Description: STMedianFilter is a spatial/temporal median filter for for Avisynth. It now filters both
luma and chroma but chroma filtering is somewhat more limited. Try it-STMedianFilter is a spatial / temporal med ian filter for for Avisynth. It now filters both but luma and chroma chroma filtering is somewha t more limited. Try it Platform: |
Size: 59963 |
Author:yuhjay |
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Description: STMedianFilter is a spatial/temporal median filter for for Avisynth. It now filters both
luma and chroma but chroma filtering is somewhat more limited. Try it-STMedianFilter is a spatial/temporal med ian filter for for Avisynth. It now filters both but luma and chroma chroma filtering is somewha t more limited. Try it Platform: |
Size: 59392 |
Author:yuhjay |
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Description: Analysis and Hardware Design of Intermediate View Generation Using Adaptive Disparity Estimation Based on Spatial and Temporal Correlation Platform: |
Size: 166912 |
Author:peng |
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Description: 在本文中，我們首先實作了 一個針對壓縮視訊（像是 H.264/AVC）之畫面解析度 改善
的方法。接著，我們分析這個方法的模擬結果。-In this thesis, we first implement a spatial resolution enhancement algorithm for H.264/AVC
compressed videos. Based on the analysis of the simulation results, we identify a few
shortcomings of this algorithm, like some visual artifacts in the enhanced videos and some
problems caused by scene change and fast motion. Then, we propose two methods to suppress
these artifacts, including adding a median regularization term in the spatial-domain and using a
median filter in the temporal domain. We also propose two methods, a global method and a
local method, to overcome the scene change and fast motion problems. With these
modifications, the artifacts and problems are suppressed significantly. Platform: |
Size: 1556480 |
Author:hekai |
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Description: Abstract-In this paper, simple autonomous chaotic circuits
coupled by resistors are investigated. By carrying out computer
calculations and circuit experiments, irregular self-switching phenomenon
of three spatial patterns characterized by the phase
states of quasi-synchronization of chaos can be observed from
only four simple chaotic circuits. This is the same phenomenon
as chaotic wandering of spatial patterns observed very often from
systems with a large number of degrees of freedom. Namely, one
of spatial-temporal chaos observed from systems of large size can
be also generated in the proposed system consisting of only four
chaotic circuits. A six subcircuits case and a coupled chaotic circuits
networks are also studied, and such systems are confirmed
to produce more complicated spatio-temporal phenomena. Platform: |
Size: 848896 |
Author:wang |
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Description: We present a video caption detection and recognition
system based on a fuzzy-clustering neural network (FCNN) classifier.
Using a novel caption-transition detection scheme we locate
both spatial and temporal positions of video captions with high precision
and efficiency. Then employing several new character segmentation
and binarization techniques, we improve the Chinese
video-caption recognition accuracy from 13 to 86 on a set of
news video captions. As the first attempt on Chinese video-caption
recognition, our experiment results are very encouraging.-A spatial-temporal approach for video caption date Platform: |
Size: 466944 |
Author:段军伟 |
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Description: 本篇論文提出的整合式解交錯(Integrated De-interlacing)的演算法，可以有效提昇移
動區域的畫面，但是當移動估計不正確時，反而會使移動補償後的畫面變得很差，為了
改善這種情況，因此結合移動可適性解交錯的優點，並將空間圖場內插(Spatial
Interpolation)的方式改成ELA(Edge Line Average)來設計，經過電腦模擬的結果發現，不僅在視覺上提高畫面的解析度，在某些影像峰值訊號雜訊比(Peak Signal Noise Ratio ,
PSNR)也比線平均解交 錯(Line Average De-interlacing)多出好幾分貝的畫質增益。
此外，在整合式解交錯演算法中也增加影片偵測(Film Detection)和影像加強(Image
Enhancement)的演算法設計，在這樣演算法的架構下，透過影片偵測的演算法，我們可
真實地還原3:2 Pull Down 的影片格式，而不會有鋸齒狀(Saw-Toothed)的畫面出現，而影
像加強的演算法，則可以在解交錯後，經過影像的調整，使輸出畫面呈現不同的效果，
達到消費者的需求。-The main theme of this thesis is an integrated de-interlacing system, which incorporates
several known and improved techniques in a nice manner to produce good de-interlaced image
quality. We first develop an accurate motion detector that classifies image regions into
stationary, low-motion, and high-motion categories. The simple field merging method is
applied to the stationary regions. The edge line average interpolation method is applied to the
slow-motion regions. Finally, the motion-compensated interpolation is applied to the
high-motion regions. In addition, hierarchical motion estimation and motion vector smoothing
techniques are employed to enhance the quality of estimated motion vectors. Our computer
simulation shows that the subjective image quality is improved by using the proposed scheme.
