Image matching is the first step in almost any 3D computer vision task, and hence has received extensive attention. In this paper, the problem is addressed from a novel perspective, which is different from the classic...
详细信息
ISBN:
(纸本)9780819469502
Image matching is the first step in almost any 3D computer vision task, and hence has received extensive attention. In this paper, the problem is addressed from a novel perspective, which is different from the classic stereo matching paradigm. Two images with different resolutions, that is high resolution versus low resolution are matched. Since the high resolution image only corresponds to a small region of the low resolution one, the matching task therefore consists in finding a small region in the low resolution image that can be assigned to the whole high resolution image under the plane similarity transformation, which can be defined by the local area correlation coefficient to match the interest points and rectified by similarity transform. Experiment shows that our matching algorithm can be used for scale changing up to a factor of 6. And it is successful to deal with the point matching between two images under large scale.
The spacing distance of amino acids is used to study the relationship between the primary structure and structural classification of large proteins. Rescaled-range (R/S) analysis was adopted to examine long-range corr...
详细信息
The spacing distance of amino acids is used to study the relationship between the primary structure and structural classification of large proteins. Rescaled-range (R/S) analysis was adopted to examine long-range correlation property of proteins. And multifractal property is then discussed by the spacing distance of amino acids. A classification of proteins by assigning to each sequence a point in two-dimensional space (Aa,H) is given to construct a phylogenetic tree. This result is shown to be reasonably satisfactory to alpha + beta class.
In this paper, we use the relations of quotient space theory and martingale therory to research the iterated function system that is fractal geometry images, and propose these conclusions: Given an irreducible iterate...
详细信息
ISBN:
(纸本)7900719229
In this paper, we use the relations of quotient space theory and martingale therory to research the iterated function system that is fractal geometry images, and propose these conclusions: Given an irreducible iterated function system {X,wipij;i,j = 1,2,}, then exists a corresponding chain of quotient space {Wk = (Xk, μk,Fk);&=1,2,:} and a martingale {(μk, Fk );k = 1,2,}on the chain, therefore there are: l)Assume Pk is a invariant subsets of Wk, P is a invariant subsets of W, then exists lim k&rarr∞ Pk =P and the convergence is according to Hausdorff distance. 2)Assume μk is a invariant measure of Fk, μ is a invariant measure of F, then exists limμk k&rarr∞=μ 3) Pk is a support set of μk, P is a support set of μ Namely we present the quotient approximation theorem about fractal geometry images, and build relations among chain of quotient space, martingale, fractal geometry images and Markovian process.
In order to represent a complex large concept hierarchy tree, this paper proposes a more general coding scheme which applies concept hierarchy into the mining of fuzzy association rules. As it is difficult to determin...
详细信息
In order to represent a complex large concept hierarchy tree, this paper proposes a more general coding scheme which applies concept hierarchy into the mining of fuzzy association rules. As it is difficult to determine the membership function subjectively, a self-organizing feature map (SOFM) network is introduced to determine the membership function of sample data. Based on the improved coding scheme and the SOFM network, fuzzy set is then introduced to design a new algorithm of mining multi-level fuzzy association rules. Experimental results show that the proposed algorithm is of high efficiency and scalability and can effectively mine multi-level fuzzy association rules that are meaningful and easily understandable.
Automatic keyword extraction is one of the most important techniques in natural language processing. In this paper, features of complex networks composed of Chinese are studied. A novel automatic keyword extraction al...
详细信息
Automatic keyword extraction is one of the most important techniques in natural language processing. In this paper, features of complex networks composed of Chinese are studied. A novel automatic keyword extraction algorithm for Chinese document is proposed which is based on the features of the complex networks according to the small world structure in language networks and the theoretical achievements in complex networks. It extracts keyword based on the feature values of the word nodes in a documental language network. Experimental results show the proposed algorithm obtains higher average precision compared with the keyword extraction algorithm based on TFIDF.
It is widely recognized that clustering ensemble is fit for any shape and any distribution dataset and that the boosting method provides superior results for classification problems. In the paper, a dual boosting is p...
详细信息
It is widely recognized that clustering ensemble is fit for any shape and any distribution dataset and that the boosting method provides superior results for classification problems. In the paper, a dual boosting is proposed for fuzzy clustering ensemble . At each boosting iteration, a new training set is created based on the original datasets' probability which is associated with the previous clustering. According to the dual boosting method, the new training subset contains not only the instances which is hard to cluster in previous stages , but also the instances which is easy to cluster. The final clustering solution is produced by using the clustering based on the co-association matrix. Experiments on both artificial and realworld datasets demonstrate the efficiency of the fuzzy clustering ensemble based on dual boosting in stability and accuracy.
Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative mod...
详细信息
Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative model to eliminate cast shadow on Discriminative Random Fields (DRFs). The method combines different features for Boosting to discriminate cast shadow from moving objects, then temporal and spatial coherence of shadow and foreground are incorporated on Discriminative Random Fields and the problem can be solved by graph cut. Firstly, moving objects are obtained by background subtraction;secondly, shadow candidates can be derived through pre-processing moving objects, in terms of the shadow physical property;thirdly, color information and texture information is derived by comparing shadow and foreground points in current image with corresponding points in background image, which are selected as features for Boosting;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields and discriminate shadow and foreground by graph cut accurately.
The DS-CDMA signal model and the noisy linear independent component analysis (ICA) model are analyzed in this paper. Comparing these models shows that they have the same form. The adaptive minimum mean-square error (M...
The DS-CDMA signal model and the noisy linear independent component analysis (ICA) model are analyzed in this paper. Comparing these models shows that they have the same form. The adaptive minimum mean-square error (MMSE) multiuse detection based on ICA is proposed. It uses the output of adaptive MMSE multi-user detection to initialize the ICA iterations, not only the known spread information of interesting user is used to overcome the uncertainness of ICA, but also the character of statistical independence is used. The simulation results show that the performance is improved obviously.
In this article, we propose a (t,n) threshold verifiable multi-secret sharing scheme, in which to reconstruct t secrets needs to solve t simultaneous equations. The analysis results show that our scheme is as easy as ...
详细信息
In this article, we propose a (t,n) threshold verifiable multi-secret sharing scheme, in which to reconstruct t secrets needs to solve t simultaneous equations. The analysis results show that our scheme is as easy as Yang's scheme [8] in the secret reconstruction and requires less public values than Chien's [7] and Yang's schemes. Furthermore, the shares in our scheme can be verified their validity with t public values based on ECDLP, and there are two verified forms: one is computationally secure as Feldman 's scheme [12] and other is unconditionally secure as Pedersen's scheme [13]. In addition, for the main computation: a i,1 P 1 + a i,2 P 2 + hellip + a i,t P t in our scheme, we present a new method based on the signed factorial expansion and implement it, the results show that it is more efficient than the current public methods. Thus our scheme is a secure and efficient (t,n) threshold verified multi-secret sharing scheme.
暂无评论