1287667
Video object segmentation by motion-based sequential feature clustering
Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and manipulation. Most existing methods work on image pixels or color segments which are computationally expensive. Some methods require extensive manual inputs, static cameras, and/or rigid scenes. In this paper we propose a fully automatic foreground segmentation method based on sequential clustering of sparse image features. The sparseness makes the method computationally efficient. We use both edge and corner points extracted from each video frame. A joint spatio-temporal linear regression method is developed to compute sparse motion layers of M consecutive frames jointly under the temporal consistency constraint. Once the sparse motion layers have been identified for each frame, the corresponding dense motion layers are created using the Markov Random Field (MRF) model. The MRF model assigns the rest of the image pixels to the motion layers by considering both the color attributes and the spatial relations between each pixel and its surrounding edge/corner points. Experimental evaluations on videos taken by webcams show the effectiveness of the proposed method.
7153454
Temporal difference learning to detect unsafe system states
This paper proposes a general framework to detect unsafe states of a system whose basic realtime parameters are captured by multi-sensors. Our approach is to learn a danger level function which can be used to alert the users in advance of dangerous situations. The main challenge to this learning problem is the labelling issue, i.e., it is difficult to assign an objective danger level at each time step to the training data, except at the collapse points where a penalty can be assigned and at the successful ends where a certain reward can be assigned. In this paper, we treat the danger level as expected future reward (penalty is regarded as negative reward) and use temporal difference (TD) learning [2] to learn a function to approximate the expected future reward. The TD learning obtains the approximation by propagating the penalty/reward observable at collapse points or successful ends to the entire feature space following some constraints. Our approach is applied to, but not limited to, the application of monitoring of driving safety and the experimental results demonstrate the effectiveness of the approach.
15069022
An integrated baseball digest system using maximum entropy method
In this paper, we propose a novel system that is able to automatically detect and classify highlights from baseball game videos in TV broadcast. The digest system gives complete indexes of a baseball game which cover all of the status changes in a game. We achieve this by seamlessly integrating image, audio and speech clues using a maximum entropy based method. What distinguishes our system from previous ones is that we emphasize on the integration of multimedia features and the acquisition of domain knowledge through machine learning process. Integration of multimedia features is important because with the current state-of-the-art image and audio analysis techniques, most image and audio features we can extract from videos are very low level, and detecting/classifying sports game highlights based on features from single medium are doomed to yield poor performances. Acquiring domain knowledge through learning process is preferred over heuristic rules because machine learning process is more powerful for discovering and expressing domain knowledge. We perform extensive experiments on game videos including various stadiums, teams and broadcasted by different TV stations.
2237682
Document clustering based on non-negative matrix factorization
In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive combination of the base topics. The cluster membership of each document can be easily determined by finding the base topic (the axis) with which the document has the largest projection value. Our experimental evaluations show that the proposed document clustering method surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustering results, but also in document clustering accuracies.
9180515
Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities
In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs.
2171158
Detecting video events based on action recognition in complex scenes using spatio-temporal descriptor
Event detection plays an essential role in video content analysis and remains a challenging open problem. In particular, the study on detecting human-related video events in complex scenes with both a crowd of people and dynamic motion is still limited. In this paper, we investigate detecting video events that involve elementary human actions, e.g. making cellphone call, putting an object down, and pointing to something, in complex scenes using a novel spatio-temporal descriptor based approach. A new spatio-temporal descriptor, which temporally integrates the statistics of a set of response maps of low-level features, e.g. image gradients and optical flows, in a space-time cube, is proposed to capture the characteristics of actions in terms of their appearance and motion patterns. Based on this kind of descriptors, the bag-of-words method is utilized to describe a human figure as a concise feature vector. Then, these features are employed to train SVM classifiers at multiple spatial pyramid levels to distinguish different actions. Finally, a Gaussian kernel based temporal filtering is conducted to segment the sequences of events from a video stream taking account of the temporal consistency of actions. The proposed approach is capable of tolerating spatial layout variations and local deformations of human actions due to diverse view angles and rough human figure alignment in complex scenes. Extensive experiments on the 50-hour video dataset of TRECVid 2008 event detection task demonstrate that our approach outperforms the well-known SIFT descriptor based methods and effectively detects video events in challenging real-world conditions.
