Mark Liao. "Virtual Contour Guided Video Object Inpainting Using Posture Mapping and Retrieval." IEEE Transactions on Multimedia 13, no. 2 (2011): 292–302. http://dx.doi.org/10.1109/tmm.2010.2095000.

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18

Ramya, C., S. Subha Rani, and G. Kayalvizhi. "Image Inpainting Based on Fast Inpainting and Sparse Representation Method." Advanced Materials Research 984-985 (July 2014): 1350–56. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1350.

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Especially there is a wide interest in handling the outdoor surveillance images in autonomous navigation, remote surveillance, automatic incident detection, vision based driver assistant system and law enforcement services. In each case, there is an underlying object or scene which is wished to be captured, processed, analyzed and interpreted. Live recording and transmission of outdoor surveillance images are often one of a forensic tool but while transmission it may introduce some variations in the pixels and it may visible as missing blocks. Thus it is significant to improve the visual quali

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19

Daribo, I., and H. Saito. "A Novel Inpainting-Based Layered Depth Video for 3DTV." IEEE Transactions on Broadcasting 57, no. 2 (2011): 533–41. http://dx.doi.org/10.1109/tbc.2011.2125110.

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20

Ebdelli, Mounira, Olivier Le Meur, and Christine Guillemot. "Video Inpainting With Short-Term Windows: Application to Object Removal and Error Concealment." IEEE Transactions on Image Processing 24, no. 10 (2015): 3034–47. http://dx.doi.org/10.1109/tip.2015.2437193.

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21

Kim, Joohyung, Janghun Hyeon, and Nakju Doh. "Generative multiview inpainting for object removal in large indoor spaces." International Journal of Advanced Robotic Systems 18, no. 2 (2021): 172988142199654. http://dx.doi.org/10.1177/1729881421996544.

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As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the color

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22

Borole, Rajesh Pandurang, and Sanjiv Vedu Bonde. "Patch-Based Inpainting for Object Removal and Region Filling in Images." Journal of Intelligent Systems 22, no. 3 (2013): 335–50. http://dx.doi.org/10.1515/jisys-2013-0031.

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AbstractA large number of articles have been devoted to the application of “texture synthesis” for large regions and “inpainting” algorithms for small cracks in an image. A new approach that allows the simultaneous filling in of different structures and textures is discussed in this present study. The combination of structure inpainting and patch-based texture synthesis carried out (termed as “patch-based inpainting”) for filling and updating the target region shows additional advantages over earlier approaches. The algorithm discussed here uses the patch-based inpainting with isophote-driven

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23

V.V, Voronin, Sizyakin R.A, Marchuk V.I, Yigang Cen, Galustov G.G, and Egiazarian K.O. "Video inpainting of complex scenes based on local statistical model." Electronic Imaging 2016, no. 15 (2016): 1–6. http://dx.doi.org/10.2352/issn.2470-1173.2016.15.ipas-193.

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24

M, Baburaj, and Sudhish N. George. "Tensor based approach for inpainting of video containing sparse text." Multimedia Tools and Applications 78, no. 2 (2018): 1805–29. http://dx.doi.org/10.1007/s11042-018-6251-7.

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25

Wang, Xinyi, He Wang, and Shaozhang Niu. "An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting." Mathematical Problems in Engineering 2020 (October 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/8892989.

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Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning method

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26

Wang, Jing, Ke Lu, Daru Pan, Ning He, and Bing-kun Bao. "Robust object removal with an exemplar-based image inpainting approach." Neurocomputing 123 (January 2014): 150–55. http://dx.doi.org/10.1016/j.neucom.2013.06.022.

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27

Criminisi, A., P. Perez, and K. Toyama. "Region Filling and Object Removal by Exemplar-Based Image Inpainting." IEEE Transactions on Image Processing 13, no. 9 (2004): 1200–1212. http://dx.doi.org/10.1109/tip.2004.833105.

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28

Chih-Hung Ling, Yu-Ming Liang, Chia-Wen Lin, Yong-Sheng Chen, and Hong-Yuan Mark Liao. "Human Object Inpainting Using Manifold Learning-Based Posture Sequence Estimation." IEEE Transactions on Image Processing 20, no. 11 (2011): 3124–35. http://dx.doi.org/10.1109/tip.2011.2158228.

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29

Wang, Meiqing, Chensi Huang, Chao Zeng, and Choi-Hong Lai. "Two-Phase Image Inpainting: Combine Edge-Fitting with PDE Inpainting." Advances in Applied Mathematics and Mechanics 4, no. 06 (2012): 769–79. http://dx.doi.org/10.4208/aamm.12-12s08.

