Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions

本文介绍了一种通过空间准确性和时间密集型提取主要对象区域来进行视频对象分割的方法。该研究由UCF CRCV的张栋博士生、前UCF博士Omar Javed及UCF计算机科学信托主席教授Mubarak Shah共同完成。研究聚焦于计算机视觉领域,具体涉及图像/视频分割、场景理解、人体活动分析、运动重建及跟踪等方面。

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Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions
Dong Zhang1, Omar Javed2, Mubarak Shah1
1Center for Research in Computer Vision (CRCV)
University of Central Florida
2SRI International

1: About the author

(1): Dong Zhang

1): He is now a Ph.D student in UCF CRCV(1Center for Research in Computer Vision, University of Central Florida) under the supervision of Dr. Mubarak Shah. His research interests are Computer Vision and Artificial Intelligence:

Image/Video Segmentation

Scene Understanding

Human Activity Analysis

Structure from Motion

Tracking

2): His Educations:

Ph.D., Computer Science, University of Central Florida, 2011 -- 2015 (expected)

Ph.D., Control Science and Engineering, Zhejiang University, China, 2011

B.S., Automation, Zhejiang University, China, 2007

(2): Omar Javed

Graduated from UCF with a Ph.D. in May 2005

Worked at ObjectVideo in Reston, Virginia from Jan 2005 to Feb 2010.

Joined the SRI International-Sarnoff Corporation as a Vision Technology Leader in March 2010.

He is an associate editor for the Machine Vision & Applications Journal. (I was also an Area Chair for CVPR 2008).

(3): Mubarak Shah

Dr. Mubarak Shah, Trustee Chair Professor of Computer Science, is the founding director of the Center for Research in Computer Vision at UCF. His research interests include: video surveillance, visual tracking, human activity recognition, visual analysis of crowded scenes, video registration, UAV video analysis, etc. Dr. Shah is a fellow of IEEE, AAAS, IAPR and SPIE. In 2006, he was awarded a Pegasus Professor award, the highest award at UCF. He is ACM distinguished speaker. He was an IEEE Distinguished Visitor speaker for 1997-2000 and received IEEE Outstanding Engineering Educator Award in 1997. He received the Harris Corporation's Engineering Achievement Award in 1999, the TOKTEN awards from UNDP in 1995, 1997, and 2000; Teaching Incentive Program award in 1995 and 2003, Research Incentive Award in 2003 and 2009, Millionaires' Club awards in 2005 and 2006, University Distinguished Researcher award in 2007, honorable mention for the ICCV 2005 Where Am I? Challenge Problem, and was nominated for the best paper award in ACM Multimedia Conference in 2005. He is an editor of international book series on Video Computing; editor in chief of Machine Vision and Applications journal, and an associate editor of ACM Computing Surveys journal. He was an associate editor of the IEEE Transactions on PAMI, and a guest editor of the special issue of International Journal of Computer Vision on Video Computing.

2: 

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你好!对于 "Region-Based Convolutional Networks for Accurate Object Detection and Segmentation" 这篇论文的复现,我可以给你一些指导。该论文介绍了一种基于区域的卷积神经网络方法,用于准确的物体检测和分割。 首先,你需要了解论文中提出的方法的核心思想和技术细节。然后,你可以按照论文中描述的步骤进行复现。以下是一些可能的步骤: 1. 数据集准备:根据论文中使用的数据集,你需要获取相应的训练集和测试集数据。确保数据集包含物体检测和分割的标注信息。 2. 模型架构:根据论文中描述的模型架构,你可以使用深度学习框架(如TensorFlow、PyTorch等)来构建模型。确保按照论文中提到的网络层次结构、连接方式和参数设置来构建模型。 3. 损失函数:根据论文中提到的损失函数,你可以实现相应的损失函数来衡量检测和分割任务的性能。 4. 训练过程:使用训练集数据对模型进行训练。根据论文中提到的训练策略和超参数设置,你可以使用反向传播算法来更新模型的权重。 5. 测试过程:使用测试集数据对训练好的模型进行测试。评估模型在物体检测和分割任务上的性能,并与论文中的结果进行比较。 请注意,由于论文可能没有提供完整的代码实现,你可能需要根据论文的描述进行一定的调整和优化。 希望这些步骤能为你复现该论文提供一些帮助!如果你有任何进一步的问题,欢迎继续提问。
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