Moving Object Completion on the Compressed Domain
スポンサーリンク
概要
- 論文の詳細を見る
Moving object completion is a process of completing moving objects missing information based on local structures. Over the past few years, a number of computable algorithms of video completion have been developed, however most of these algorithms are based on the pixel domain. Little theoretical and computational work in video completion is based on the compressed domain. In this paper, a moving object completion method on the compressed domain is proposed. It is composed of three steps: motion field transferring, thin plate spline interpolation and combination. Missing space-time blocks will be completed by placing new motion vectors on them so that the resulting video sequence will have as much global visual coherence with the video portions outside the hole. The experimental results are presented to demonstrate the efficiency and accuracy of the proposed algorithm.
- （社）電子情報通信学会の論文
- 2009-07-01
著者
-
XU De
Institute of Computer Science and Engineering, Beijing Jiaotong University
-
Xu De
Institute Of Computer & Engineering Beijing Jiaotong University
-
LANG Congyan
Institute of Computer Science and Engineering, Beijing Jiaotong University
-
Liu Na
Lenovo Corporate R & D
-
JIANG YIWEI
Institute of Computer Science and Engineering, Beijing Jiaotong University
-
Jiang Yiwei
Institute Of Computer Science And Engineering Beijing Jiaotong University
-
Lang Congyan
Institute Of Computer Science And Engineering Beijing Jiaotong University
関連論文
- Adaptively Combining Local with Global Information for Natural Scenes Categorization
- Multi-Scale Multi-Level Generative Model in Scene Classification
- Color Constancy Based on Image Similarity
- How the Number of Interest Points Affect Scene Classification
- Natural Scene Classification Based on Integrated Topic Simplex
- Edge-Based Color Constancy via Support Vector Regression
- Combining Attention Model with Hierarchical Graph Representation for Region-Based Image Retrieval
- A Multi-Scale Adaptive Grey World Algorithm(Image Recognition, Computer Vision)
- A Novel Tone Mapping Based on Double-Anchoring Theory for Displaying HDR Images
- Adaptively Combining Local with Global Information for Natural Scenes Categorization
- Action Recognition Using Visual-Neuron Feature
- 2D Log-Gabor Wavelet Based Action Recognition
- Adaptive Non-linear Intensity Mapping Based Salient Region Extraction
- Modeling Bottom-Up Visual Attention for Color Images
- A Visual Inpainting Method Based on the Compressed Domain(Image Processing and Video Processing)
- Moving Object Completion on the Compressed Domain
- Color Constancy Based on Effective Regions
- Predicting DataSpace Retrieval Using Probabilistic Hidden Information
- Multi-Scale Multi-Level Generation Model in Scene Classification
- Discriminating Semantic Visual Words for Scene Classification
- A Novel Saliency-Based Graph Learning Framework with Application to CBIR
- Global-Context Based Salient Region Detection in Nature Images