14260748
Segment Clustering Radar Signal Sorting
Radar signal sorting is picking-up pulse serial of same radar emitter from dense complex pulse signal flow. The tolerance of radar signal sorting is analyzed in modern electronic warfare. The complex and dense pulses environment makes it become a vital factor to restrict the efficiency of sorting of the conventional multi-parameters signal sorting system. A segment clustering radar signal sorting method is presented based on support vector clustering (SVC) according to the idea of statistics learning theory. It prevents tolerance from affecting radar sorting. The accuracy of sorting and the sensitivity of algorithm on parameter variation is analyzed. The experimental results show that the sorting method presented is effective to overcome the tolerance of radar signal sorting.
11893705
View Synthesis for Virtual Walk through in Real Scene Based on Catadioptric Omnidirectional Images
This paper proposes an approach to more conveniently and efficiently create virtual walk through in real scene from catadioptric omni directional images via view synthesis. Acquisition and unwarping process of omnidirectional image are discussed firstly, then, according to the speciality of cylindrical panoramic image, we utilize the advantage of epiline-sampling method for cylindrical image, which samples reference images along epilines as much as possible grounding on epipolar geometry. As to novel view generation after image rectification, a corresponding interpolation algorithm based on rectified images is developed. Results of proposed approach applied to both synthetic and real scene are given at the end of this paper. Experiments show that our method can reduce distortion and pixel information losing from image rectification obviously meanwhile maintain scene information well.
38041019
Towards an Efficient Self-organizing Reconfiguration Method for Self-reconfigurable Robots
This paper deals with the essential technique for self-organizing metamorphosis of self-reconfigurable robots. A general model describing self-reconfigurable robots is proposed; the model is capable of expressing the topological structure of typical 2-D and 3-D self-reconfigurable robots. Afterwards, a self-organizing metamorphic strategy of self-reconfigurable robots, based on full-discrete local intelligence, is proposed. The modules concurrently process the local interactive information. A reasonable motion sequence for modules can be obtained by means of determining and evolving the rules of modules motion. Consequently, the global self-organizing behavior will be obtained, and the complexity of the algorithm is reduced. Simulation samples are provided to illustrate this approach and demonstrate the effectiveness of the proposed strategy.
206821455
Compositional dependence of wetting and contact angles in solid-liquid-liquid systems under realistic environments
The wetting and contact angles in porous media are important for the characterization of multi-phase flow behavior. The polar components such as asphaltenes in the oil-phase, which have been widely believed to be a major source of oil-wetting characteristics. The effect of light gaseous ends in crude oil on wetting is studied by depleting the pressure at regular intervals. The variation of contact angles observed with the pressure during the pressure depletion clearly showed the influence of light ends on wetting. The water-advancing contact angle of live crude oil gradually increased from 55/spl deg/ at bubble point pressure to 154/spl deg/ at ambient pressure. The de-asphaltened and the deresined crude oils showed strong oil-wet characteristics as stocktank crude oil. The light gaseous ends in oil phase appear to peptize the asphaltene molecules by surrounding them, thereby preventing their agglomeration and migration to the interface. The differences observed in the behavior of dynamic interfacial tensions between live and stocktank crude oils further substantiates the influence of light gaseous ends on the activity of polar components in live oil. The influence of solid surface roughness, mineralogy of rock substrates as well as the brine composition on wetting and contact angles have also been investigated. For highly smooth substrates, carbonate and silica showed relatively similar high water-advancing contact angles, while for rough substrates, the advancing contact angles on silica were much lower compared to the carbonates.
5556814
Travelling wave solutions in the generalized Hirota-Satsuma coupled KdV system
By using the bifurcation theory of dynamical systems to the generalized Hirota-Satsuma coupled KdV system, bifurcation parameter sets are shown. Numbers of solitary waves, kink waves and periodic waves are given. Under various parameter conditions, all explicit formulas of solitary wave solutions and kink wave solutions and periodic wave solutions are listed.