6045052
Basis pursuit for tracking
This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon nonlinearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion.
14368338
Optimal radial contour tracking by dynamic programming
A common problem in most active contour methods is that the recursive searching scheme can only return a local optimal solution. Furthermore, the internal energy of the snake is not strong enough to control the shape of the contour. To overcome these difficulties, we develop a causal internal energy term based on a radial contour representation to encode the smooth constraint of the contour, and develop a global shape priori to control contour's shape and position based on object's dynamics. The causality nature of the representation allows us to efficiently find global optimal solution using dynamic programming. To validate the efficacy and robustness of the proposed approach, we apply this approach to track people in bad illumination and cluttered environments. We report promising results in the paper.
10370670
Top-Down Design of Bulk-Synchronous Parallel Programs
This paper studies top-down program development techniques for Bulk-Synchronous Parallelism. In that context a specification formalism LOGS, for 'the Logic of Global Synchrony', has been proposed for the specification and high-level development of BSP designs. This paper extends the use of LOGS to provide support for the protection of local variables in BSP programs, thus completing the link between specifications and programs.
9845542
Back Propagation with Randomized Cost Function for Training Neural Networks
A novel method to improve both the generalization and convergence performance of the back propagation algorithm (BP) by using multiple cost functions with a randomizing scheme is proposed in this paper. Under certain conditions, the randomized technique will converge to the global minimum with probability one. Experimental results on benchmark Encoder-Decoder problems and the NC2 classification problem show that the method is effective in enhancing BP's convergence and generalization performance
6176366
The Ramsey numbers of stars versus wheels
For two given graphs G 1 and G 2 , the Ramsey number R(G 1 , G 2 ) is the smallest positive integer n such that for any graph G of order n, either G contains G 1 or the complement of G contains G 2 . Let S n denote a star of order n and W m a wheel of order m + 1. This paper shows that R(S n , W 6 )=2n + 1 for n ≥ 3 and R(S n , W m )=3n - 2 for m odd and n ≥ m - 1 ≥ 2.
34645664
Applying Family Competition to Evolution Strategies for Constrained Optimization
This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.
17475296
Statistical geometrical features for texture classification
Abstract This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray Level Dependence Matrix method, the recently proposed Statistical Feature Matrix, and Liu's features. The deterioration in performance with the increase in the number of textures in the set is less with the new SGF features than with the other methods, indicating that SGF is capable of handling a larger texture population. The new method's performance under additive noise is also shown to be the best of the four.