6506776
A set theoretical method for video synopsis
A synopsis video presents a condensed video activities occurring during different periods, based on moving objects extracted in the spatial-temporal domain. How to place different object tubes with least collision in limited video length is crucial to synopsis performance but has not been thoroughly studied in previous work.
 In this paper, we address an important problem in video synopsis, that of object start-time programming. We formulate the problem in terms of set theory. An objective is derived to maximize visual information in video synopsis. After relaxing the problem to obtain a continuous one, the problem can be efficiently solved via mean-shift. The resulting algorithm can converge to the local optimum within a few iterations.
15998088
Understand behavior and performance of Real Coded Optimization Algorithms via NK-linkage model
Classical NK-landcape model was designed for analyzing optimization and evolution process in binary solution space, so it can not be used to analyze real coded optimization algorithms (RCOAs) directly, which work in continuous solution space directly. In this paper, the concept of NK-landscape model is extended to the continuous space, and a new NK-landscape model with continuous space is proposed. The new model is powerful and comprehensive with simple structure and flexible formula. Therefore, it can be used to construct test functions of various types of linkages for analyzing various performances of RCOAs. The feasibility of the proposed model is testified via experiments with 3 well-known RCOAs, ( i.e. covariance matrix adapting evolutionary strategy (CMA-ES), differential evolution (DE), neighborhood search differential evolution (NSDE)). The results show that the new model can reveal the merits and demerits of RCOAs effectively.
9214943
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.
27086487
A Robust Iris Localization Algorithm via Radial Symmetry for Nonideal Capturing Condition
Iris localization is a key component of practical iris recognition system. Previous algorithms show good localization performances for iris images captured in the ideal conditions. However, in practice, the quality of iris image is greatly influenced by luminance, eyelashes, hair or glasses frame, which will cause mislocalization. In order to improve the robustness of iris localization, this paper proposes a new localization algorithm based on the radial symmetry transform, in which the radial symmetry characteristic of the pupil is utilized to realize iris localization. Experimental results show that the proposed algorithm can efficiently avoid the interference of luminance and other bad conditions, and realize robust precise localization in a real-time system.
17592710
Full-Space LDA With Evolutionary Selection for Face Recognition
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods
14435465
A hardware/software partitioning algorithm based on artificial immune principles
Hardware/software codesign is the main approach to designing the embedded systems. One of the primary steps of the hardware/software codesign is the hardware/software partitioning. A good partitioning scheme is a tradeoff of some constraints, such as power, size, performance, and so on. Inspired by both negative selection model and evolutionary mechanism of the biological immune system, an evolutionary negative selection algorithm for hardware/software partitioning, namely ENSA-HSP, is proposed in this paper. This ENSA-HSP algorithm is proved to be convergent, and its ability to escape from the local optimum is also analyzed. The experimental results demonstrate that ENSA-HSP is more efficient than traditional evolutionary algorithm.
35782406
License management scheme with anonymous trust for digital rights management
One of the major issues raised by digital rights management systems concerns the protection of the user's privacy and anonymous consumption of content. However, most existing license management schemes for DRM systems do not support the protection of user privacy. Moreover, some other schemes such as PrecePt can only bind the license with a specified device though they concern privacy protection. In this paper, we propose a license management scheme named LMSAT (License Management Scheme with Anonymous Trust) which provides a more powerful and flexible license acquisition and usage tracking scheme to allow the user access the contents anytime, anywhere, and on any compliant devices anonymously.
5295208
Bit rate reduction of H.264/AVC video coding for mobile applications
The increasing use of mobile applications requires higher compression efficiency for video coding. However, in mobile applications, the capability of the processor is limited and the channel is unreliable. Since the emerging H.264/AVC video coding standard provides superior compression performance compared to all the existing video coding standards, many research efforts concentrate on the compression efficiency of intra-frame coding in H.264/AVC. In this paper, we propose a novel algorithm to reduce the bit rate compressed by H.264/AVC intra-frame coding. We focus on the method to code the intra-frame prediction modes. Our proposed method significantly reduces the bits used to code the modes and do not change the syntax of the bit stream. Experimental results demonstrate it is effective and efficient
10906433
A Practical Calibration Method for Multiple Cameras
In this paper, we propose a practical factorization- and-position based method for multiple cameras calibration. The method yields a simple calibration means for an arbitrary number of linear projective cameras while maintaining the handiness and flexibility of the original method. A freely moving planar pattern as a calibration object at a few different orientations is only required. All the cameras do not have to see this pattern at all orientations, and only reasonable overlap between camera subgroups is necessary. We divide these cameras into groups according to their positions and orientations first, and then calibrate each camera in the world coordinate system of its own group via a factorization-based method. Common view fields of planar pattern are used to estimate the Euclidean transformation between these world coordinate systems and represent all cameras in a same world coordinate system. Both the intrinsic and extrinsic parameters of cameras can be obtained in a uniform world coordinate system of accuracy to within a pixel.
206600042
Multiagent Reinforcement Learning for a Planetary Exploration Multirobot System
In a planetary rover system called “SMC rover”, the motion coordination between robots is a key problem to be solved. Multiagent reinforcement learning methods for multirobot coordination strategy learning are investigated. A reinforcement learning based coordination mechanism is proposed for the exploration system. Four-robot climbing a slope is studied in detail as an instance. The actions of the robots are divided into two layers and realized respectively, which simplified the complexity of the climbing task. A Q-Learning based multirobot coordination strategy mechanism is proposed for the climbing mission. An OpenGL 3D simulation platform is used to verify the strategy and the learning results.
29762263
Look-Ahead Scheme for High Speed Consecutive Micro Line Interpolation Based on Dynamics of Machine Tool
This paper presents a new look-ahead Scheme with adaptability of dynamics of machine tool for high speed consecutive micro-line interpolation. A backtracking arithmetic is used to calculate the speeds in the link points of consecutive micro-lines. Moreover, the speed limitations in the link points are analyzed in detail and the compound constraint conditions of the motion axes is introduced based the dynamic characteristics of machine tool. The case studies indicate that, using the scheme presented by this paper, the actual interpolation speed is closer to the command speed and the shock that results from the sharp fluctuate of feed speed is depressed, which will improve the machining efficiency and quality.