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AbstractThe digital image inpainting technology based on partial differential equations (PDEs) has become an intensive research topic over the last few years due to the mature theory and prolific numerical algorithms of PDEs. However, PDE based models are not effective when used to inpaint large missing areas of images, such as that produced by object removal. To overcome this problem, in this paper, a two-phase image inpainting method is proposed. First, some edges which cross the damaged regions are located and the missing parts of these edges are fitted by using the cubic spline interpolati

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30

Sallam, A., M. Shaarawy, O. Elmowafy, R. Elbordany, and A. Fahmy. "Object based video coding algorithm." International Conference on Electrical Engineering 7, no. 7 (2010): 1–13. http://dx.doi.org/10.21608/iceeng.2010.33056.

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31

Wang, Chuan, Haibin Huang, Xiaoguang Han, and Jue Wang. "Video Inpainting by Jointly Learning Temporal Structure and Spatial Details." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5232–39. http://dx.doi.org/10.1609/aaai.v33i01.33015232.

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We present a new data-driven video inpainting method for recovering missing regions of video frames. A novel deep learning architecture is proposed which contains two subnetworks: a temporal structure inference network and a spatial detail recovering network. The temporal structure inference network is built upon a 3D fully convolutional architecture: it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution. The low resolution result provides temporal guidance to the spatial detail recovering network, which performs imagebased inpainting

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32

Changick Kim and Jenq-Neng Hwang. "Object-based video abstraction for video surveillance systems." IEEE Transactions on Circuits and Systems for Video Technology 12, no. 12 (2002): 1128–38. http://dx.doi.org/10.1109/tcsvt.2002.806813.

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33

Vadhana, R. Vedha Priya, and K. Ruba Soundar. "A Novel Video Inpainting Technique Based on Digital Notch Filtering Method." Journal of Computational and Theoretical Nanoscience 14, no. 2 (2017): 1239–44. http://dx.doi.org/10.1166/jctn.2017.6437.

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34

D., Ankit, Hetal M., and Ankurkumar G. "Modified Region Filling and Object Removal by Exemplar - based Image Inpainting." International Journal of Computer Applications 182, no. 24 (2018): 27–31. http://dx.doi.org/10.5120/ijca2018918042.

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35

Cai, Xiuxia, and Bin Song. "Semantic object removal with convolutional neural network feature-based inpainting approach." Multimedia Systems 24, no. 5 (2018): 597–609. http://dx.doi.org/10.1007/s00530-018-0585-x.

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36

Lu, Ming, and Shaozhang Niu. "A Detection Approach Using LSTM-CNN for Object Removal Caused by Exemplar-Based Image Inpainting." Electronics 9, no. 5 (2020): 858. http://dx.doi.org/10.3390/electronics9050858.

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Exemplar-based image inpainting technology is a “double-edged sword”. It can not only restore the integrity of image by inpainting damaged or removed regions, but can also tamper with the image by using the pixels around the object region to fill in the gaps left by object removal. Through the research and analysis, it is found that the existing exemplar-based image inpainting forensics methods generally have the following disadvantages: the abnormal similar patches are time-consuming and inaccurate to search, have a high false alarm rate and a lack of robustness to multiple post-processing co

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37

P, Jini. "Image Inpainting Using Image Interpolation - An Analysis." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1906–20. http://dx.doi.org/10.47059/revistageintec.v11i2.1807.

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Image inpainting is a computer technique of filling lost regions of an image by using available information from the surrounding area. This digital image inpainting technique has wider applications like image restoration, dis-occlusion and image/video compression. The traditional image inpainting approaches Partial Differential Equation (PDE) based method and Exemplar based method mainly focus on the size of the target region to be filled. PDE is a pixel oriented method and works well if the region to be filled is small. On the other hand exemplar based method is a patch based method and works

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38

A.S, Raihanath, and Chithra Rani P R. "Saliency Based Video Object Recognition in Single Concept Video." International Journal of Innovative Research in Science, Engineering and Technology 03, no. 09 (2014): 16250–56. http://dx.doi.org/10.15680/ijirset.2014.0309063.

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39

Guo, Yi, Su-Rong Qi-Mu, Wei Jin, and Xing Wei. "Object-based video synopsis and multi-video body retrieval." Signal Processing Research 5 (2016): 7. http://dx.doi.org/10.14355/spr.2016.05.002.

