Automatic content generation for video self modeling

Shen, Ju; Raghunathan, Anusha; Cheung, Susan; Patel, Rita R.

doi:10.1109/icme.2011.6011997

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Various approaches regarding video processing [12,13,14,15] and transmission [9,10,11,18,19] over WMSNs were proposed. Q. Cai et al [23,24] proposed a dynamic action recognition, which is important to capture videos in WM-SNs.…”

Section: Related Workmentioning

confidence: 99%

Compression of Video Tracking and Bandwidth Balancing Routing in Wireless Multimedia Sensor Networks

Wang¹,

Yang²,

Shen³

et al. 2015

Proceedings of the 8th International Conference on Mobile Multimedia Communications

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There has been a tremendous growth in multimedia applications over wireless networks. Wireless Multimedia Sensor Networks(WMSNs) have become the premier choice in many research communities and industry. Many state-of-art applications, such as surveillance, traffic monitoring, and remote heath care are essentially video tracking and transmission in WMSNs. The transmission speed is constrained by big size of video data and fixed bandwidth allocation in constant routing path. In this paper, we present a CamShift based algorithm to compress the tracking of videos. Then we propose a bandwidth balancing strategy in which each sensor node is able to dynamically select the node for next hop with the highest potential bandwidth capacity to resume communication. Key to the strategy is that each node merely maintains two parameters that contains its historical bandwidth varying trend and then predicts its near future bandwidth capacity. Then forwarding node selects the next hop with the highest potential bandwidth capacity. Simulations demonstrate that our approach significantly increases the data received by sink node and decreases the delay on video transmission in Wireless Multimedia Sensor Network environment. ABSTRACTThere has been a tremendous growth in multimedia applications over wireless networks. Wireless Multimedia Sensor Networks(WMSN) have become the premier choice in many research communities and industry. Many state-to-art applications, such as surveillance, traffic monitoring, and remote heath care are essentially video tracking and transmission in WMSN. The transmission speed is constrained by big size of video data and fixed bandwidth allocation in fixed routing path. In this paper, we present a CamShift algorithm to compress the tracking of videos. Then we propose a bandwidth balancing strategy in which the sensor nodes are able to dynamically select the node for next hop with the highest potential bandwidth capacity to resume communication. Key to the strategy is that each node merely maintains two parameters that contains the historical network traffic and then calculates its bandwidth capacity. Then each node selects the next hop with the highest potential bandwidth capacity. Simulations demonstrate that our approach significantly increase the aggregate throughput and decrease the latency on video transmission in Wireless Multimedia Sensor Network environment. ABSTRACTThere has been a tremendous growth in multimedia applications over wireless networks. Wireless Multimedia Sensor Networks(WMSN) have become the premier choice in many research communities and industry. Many state-to-art applications, such as surveillance, traffic monitoring, and remote heath care are essentially video tracking and transmission in WMSN. The transmission speed is constrained by big size of video data and fixed bandwidth allocation in fixed routing path. In this paper, we present a CamShift algorithm to compress the tracking of videos. Then we propose a bandwidth balancing strategy in which the sensor nodes are able to dynam...

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Section: Related Workmentioning

confidence: 99%

Compression of Video Tracking and Bandwidth Balancing Routing in Wireless Multimedia Sensor Networks

Wang¹,

Yang²,

Shen³

et al. 2015

Proceedings of the 8th International Conference on Mobile Multimedia Communications

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“…Second, an orientated patch matching algorithm is designed for texture propagation. Our work has a broad range of applications including image localization [7] [8], privacy protection [9] [10] [11], and other network based applications [12] [13] [14] [15] [16].…”

Section: Introductionmentioning

confidence: 99%

Automatic objects removal for scene completion

Yang¹,

Wang²,

Wang³

et al. 2014

2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as 3D scene reconstruction and other big data applications. However, this is not an easy task due to the fact the retrieved photos are neither aligned nor calibrated. Furthermore, with the occlusion of unexpected foreground objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structurebased image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to the natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing: 3D scene reconstruction and location recognition.

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“…Moreover, with the recent advance in image processing techniques, such as feature descriptors [1], pixel-domain matrix factorization approaches [2][3][4] or probabilistic optimization [5], images can be read in an automatic manner rather than relying on the associated text. This leads to a revolutionary impact to a broad range of applications, from image clustering or recognition [6][7][8][9][10][11][12] to video synthesis or reconstruction [13][14][15] to cybersecurity via online images analysis [16][17][18][19] to other scientific applications [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Structure Preserving Large Imagery Reconstruction

Shen

Yang

Taha-abusneineh

et al. 2014

JCSM

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With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Furthermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure-based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing, such as image localization, object retrieval, and scene reconstruction. Our experiments show that this approach achieves favorable results that outperform existing state-of-the-art techniques.

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Automatic content generation for video self modeling

Cited by 9 publications

References 13 publications

Compression of Video Tracking and Bandwidth Balancing Routing in Wireless Multimedia Sensor Networks

Compression of Video Tracking and Bandwidth Balancing Routing in Wireless Multimedia Sensor Networks

Automatic objects removal for scene completion

Structure Preserving Large Imagery Reconstruction

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