12496830
A Fast Algorithm for Color Image Segmentation
Based on K-means and a two-layer pyramid structure, a fast algorithm is proposed for color image segmentation. The algorithm employs two strategies. Firstly, a two-layer structure of a color image is established. Then, an improved K-means with integer based lookup table implementation is applied to each layer. The clustering result on the upper layer (lower resolution) is used to guide the clustering in the lower layer (higher resolution). Experiments have shown that the proposed algorithm is significantly faster than the original K-means algorithm while producing comparable segmentation results
7583260
Simultaneous MAP estimation of inhomogeneity and segmentation of brain tissues from MR images
Intrascan and interscan intensity inhomogeneities have been identified as a common source of making many advanced segmentation techniques fail to produce satisfactory results in separating brains tissues from multi-spectral magnetic resonance (MR) images. A common solution is to correct the inhomogeneity before applying the segmentation techniques. This paper presents a method that is able to achieve simultaneous semi-supervised MAP (maximum a-posterior probability) estimation of the inhomogeneity field and segmentation of brain tissues, where the inhomogeneity is parameterized. Our method can incorporate any available incomplete training data and their contribution can be controlled in a flexible manner and therefore the segmentation of the brain tissues can be optimised. Experiments on both simulated and real MR images have demonstrated that the proposed method estimated the inhomogeneity field accurately and improved the segmentation.
13400769
Simulation of Human Motion for Learning and Recognition
Acquisition of good quality training samples is becoming a major issue in machine learning based human motion analysis. This paper presents a method to simulate human body motion with the intention to establish a human motion corpus for learning and recognition. The simulation is achieved by a unique temporal-spatial-temporal decomposition of human body motion into actions, joint actions and actionlets based on the human kinematic model. The actionlet models the primitive moving phase of a joint and can be simulated based on the kinesiological study. A joint action is formed by proper concatenation of actionlets and an action is a group of synchronized joint actions. Methods for concatenation and synchronization are proposed in this paper for realistic simulation of human motion. Results on simulating ”running” verifies the feasibility of the proposed method.
9628721
A Homogeneous PCS network with Markov Call Arrival Process and Phase Type Cell Residence Time
In this paper, the arrival of calls (i.e., new and handoff calls) in a personal communications services (PCS) network is modeled by a Markov arrival process (MAP) in which we allow correlation of the interarrival times among new calls, among handoff calls, as well as between these two kinds of calls. The PCS network consists of homogeneous cells and each cell consists of a finite number of channels. Under the conditions that both cell's residence time and the requested call holding time possess the general phase type (PH) distribution, we obtain the distribution of the channel holding times, the new call blocking probability and the handoff call failure probability. Furthermore, we prove that the cell residence time is PH distribution if and only if• the new call channel holding time is PH distribution; or• the handoff call channel holding time is PH distribution; or• the call channel holding time is PH distribution;provided that the requested call holding time is a PH distribution and the total call arrival process is a MAP. Also, we prove that the actual call holding time of a non-blocked new call is a mixture of PH distributions. We then developed the Markov process for describing the system and found the complexity of this Markov process. Finally, two interesting measures for the network users, i.e., the duration of new call blocking period and the duration of handoff call blocking period, are introduced; their distributions and the expectations are then obtained explicitly.
450045
A Bootstrapping Approach to Named Entity Classification Using Successive Learners
This paper presents a new bootstrapping approach to named entity (NE) classification. This approach only requires a few common noun/pronoun seeds that correspond to the concept for the target NE type, e.g. he/she/man/woman for PERSON NE. The entire bootstrapping procedure is implemented as training two successive learners: (i) a decision list is used to learn the parsing-based high precision NE rules; (ii) a Hidden Markov Model is then trained to learn string sequence-based NE patterns. The second learner uses the training corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types. The system also demonstrates intuitive support for tagging user-defined NE types. The differences of this approach from the co-training-based NE bootstrapping are also discussed.
18767466
Spectra of Regular Graphs and Hypergraphs and Orthogonal Polynomials
Abstract In this paper we study the distribution of eigenvalues of regular graphs, regular hypergraphs, and biregular bipartite graphs of given girth by considering the polynomials orthogonal with respect to the measures attached to the spectra of such graphs and to the continuous spectra of their ‘universal covers’. Our estimates are tight for Biggs graphs and generalized polygons. We also give an application to the distribution of eigenvalues of Hecke operators acting on weight 2 cusp forms for certain congruence subgroups.
12034739
An Algorithm for Determining the Controllers of Supervised Entities at the First and Second Levels: A Case Study with the Brazilian Central Bank
An algorithm was developed and implemented to find controllers from the stock shares for some financial institutions for the Brazilian Central Bank (BCB). The original problem is similar to a typical Sum of Subset problem that is suggested to be solved by a backtracking algorithm and the problem complexity is NP-complete. Usually BCB solves this problem manually which is time consuming and prone to errors. The heuristical approximation algorithm presented in this paper has polynomial complexity O(n3) and is based on subroutines for determining controllers at the first two levels. The paper describes the basic concepts and business rules currently employed in BCB, our algorithm and its major subroutines, it gives a brief complexity analysis and an example illustration at level 2. Our experimental results indicate the feasibility of an automation of the process of finding controllers. Though developed for BCB, our algorithm works equally well for other financial institutions.
6001623
A segment alignment approach to protein comparison
MOTIVATION
Local structure segments (LSSs) are small structural units shared by unrelated proteins. They are extensively used in protein structure comparison, and predicted LSSs (PLSSs) are used very successfully in ab initio folding simulations. However, predicted or real LSSs are rarely exploited by protein sequence comparison programs that are based on position-by-position alignments.


RESULTS
We developed a SEgment Alignment algorithm (SEA) to compare proteins described as a collection of predicted local structure segments (PLSSs), which is equivalent to an unweighted graph (network). Any specific structure, real or predicted corresponds to a specific path in this network. SEA then uses a network matching approach to find two most similar paths in networks representing two proteins. SEA explores the uncertainty and diversity of predicted local structure information to search for a globally optimal solution. It simultaneously solves two related problems: the alignment of two proteins and the local structure prediction for each of them. On a benchmark of protein pairs with low sequence similarity, we show that application of the SEA algorithm improves alignment quality as compared to FFAS profile-profile alignment, and in some cases SEA alignments can match the structural alignments, a feat previously impossible for any sequence based alignment methods.