Browsing by Author "Lin, Chia-Wen"

Now showing 1 - 9 of 9

A COMPARATIVE STUDY ON ATTENTION-BASED RATE ADAPTATION FOR SCALABLE VIDEO CODING
(2009)
Tsai, Chia-Ming
;
Lin, Chia-Wen
;
Lin, Weisi
;
Peng, Wen-Hsiao
;
資訊工程學系
We conduct subjective tests to evaluate the performance of scalable video coding with different spatial-domain bit-allocation methods, visual attention models, and motion feature extractors in the literature. For spatial-domain bit allocation, we use the selective enhancement and quality layer assignment methods. For characterizing visual attention, we use the motion attention model and perceptual quality significant map. For motion features, we adopt motion vectors from hierarchical B-picture coding and optical flow. Experimental results show that a more accurate visual attention model leads to better perceptual quality. In cooperation with a visual attention model, the selective enhancement method, compared to the quality layer assignment, achieves better subjective quality when an ROI has enough bit allocation and its texture is not complex. The quality layer assignment method is suitable for region-wise quality enhancement due to its frame-based allocation nature.
Automatic fall incident detection in compressed video for intelligent homecare
(2007)
Lin, Chia-Wen
;
Ling, Zhi-Hong
;
資訊工程學系
;
Department of Computer Science
This paper presents a compressed-domain fall incident detection scheme for intelligent homecare applications. First, a compressed-domain object segmentation scheme is performed to extract moving objects based on global motion estimation and local motion clustering. After detecting the moving objects, three compressed-domain features of each object are then extracted for identifying and locating fall incidents. The proposed system can differentiate fall-down from squatting by taking into account the event duration. Our experiments show that the proposed method can correctly detect fall incidents in real time.
Examplar-Based Object Posture Super-Resolution Using Manifold Learning
(2012)
Ling, Chih-Hung
;
Lin, Chia-Wen
;
Hsu, Chiou-Ting
;
Liao, Hong-Yuan Mark
;
資訊工程學系
This paper proposes a learning-based approach to increase the temporal resolutions of human motion sequences. Given a set of high resolution motion sequences, our idea is first to learn the motion tendency from this learning dataset and then synthesize new postures for the low-resolution sequence according to the learned motion tendency. We summarize the proposed framework in the following steps: (1) Each motion sequence is first projected into a low-dimension manifold space, where the local distance between postures could be better preserved. We then represent each of the projected motion sequences as a motion trajectory. (2) Next, motion priors learned from the HR training sequences are used to reconstruct the motion trajectory for the input sequence. (3) Finally, we use the reconstructed motion trajectory combined with object inpainting technique to generate the final result. Our experimental results demonstrate the effectiveness of the proposed method, and also show its outperformance over existing approaches.
Human Object Inpainting Using Manifold Learning-Based Posture Sequence Estimation
(2011-11-01)
Ling, Chih-Hung
;
Liang, Yu-Ming
;
Lin, Chia-Wen
;
Chen, Yong-Sheng
;
Liao, Hong-Yuan Mark
We propose a human object inpainting scheme that divides the process into three steps: 1) human posture synthesis; 2) graphical model construction; and 3) posture sequence estimation. Human posture synthesis is used to enrich the number of postures in the database, after which all the postures are used to build a graphical model that can estimate the motion tendency of an object. We also introduce two constraints to confine the motion continuity property. The first constraint limits the maximum search distance if a trajectory in the graphical model is discontinuous, and the second confines the search direction in order to maintain the tendency of an object's motion. We perform both forward and backward predictions to derive local optimal solutions. Then, to compute an overall best solution, we apply the Markov random field model and take the potential trajectory with the maximum total probability as the final result. The proposed posture sequence estimation model can help identify a set of suitable postures from the posture database to restore damaged/missing postures. It can also make a reconstructed motion sequence look continuous.
Motion flow-based video retrieval
(2007-10-01)
Su, Chih-Wen
;
Liao, Hong-Yuan Mark
;
Tyan, Hsiao-Rong
;
Lin, Chia-Wen
;
Chen, Duan-Yu
In this paper, we propose the use of motion vectors embedded in MPEG bitstreams to generate so-called "motion flows", which are applied to perform video retrieval. By using the motion vectors directly, we do not need to consider the shape of a moving object and its corresponding trajectory. Instead, we simply "link" the local motion vectors across consecutive video frames to form motion flows, which are then recorded and stored in a video database. In the video retrieval phase, we propose a new matching strategy to execute the video retrieval task. Motions that do not belong to the mainstream motion flows are filtered out by our proposed algorithm. The retrieval process can be triggered by query-by-sketch or query-by-example. The experiment results show that our method is indeed superb in the video retrieval process.