Also, its PSNR measures are better than the conventional spatial or temporal interpolation
schemes. Platform: |
Size: 1171456 |
Author:robin |
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Description: 该代码用于实现视觉目标跟踪研究中的协方差跟踪，能够把多种时空特征融合于统一的模型中，在实现视觉目标跟踪时具有较好的鲁棒性，而且其维数等于使用特征的数量，与各个特征的维数无关，因此，其计算复杂度较小，实时性较好。-This code is used to realize covariance tracking in the research field of visual object tracking of computer vision. It enables fusion of various spatial-temporal features into an unified object feature model, hence robust to noise. In addition, its dimension is equal to the number of the features used and independent of the dimensions of the features used. Therefore, it satisfies the requirement of real-time application due to its low computation cost. Platform: |
Size: 133120 |
Author:朱亮亮 |
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Description: 基于CLUE_S模型的喀斯特地区城镇用地时空动态模拟_-Karst area based on CLUE_S model of urban land use spatial temporal dynamic simulation _ Platform: |
Size: 811008 |
Author:LM88 |
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Description: 行为识别参考文献一篇，引入了空时关系描述局部特征-References a behavior recognition, the introduction of local features describe the relationship between space-time Platform: |
Size: 270336 |
Author:zhtch |
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Description: A new point process transition density model is proposed
based on the ory of point patterns for predicting the likelihood
of occurrence of spatial–temporal random events. The model
provides a framework for discovering and incorporating event initiation
preferences in terms of clusters of feature values. Components
of the proposed model are specified taking into account additional
behavioral assumptions such as the “journey to event” and
“lingering period to resume act.” Various feature selection techniques
are presented in conjunction with the proposed model. Extending
knowledge discovery into feature space allows for extrapolation
beyond spatial or temporal continuity and is shown to be
a major advantage of our model over traditional approaches. We
examine the proposed model primarily in the context of predicting
criminal events in space and time.-A new point process transition density model is proposed
based on the theory of point patterns for predicting the likelihood
of occurrence of spatial–temporal random events. The model
provides a framework for discovering and incorporating event initiation
preferences in terms of clusters of feature values. Components
of the proposed model are specified taking into account additional
behavioral assumptions such as the “journey to event” and
“lingering period to resume act.” Various feature selection techniques
are presented in conjunction with the proposed model. Extending
knowledge discovery into feature space allows for extrapolation
beyond spatial or temporal continuity and is shown to be
a major advantage of our model over traditional approaches. We
examine the proposed model primarily in the context of predicting
criminal events in space and time. Platform: |
Size: 327680 |
Author:ahmed |
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Description: A significant amount of research and practice in the law enforcement arena focuses on spatial
and temporal event analysis. And although some efforts have been made to integrate spatial and
temporal analysis, the majority of the previous work focuses on either a spatial or temporal
analysis. This research adds temporal and neighborhood indicator functions to a feature-space
based Generalized Linear Model (GLM) to identify patterns both spatially and temporally within
an actor’s site selection criteria. Platform: |
Size: 327680 |
Author:ahmed |
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Description: ue to the volume conduction multichannel electroencephalogram (EEG) recordings
give a rather blurred image of brain activity. Therefore spatial filters are
extremely useful in single-trial analysis in order to improve the signal-to-noise
ratio. There are powerful methods from machine learning and signal processing
that permit the optimization of spatio-temporal filters for each subject in a data
dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here
we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular
method in brain-computer interface (BCI) research. Apart from reviewing several variants of
the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance,
briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing
in our studies of the Berlin BCI (BBCI) project. Platform: |
Size: 1248256 |
Author:fariba |
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Search - spatial temporal