70 research outputs found
Multiscale motion saliency for keyframe extraction from motion capture sequences
Cataloged from PDF version of article.Motion capture is an increasingly popular animation technique; however data acquired by motion capture can become substantial. This makes it difficult to use motion capture data in a number of applications, such as motion editing, motion understanding, automatic motion summarization, motion thumbnail generation, or motion database search and retrieval. To overcome this limitation, we propose an automatic approach to extract keyframes from a motion capture sequence. We treat the input sequence as motion curves, and obtain the most salient parts of these curves using a new proposed metric, called 'motion saliency'. We select the curves to be analysed by a dimension reduction technique, Principal Component Analysis (PCA). We then apply frame reduction techniques to extract the most important frames as keyframes of the motion. With this approach, around 8% of the frames are selected to be keyframes for motion capture sequences. Copyright (C) 2010 John Wiley & Sons, Ltd
A multi scale motion saliency method for keyframe extraction from motion capture sequences
Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 47-50.Motion capture is an increasingly popular animation technique; however data acquired
by motion capture can become substantial. This makes it di cult to use
motion capture data in a number of applications, such as motion editing, motion
understanding, automatic motion summarization, motion thumbnail generation,
or motion database search and retrieval. To overcome this limitation, we propose
an automatic approach to extract keyframes from a motion capture sequence.
We treat the input sequence as motion curves, and obtain the most salient parts
of these curves using a new proposed metric, called 'motion saliency'. We select
the curves to be analyzed by a dimension reduction technique, Principal Component
Analysis. We then apply frame reduction techniques to extract the most
important frames as keyframes of the motion. With this approach, around 8% of
the frames are selected to be keyframes for motion capture sequences. We have
quanti ed our results both mathematically and through user tests.Halit, CihanM.S
Pose selection for animated scenes and a case study of bas-relief generation
This paper aims to automate the process of generating a meaningful single still image from a temporal input of scene sequences. The success of our extraction relies on evaluating the optimal pose of characters selection, which should maximize the information conveyed. We define the information entropy of the still image candidates as the evaluation criteria. To validate our method and to demonstrate its effectiveness, we generated a relief (as a unique form of art creation) to narrate given temporal action scenes. A user study was conducted to experimentally compare the computer-selected poses with those selected by human participants. The results show that the proposed method can assist the selection of informative pose of character effectively
Spatiotemporal Saliency Detection: State of Art
Saliency detection has become a very prominent subject for research in recent time. Many techniques has been defined for the saliency detection.In this paper number of techniques has been explained that include the saliency detection from the year 2000 to 2015, almost every technique has been included.all the methods are explained briefly including their advantages and disadvantages. Comparison between various techniques has been done. With the help of table which includes authors name,paper name,year,techniques,algorithms and challenges. A comparison between levels of acceptance rates and accuracy levels are made
Enriquecendo animações em quadros-chaves espaciais com movimento capturado
While motion capture (mocap) achieves realistic character animation at great cost, keyframing is capable of producing less realistic but more controllable animations. In this work we show how to combine the Spatial Keyframing (SK) Framework of IGARASHI et al. [1] and multidimensional projection techniques to reuse mocap data in several ways. Additionally, we show that multidimensional projection also can be used for visualization and motion analysis. We also propose a method for mocap compaction with the help of SK’s pose reconstruction (backprojection) algorithm. Finally, we present a novel multidimensional projection optimization technique that significantly enhances SK-based reconstruction and can also be applied to other contexts where a backprojection algorithm is available.Movimento capturado (mocap) produz animacões de personagens com grande realismo mas a um custo alto. A utilização de quadros-chave torna mais difícil um resultado com realismo mas torna mais fácil o controle da animacão. Neste trabalho, mostramos como combinar o uso de quadros-chaves espaciais – Spatial Keyframing (SK) Framework – de IGARASHI et al. [1] e técnicas de projeção multidimensional para reutilizar dados de movimento capturado de várias maneiras. Mostramos também como projeções multidimensionais podem ser utilizadas para visualização e análise de movimento. Propomos um método de compactação de dados de mocap utilizando a reconstrução de poses por meio do algoritmo de quadros-chaves espaciais. Também apresentamos uma técnica de otimização para as projeções multidimensionais que melhora a reconstrução do movimento e que pode ser aplicada em outros casos onde um algoritmo de retroprojecão esteja dad
BilVideo-7 : video parsing, indexing and retrieval
Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Ph. D.) -- Bilkent University, 2010.Includes bibliographical references leaves 91-103.Video indexing and retrieval aims to provide fast, natural and intuitive access to
large video collections. This is getting more and more important as the amount of
video data increases at a stunning rate. This thesis introduces the BilVideo-7 system
to address the issues related to video parsing, indexing and retrieval.
BilVideo-7 is a distributed and MPEG-7 compatible video indexing and retrieval
system that supports complex multimodal queries in a unified framework. The video
data model is based on an MPEG-7 profile which is designed to represent the videos
by decomposing them into Shots, Keyframes, Still Regions and Moving Regions. The
MPEG-7 compatible XML representations of videos according to this profile are obtained
by the MPEG-7 compatible video feature extraction and annotation tool of
BilVideo-7, and stored in a native XML database. Users can formulate text, color,
texture, shape, location, motion and spatio-temporal queries on an intuitive, easy-touse
visual query interface, whose composite query interface can be used to formulate
very complex queries containing any type and number of video segments with their
descriptors and specifying the spatio-temporal relations between them. The multithreaded
query processing server parses incoming queries into subqueries and executes
each subquery in a separate thread. Then, it fuses subquery results in a bottom-up manner
to obtain the final query result and sends the result to the originating client. The
whole system is unique in that it provides very powerful querying capabilities with a
wide range of descriptors and multimodal query processing in an MPEG-7 compatible
interoperable environment.Baştan, MuhammetPh.D
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Divide-and-conquer based summarization framework for extracting affective video content
YesRecent advances in multimedia technology have led to tremendous increases in the available volume of video data, thereby creating a major requirement for efficient systems to manage such huge data volumes. Video summarization is one of the key techniques for accessing and managing large video libraries. Video summarization can be used to extract the affective contents of a video sequence to generate a concise representation of its content. Human attention models are an efficient means of affective content extraction. Existing visual attention driven summarization frameworks have high computational cost and memory requirements, as well as a lack of efficiency in accurately perceiving human attention. To cope with these issues, we propose a divide-and-conquer based framework for an efficient summarization of big video data. We divide the original video data into shots, where an attention model is computed from each shot in parallel. Viewer's attention is based on multiple sensory perceptions, i.e., aural and visual, as well as the viewer's neuronal signals. The aural attention model is based on the Teager energy, instant amplitude, and instant frequency, whereas the visual attention model employs multi-scale contrast and motion intensity. Moreover, the neuronal attention is computed using the beta-band frequencies of neuronal signals. Next, an aggregated attention curve is generated using an intra- and inter-modality fusion mechanism. Finally, the affective content in each video shot is extracted. The fusion of multimedia and neuronal signals provides a bridge that links the digital representation of multimedia with the viewer’s perceptions. Our experimental results indicate that the proposed shot-detection based divide-and-conquer strategy mitigates the time and computational complexity. Moreover, the proposed attention model provides an accurate reflection of the user preferences and facilitates the extraction of highly affective and personalized summaries.Supported by the ICT R&D program of MSIP/IITP. [2014(R0112-14-1014), The Development of Open Platform for Service of Convergence Contents]
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