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Fuzzy relational classifier (FRC) is the recently proposed two-step nonlinear classifiers, which effectively integrates the formed clusters and the given classes. However, FRC can not copy with the influence of those irrelevant or redundant features. To effectively filter out those irrelevant features and preserve the internal structure hidden in the given data, in this paper, a simultaneous clustering...
This paper describes the detailed process required to accomplish the printed Yi optical character recognition, including pretreatment, feature extraction and pattern classification. The arithmetic of Yi multifontpsilas feature extraction, the arithmetic of recognition required for dictionary building and the arithmetic of multilevel Yi character matching were analyzed with particular emphases. An...
A novel image perceptual hashing algorithm is proposed on the intermediate hashing stage. It first uses an iterative geometric technique to extract significant geometry preserving feature points. Then, the fuzzy distance matching method is proposed based on the observation that the distances of feature points are invariant in the polar coordinate under arbitrary rotation. It is verified that the proposed...
This paper proposes a kind of feature extraction based efficient registration algorithm including coarse and fine registrations. During coarse registration, feature points are detected, and feature point links are set up according to the relationship establishment among all detected feature points. Iterative closest point (ICP) is used in fine registration to align view pairs after the coarse registration...
Time series data generation has exploded in almost every domain such as in business, industry, or medicine. The demand for analyzing efficiently the huge amount of this information necessitates the application of a representation on the purpose of reducing the intrinsically high dimensionality of time series. In this paper we introduce DPAA, a new representation that can be considered as a variation...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
The reconstruction of 3-D solids from 2-D projections is an important research in reverse engineering. In this paper, a new method based on features for automatic reconstruction is proposed. The main features of the algorithm are to improve the speed of the reconstruction process. A 3D object is considered to be composed of one, or more than one part. The idea of the algorithm is to extract closed...
The paper presents an evaluation of four clustering algorithms: k-means, average linkage, complete linkage, and Wardpsilas method, with the latter three being different hierarchical methods. The quality of the clusters created by the algorithms was measured in terms of cluster cohesiveness and semantic cohesiveness, and both quantitative and predicate-based similarity criteria were considered.Two...
A new algorithm, Laplacian minmax discriminant projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a linear transformation which is an extension of linear discriminant analysis (LDA). Specifically, we define the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample space. After...
The purpose of this article is to present a novel algorithm for ship wake detection in synthetic aperture radar (SAR) images. The main originality of our work is that splitting the image with small window before conventional Radon transform to make the illumination has stronger consistency in each window and adopting clustering algorithm to select real wakes form disturbing lines. Experimental result...
Content based video indexing and retrieval traces back to the elementary video structures, such as a table of contents. Thus, algorithms for video partitioning have become crucial with the unremitting growth in the prevalent digital video technology. This demands for a tool which would break down the video into smaller and manageable units called shots. In this paper, a shot boundary detection technique...
A new knowledge mining framework based on multivariate analyses is proposed to discover and simulate the school grading policy. The framework comprises three major steps. Firstly, factor analysis is adopted to separate the scores of several different subjects into grading-related ones and grading-unrelated ones. Secondly, multidimensional scaling is employed for dimensionality reduction to facilitate...
Reliable shape modeling and clustering of white matter fiber tracts is essential for clinical and anatomical studies that use diffusion tensor imaging (DTI) tractography techniques. In this work we present a novel scheme to model the shape of white matter fiber tracts reconstructed from DTI and cluster them into bundles using Fourier descriptors. We characterize a tract's shape by using Fourier descriptors...
Protein structure similarity and classification methods have many applications in protein function prediction and associated fields (e.g. drug discovery). In this paper, we propose a new protein structure representation method enabling fast and accurate classification. In our approach, each protein structure is represented by number of vectors (based on histogram of distances) equivalent to the number...
In this paper we present a socially interactive multi-modal robotic head, ERWIN - Emotional Robot With Intelligent Networks, capable of emotion expression and interaction via speech and vision. The model presented shows how a robot can learn to attend to the voice of a specific speaker, providing a relevant emotional expressive response based on previous interactions. We show three aspects of the...
Mining images means extracting patterns and derive knowledge from large collections of images. Image mining follows image feature gathering, learning and retrieving procedures. This paper apprises as to what extent the users of the self organizing maps(SOM) techniques are satisfied with its efficiency of visualizing and organizing large amounts of image data. The main contribution of the paper consists...
This paper describes a an SLAM algorithm for the navigation for an indoor autonomous mobile robot. The main emphasis of this paper is on the ability of line extraction. A recognition method based on straight line extraction is proposed for extracting the key features on the office ceiling, in an effort to estimate the pose of mobile robot. Random sample consensus (RANSAC) paradigm is used to group...
Web is one of the most popular internet services in today's world. In today's world, web servers and web based applications are the popular corporate applications and become the targets of the attackers. A Large number of Web applications, especially those deployed for companies to e-business operation involve high reliability, efficiency and confidentiality. Such applications are written in script...
Recently, many researchers are actively studying on the reconstruction of 3-dimensional structures from the 2-dimensional images acquired from a stereo camera. However, the disparity map calculated from the existing methods does not always provide accurate depth information by the false matching and occluded areas. In this paper, by simultaneously applying the feature-based methods [1] and region-based...
Individual Chinese characters are identified mainly by their skeleton structure instead of texture or color. In this paper, an approach based on skeleton similarity for Chinese calligraphic characters retrieval is proposed. By this approach, first, the skeleton of the binarized individual characters are acquired by an improved multi-level module analysis algorithm. Second,the first round of skeleton...
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