A probabilistic spectral framework for grouping and segmentation

This paper presents an iterative spectral framework for pairwise clustering and perceptual grouping. Our model is expressed in terms of two sets of parameters. Firstly, there are cluster memberships which represent the affinity of objects to clusters. Secondly, there is a matrix of link weights for pairs of tokens. We adopt a model in which these two sets of variables are governed by a Bernoulli model. We show how the likelihood function resulting from this model may be maximised with respect to both the elements of link-weight matrix and the cluster membership variables. We establish the link between the maximisation of the log-likelihood function and the eigenvectors of the link-weight matrix. This leads us to an algorithm in which we iteratively update the link-weight matrix by repeatedly refining its modal structure. Each iteration of the algorithm is a three-step process. First, we compute a link-weight matrix for each cluster by taking the outer-product of the vectors of current cluster-membership indicators for that cluster. Second, we extract the leading eigenvector from each modal link-weight matrix. Third, we compute a revised link weight matrix by taking the sum of the outer products of the leading eigenvectors of the modal link-weight matrices.     

A novel two-phase Hilbert-scan-based search algorithm for block motion estimation using CTF data structure

Motion estimation is one of the kernel issues in MPEG series. In this correspondence, a novel two-phase Hilbert-scan-based search algorithm for block motion estimation is presented. First in the intra-phase, a segmentation of the Hilbert curve is applied to the current block, then a novel coarse-to-fine data structure is developed to eliminate the impossible reference blocks in the search window of the reference frame. In the inter-phase, a new prediction scheme for estimating the initial motion vector of the current block is presented. Experimental results reveal that when compared to the GAPD algorithm, our proposed algorithm has better execution time and estimation accuracy performance. Under the same estimation accuracy, our proposed algorithm has better execution time performance when compared to the FS algorithm. In addition, when comparing with the TSS algorithm, our proposed algorithm has better estimation accuracy performance, but has worse execution time performance.     

Connectivity oriented fast Hough transform for tool wear monitoring

Tool wear monitoring can be achieved by analyzing the texture of machined surfaces. In this paper, we present the new connectivity oriented fast Hough transform, which easily detects all line segments in binary edge images of textures of machined surfaces. The features extracted from line segments are found to be highly correlated to the level of tool wear. A multilayer perceptron neural network is applied to estimate the flank wear in various machining processes. Our experiments show that this Hough transform based approach is effective in analyzing the quality of machined surfaces and could be used to monitor tool wear. A performance analysis of our Hough transform is also provided.     

Learning switching dynamic models for objects tracking

Many recent tracking algorithms rely on model learning methods. A promising approach consists of modeling the object motion with switching autoregressive models. This article is involved with parametric switching dynamical models governed by an hidden Markov Chain. The maximum likelihood estimation of the parameters of those models is described. The formulas of the EM algorithm are detailed. Moreover, the problem of choosing a good and parsimonious model with BIC criterion is considered. Emphasis is put on choosing a reasonable number of hidden states. Numerical experiments on both simulated and real data sets highlight the ability of this approach to describe properly object motions with sudden changes. The two applications on real data concern object and heart tracking.     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.     

Object detection using feature subset selection

Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA). Without using an effective scheme to select an appropriate set of features in this space, however, these methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper, we argue that feature selection is an important problem in object detection and demonstrate that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. As a case study, we have considered PCA for feature extraction and support vector machines (SVMs) for classification. The goal is searching the PCA space using GAs to select a subset of eigenvectors encoding important information about the target concept of interest. This is in contrast to traditional methods selecting some percentage of the top eigenvectors to represent the target concept, independently of the classification task. We have tested the proposed framework on two challenging applications: vehicle detection and face detection. Our experimental results illustrate significant performance improvements in both cases.     

Decentralised production-distribution planning system using collaborative agents in supply chain network

With the fast development of information technology and the global market, collaboration between different functional units in a supply chain becomes one of the most critical factors for global companies to respond to rapid changes in customer needs and increase the efficiency of the whole supply chain. This supply chain environment requires a production-distribution planning system to enable the collaboration between production and distribution units more quickly and orderly. Generally, collaboration in a supply chain needs to resolve conflicts between two decentralised functional units, because each unit tries to locally minimise its own costs, not the overall supply chain costs. Also, there exists incomplete information sharing according to the information privacy of each functional unit. This paper proposes a decentralised production-distribution planning system using collaborative agents that can efficiently mediate the planning gaps between two decentralised planning functions: production and distribution. The validity of collaborative-agent model has been shown using some lemmas and numerical examples.     

Numerical analysis of welding residual stress using heat source models for the multi-pass weldment

Numerical prediction of welding-induced residual stresses using the finite element method has been a common practice in the development or refinement of welded product designs. Various researchers have studied several thermal models associated with the welding process. Among these thermal models, ramp heat input and double-ellipsoid moving source have been investigated. These heat-source models predict the temperature fields and history with or without accuracy. However, these models can predict the thermal characteristics of the welding process that influence the formation of the inherent plastic strains, which ultimately determines the final state of residual stresses in the weldment. The magnitude and distribution of residual stresses are compared. Although the two models predict similar magnitude of the longitudinal stress, the double-ellipsoid moving source model predicts wider tensile stress zones than the other one. And, both the ramp heating and moving source models predict the stress results in reasonable agreement with the experimental data.