ViMediaNet: an emulation system for interactive multimedia based telepresence services

Lü, Xiang; Tu, Linlin; Zhou, Xiaofei; Xiong, Naixue; Sun, Limin

doi:10.1007/s11227-016-1821-9

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Lee et al 5 mentioned uncertain data based on target registration error. Some papers 3,6,18,68 mentioned visual illustration of derived data on the basis of errors and uncertain data in visualization processing.…”

Section: Discussionmentioning

confidence: 99%

“…Although, it limits the frame rate and the size of the input file, some gestures are manipulated in a wrong way. The researchers in References 58 and 67‐6970 used robot instead of human which requires multiple humans to operate multiple robots. Martinez‐Hernandez et al 16 used humanoid robot of iCub consisting of sensors for audio, video, and touch, which are controlled by the remote location.…”

Section: System Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A recent review and a taxonomy for hard and soft tissue visualization‐based mixed reality

Tuladhar

AlSallami

Alsadoon

et al. 2020

Robotics Computer Surgery

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Background: Mixed reality (MR) visualization is gaining popularity in image-guided surgery (IGS) systems, especially for hard and soft tissue surgeries. However, a few MR systems are implemented in real time. Some factors are limiting MR technology and creating a difficulty in setting up and evaluating the MR system in real environments. Some of these factors include: the end users are not considered, the limitations in the operating room, and the medical images are not fully unified into the operating interventions. Methodology: The purpose of this article is to use Data, Visualization processing, and View (DVV) taxonomy to evaluate the current MR systems. DVV includes all the components required to be considered and validated for the MR used in hard and soft tissue surgeries. This taxonomy helps the developers and end users like researchers and surgeons to enhance MR system for the surgical field. Results: We evaluated, validated, and verified the taxonomy based on system comparison, completeness, and acceptance criteria. Around 24 state-of-the-art solutions that are picked relate to MR visualization, which is then used to demonstrate and validate this taxonomy. The results showed that most of the findings are evaluated and others are validated. Conclusion: The DVV taxonomy acts as a great resource for MR visualization in IGS. State-of-the-art solutions are classified, evaluated, validated, and verified to elaborate the process of MR visualization during surgery. The DVV taxonomy provides the benefits to the end users and future improvements in MR.

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Section: Discussionmentioning

confidence: 99%

Section: System Classificationmentioning

confidence: 99%

A recent review and a taxonomy for hard and soft tissue visualization‐based mixed reality

Tuladhar

AlSallami

Alsadoon

et al. 2020

Robotics Computer Surgery

View full text Add to dashboard Cite

show abstract

“…With the wireless sensor networks booming, various researches based on wireless sensor networks have made great progress [1][2][3][4][5][6][7][8][9][10], such as the physical sensors [11], architecture for the service computing [12], faulttolerant optimization [13], data gathering and compression [14], and smart data analysis [15]. Although the wireless sensor networks have been proposed, studied, and developed for more than a decade of years, there are still a lot of challenging issues especially in various industrial scenarios [16,17], including the object detection for the remote sensing images that are gathered by the industrial wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Aircraft detection in remote sensing images based on saliency and convolution neural network

Yang

Han

et al. 2018

J Wireless Com Network

Self Cite

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New algorithms and architectures for the current industrial wireless sensor networks shall be explored to ensure the efficiency, robustness, and consistence in variable application environments which concern different issues, such as the smart grid, water supply, and gas monitoring. Object detection automatic in remote sensing images has always been a hot topic. Using the conventional deep convolution network based on region proposal for detection, there are many negative samples in the generated region proposal, which will affect the model detection precision and efficiency. Saliency uses the human visual attention mechanism to achieve the bottom-up object detection. Since replacing the selective search with saliency can greatly reduce the number of proposal areas, we will get some region of interests (RoIs) and their position information by using the saliency algorithm based on the background priori for the remote sensing image. And then, the position information is mapped to the feature vector of the whole image obtained by deep convolution neural network. Finally, the each RoI will be classified and fine-tuned bounding box. In this paper, our model is compared with Fast-RCNN that is the current state-of-the-art detection model. The mAP of our model reaches 99%, which is 12.4% higher than that of Fast-RCNN. In addition, we also study the effect of different iterations on model and find the model of 10,000 iterations already has a higher accuracy. Finally, we compare the results of different number of negative samples and find the detection accuracy is highest when the number of negative samples reaches 400.

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“…Multimedia forensics [38,61,78,80,81] is an important domain of information security [9,10,12,13,[19][20][21][22][23][24]39]. Both IoT and MBD [17,18,28,30,42,46,47,58,62,[65][66][67][68][69][70][71]75,77,79,83,85] have a lot of multimedia data. Therefore, the research of the multimedia forensics is very meaningful to IoT and MBD.…”

Section: Introductionmentioning

confidence: 99%

Copy-move forgery detection using combined features and transitive matching

Lin

Huang

et al. 2018

Multimed Tools Appl

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Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks.

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ViMediaNet: an emulation system for interactive multimedia based telepresence services

Cited by 7 publications

References 13 publications

A recent review and a taxonomy for hard and soft tissue visualization‐based mixed reality

A recent review and a taxonomy for hard and soft tissue visualization‐based mixed reality

Aircraft detection in remote sensing images based on saliency and convolution neural network

Copy-move forgery detection using combined features and transitive matching

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