Content-Based Scene Change Detection of Video Sequence Using Hierarchical Hidden Markov Model

Park, Jong‐Hyun; Park, Soon-Young; Kang, Seong-Jun; Cho, Wan Hyun

doi:10.1007/978-3-540-39644-4_42

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach detects scene changes by estimating the posterior probability with the maximum value. Because this depends on the data used in training, a large amount of training data are required [ 23 ]. The final approach involves method for classifying scenes by modeling data in a graph in order to calculate, cluster, and arrange the similarity between frames and then detect scene changes using a graph segmentation algorithm [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

Kim

Park

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user’s device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user’s mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

Kim

Park

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…al. [13] proposed a video scene change detection technique using hierarchical Hidden Markov Models (HMMs). Two types of features, histogram-based and moment-based, were used to train the HMMs.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

Adaptive video segmentation

Banda¹

View full text Add to dashboard Cite

Adaptive Video Segmentation Nagamani Banda ACKNOWLEDGEMENT I would like to express my sincere thanks to Dr. Donald A. Adjeroh, for giving me the opportunity to work under him and finish my thesis under his guidance. I would also like to thank Dr. Mark A. Jerabek and Dr. Vasudevan Jagannathan for serving on my committee. Thanks to Umasankar Kandaswamy for helping me in the initial stages of the project. I would like to thank all my friends, especially my roommates for being there when I needed them. Thanks a million. v TABLE OF CONTENTS CHAPTER

show abstract

Video scene analysis in 3D wavelet transform domain

Liu

2010

Multimed Tools Appl

View full text Add to dashboard Cite

Content-Based Scene Change Detection of Video Sequence Using Hierarchical Hidden Markov Model

Cited by 4 publications

References 8 publications

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

Adaptive video segmentation

Video scene analysis in 3D wavelet transform domain

Contact Info

Product

Resources

About