14083444
SoftCuts: A Soft Edge Smoothness Prior for Color Image Super-Resolution
Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for evaluating their smoothness. This paper characterizes soft edge smoothness based on a novel SoftCuts metric by generalizing the Geocuts method . The proposed soft edge smoothness measure can approximate the average length of all level lines in an intensity image. Thus, the total length of all level lines can be minimized effectively by integrating this new form of prior. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image SR, by adaptively normalizing image edges according to their alpha-channel description. This leads to the adaptive SoftCuts algorithm, which represents a unified treatment of edges with different contrasts and scales. Experimental results are presented which demonstrate the effectiveness of the proposed method.
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.
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.
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.
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.
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.
26455783
Neuroscience output of China: A MEDLINE-based bibliometric study
Neuroscience is one of the most active research fields in many countries including China, an economically and scientifically emerging country, where a rapid development has been occurred since the 1970s. In this study, a MEDLINE-based bibliometric analysis of Chinese international output in neuroscience was conducted for the period from 1984 through 2001. An attempt was made to identify the pattern of the growth and to obtain some quantitative indicators over the literature studied in order to review at the developing steps of neuroscience in China during the period.
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.
1898486
A Multi-Channel Algorithm for Edge Detection Under Varying Lighting
In vision-based autonomous spacecraft docking multiple views of scene structure captured with the same camera and scene geometry is available under different lighting conditions. These "multiple-exposure" images must be processed to localize visual features to compute the pose of the target object. This paper describes a robust multi-channel edge detection algorithm that localizes the structure of the target object from the local gradient distribution computed over these multiple-exposure images. This approach reduces the effect of the illumination variation including the effect of shadow edges over the use of a single image. Experiments demonstrate that this approach has a lower false detection rate than the average response of the Canny edge detector applied to the individual images separately.
2507502
An Analytical Performance Model of IP-SAN
In this paper we propose a component-based analytical performance model for IP-SAN. We model four key components within IP-SAN individually and they are IP network, cache, RAID and disks. We validate these component models and compose them to give an entire IP-SAN performance model. The entire IP-SAN model analyzes the performance of an IP-SAN configuration under multiple synchronous workloads by using closed queueing network model and under asynchronous workload by using open queueing network model. We also validate the entire IP-SAN performance model against a real IP-SAN system. The theoretical results are compared with the results from a real IP-SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. To our knowledge, this is the first exploration into building analytical performance model for entire IP-SAN system and validating against a real system. We also identify areas for future research in the performance analysis of real IP-SAN.
38063423
A series of explicit and exact travelling wave solutions of the B(m, n) equations
Abstract In this paper, explicit and exact travelling wave solutions of B ( m ,  n ) equations are investigated by using a generalized algebraic method. A series of explicit exact solutions, such as solitary wave, triangular periodic, rational, Jacobi and Weierstrass elliptic function periodic solutions are obtained. Many new results are presented.