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40

Morand, Cl, J. Benois-Pineau, J. Ph Domenger, J. Zepeda, E. Kijak, and Ch Guillemot. "Scalable object-based video retrieval in HD video databases." Signal Processing: Image Communication 25, no. 6 (2010): 450–65. http://dx.doi.org/10.1016/j.image.2010.04.004.

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41

Cavallaro, A., E. Salvador, and T. Ebrahimi. "Shadow-aware object-based video processing." IEE Proceedings - Vision, Image, and Signal Processing 152, no. 4 (2005): 398. http://dx.doi.org/10.1049/ip-vis:20045108.

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42

Liu, David, and Tsuhan Chen. "Video retrieval based on object discovery." Computer Vision and Image Understanding 113, no. 3 (2009): 397–404. http://dx.doi.org/10.1016/j.cviu.2008.08.008.

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43

Lee, Taehee, and Stefano Soatto. "Video-based descriptors for object recognition." Image and Vision Computing 29, no. 10 (2011): 639–52. http://dx.doi.org/10.1016/j.imavis.2011.08.003.

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44

Salahieh, Basel, Wayne Cochran, and Jill Boyce. "Delivering Object-Based Immersive Video Experiences." Electronic Imaging 2021, no. 18 (2021): 103–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.18.3dia-103.

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Immersive video enables interactive natural consumption of visual content by empowering a user to navigate through six degrees of freedom, with motion parallax and wide-angle rotation. Supporting immersive experiences requires content captured by multiple cameras and efficient video coding to meet bandwidth and decoder complexity constraints, while delivering high quality video to end users. The Moving Picture Experts Group (MPEG) is developing an immersive video (MIV) standard to data access and delivery of such content. One of MIV operating modes is an objectbased immersive video coding whic

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45

Cao, Ying, Lijuan Sun, Chong Han, and Jian Guo. "Attention‐based video object segmentation algorithm." IET Image Processing 15, no. 8 (2021): 1668–78. http://dx.doi.org/10.1049/ipr2.12135.

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46

Shih, T. K., N. C. Tang, and Jenq-Neng Hwang. "Exemplar-Based Video Inpainting Without Ghost Shadow Artifacts by Maintaining Temporal Continuity." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 3 (2009): 347–60. http://dx.doi.org/10.1109/tcsvt.2009.2013519.

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47

Zhang, Lei, and Minhui Chang. "Image Inpainting for Object Removal Based on Adaptive Two-Round Search Strategy." IEEE Access 8 (2020): 94357–72. http://dx.doi.org/10.1109/access.2020.2995700.

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48

JEMI FLORINABEL, D., S. EBENEZER JULIET, and V. SADASIVAM. "REGION BASED PATCH PROPAGATION AND PATCH INPAINTING FOR IMAGE COMPLETION." International Journal of Information Acquisition 08, no. 01 (2011): 39–52. http://dx.doi.org/10.1142/s0219878911002355.

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Film and photography archives nowadays go through an accelerated process of degradation. Since the preservation of cultural heritage plays an important role in our society, photograph/film restoration has drawn a lot of attention recently. In this paper, an extent of exemplar based inpainting at determining patch priority and patch matching is proposed. Patch priority is defined by two terms: the number of homogeneous regions within the patch (heterogeneous term) obtained by image segmentation and the variance of the pixels within each homogeneous region. The first term prioritizes the heterog

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49

LEE, P. J. "Feature-Based Error Concealment for Object-Based Video." IEICE Transactions on Communications E88-B, no. 6 (2005): 2616–26. http://dx.doi.org/10.1093/ietcom/e88-b.6.2616.

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50

Xu, Haoran, Yanbai He, Xinya Li, Xiaoying Hu, Chuanyan Hao, and Bo Jiang. "Joint Subtitle Extraction and Frame Inpainting for Videos with Burned-In Subtitles." Information 12, no. 6 (2021): 233. http://dx.doi.org/10.3390/info12060233.

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Subtitles are crucial for video content understanding. However, a large amount of videos have only burned-in, hardcoded subtitles that prevent video re-editing, translation, etc. In this paper, we construct a deep-learning-based system for the inverse conversion of a burned-in subtitle video to a subtitle file and an inpainted video, by coupling three deep neural networks (CTPN, CRNN, and EdgeConnect). We evaluated the performance of the proposed method and found that the deep learning method achieved high-precision separation of the subtitles and video frames and significantly improved the vi

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