VIDEO OBJECT INPAINTING USING MANIFOLD-BASED ACTION PREDICTION
(2010)
Ling, Chih-Hung
;
Liang, Yu-Ming
;
Lin, Chia-Wen
;
Chen, Yong-Sheng
;
Liao, Hong-Yuan Mark
This paper presents a novel scheme for object completion in a video. The framework includes three steps: posture synthesis, graphical model construction, and action prediction. In the very beginning, a posture synthesis method is adopted to enrich the number of postures. Then, all postures are used to build a graphical model of object action which can provide possible motion tendency. We define two constraints to confine the motion continuity property. With the two constraints, possible candidates between every two consecutive postures are significantly reduced. Finally, we apply the Markov Random Field model to perform global matching. The proposed approach can effectively maintain the temporal continuity of the reconstructed motion. The advantage of this action prediction strategy is that it can handle the cases such as non-periodic motion or complete occlusion.
VIDEO OBJECT INPAINTING USING POSTURE MAPPING
(2009-01-01)
Ling, Chih-Hung
;
Lin, Chia-Wen
;
Su, Chih-Wen
;
Liao, Hong-Yuan Mark
;
Chen, Yong-Sheng
This paper presents a novel framework for object-based video inpainting. To complete an occluded object, our method first samples a 3-D volume of the video into directional spatio-temporal slices, and then performs patch-based image inpainting to repair the partially damaged object trajectories in the 2-D slices. The completed slices are subsequently combined to obtain a sequence of virtual contours of the damaged object. The virtual contours and a posture sequence retrieval technique are then used to retrieve the most similar sequence of object postures in the available non-occluded postures. Key-posture selection and indexing are performed to reduce the complexity of posture sequence retrieval. We also propose a synthetic posture generation scheme that enriches the collection of key-postures so as to reduce the effect of insufficient key-postures. Our experimental results demonstrate that the proposed method can maintain the spatial consistency and temporal motion continuity of an object simultaneously.
VIDEO OBJECT INPAINTING USING POSTURE MAPPING
(2009-01-01)
Ling, Chih-Hung
;
Lin, Chia-Wen
;
Su, Chih-Wen
;
Liao, Hong-Yuan Mark
;
Chen, Yong-Sheng
This paper presents a novel framework for object-based video inpainting. To complete an occluded object, our method first samples a 3-D volume of the video into directional spatio-temporal slices, and then performs patch-based image inpainting to repair the partially damaged object trajectories in the 2-D slices. The completed slices are subsequently combined to obtain a sequence of virtual contours of the damaged object. The virtual contours and a posture sequence retrieval technique are then used to retrieve the most similar sequence of object postures in the available non-occluded postures. Key-posture selection and indexing are performed to reduce the complexity of posture sequence retrieval. We also propose a synthetic posture generation scheme that enriches the collection of key-postures so as to reduce the effect of insufficient key-postures. Our experimental results demonstrate that the proposed method can maintain the spatial consistency and temporal motion continuity of an object simultaneously.
Virtual Contour Guided Video Object Inpainting Using Posture Mapping and Retrieval
(2011-04-01)
Ling, Chih-Hung
;
Lin, Chia-Wen
;
Su, Chih-Wen
;
Chen, Yong-Sheng
;
Liao, Hong-Yuan Mark
This paper presents a novel framework for object completion in a video. To complete an occluded object, our method first samples a 3-D volume of the video into directional spatio-temporal slices, and performs patch-based image in-painting to complete the partially damaged object trajectories in the 2-D slices. The completed slices are then combined to obtain a sequence of virtual contours of the damaged object. Next, a posture sequence retrieval technique is applied to the virtual contours to retrieve the most similar sequence of object postures in the available non-occluded postures. Key-posture selection and indexing are used to reduce the complexity of posture sequence retrieval. We also propose a synthetic posture generation scheme that enriches the collection of postures so as to reduce the effect of insufficient postures. Our experiment results demonstrate that the proposed method can maintain the spatial consistency and temporal motion continuity of an object simultaneously.