Pattern Recognition

This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper ...

Discrete classification problems abound in pattern recognition and data mining applications. One of the most common discrete rules is the discrete histogram rule. This paper presents exact formulas for the computation of bias, variance, and RMS of the ...

Hybrid wavelet-large margin classifiers have recently proven to solve difficult signal classification problems in cases where solely using a large margin classifier like, e.g., the Support Vector Machine may fail. In this paper, we evaluate several ...

We propose a method for characterizing spatial region data. The method efficiently constructs a k-dimensional feature vector using concentric spheres in 3D (circles in 2D) radiating out of a region's center of mass. These signatures capture structural ...

A novel successive learning algorithm based on a Test Feature Classifier is proposed for efficient handling of sequentially provided training data. The fundamental characteristics of the successive learning are considered. In the learning, after ...

Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this ...

Assessment of clustering tendency is an important first step in cluster analysis. One tool for assessing cluster tendency is the Visual Assessment of Tendency (VAT) algorithm. VAT produces an image matrix that can be used for visual assessment of ...

In this paper we propose a clustering algorithm to cluster data with arbitrary shapes without knowing the number of clusters in advance. The proposed algorithm is a two-stage algorithm. In the first stage, a neural network incorporated with an ART-like ...

This paper is devoted to the techniques of clustering of texts based on the comparison of vocabularies of N-grams. In contrast to the regular N-grams approach, the proposed N-grams method is based on calculation of imperfect occurrences of N-grams in a ...

Hierarchical clustering is the grouping of objects of interest according to their similarity into a hierarchy, with different levels reflecting the degree of inter-object resemblance. It is an important area in data analysis and pattern recognition. In ...

In this article, a minimum description length (MDL) criterion adapted to independent multi-component image segmentation into homogeneous regions is proposed. This approach, based on a deformable polygonal grid, allows us to segment noisy multi-component ...

This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the ...

Active contours or snakes have been extensively utilized in handling image segmentation and classification problems. In traditional active contour models, snake initialization is performed manually by users, and topological changes, such as splitting of ...

This work proposes a novel adaptive approach for character segmentation and feature vector extraction from seriously degraded images. An algorithm based on the histogram automatically detects fragments and merges these fragments before segmenting the ...

The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this ...

The purpose of data hiding is to embed relating textural description into an image. The textural description and original host image can be extracted and reconstructed from the stego-image in the data extraction process. However, the reconstructed host ...

Hidden annotation (HA) is an important research issue in content-based image retrieval (CBIR). We propose to incorporate long-term relevance feedback (LRF) with HA to increase both efficiency and retrieval accuracy of CBIR systems. The work contains two ...

We present a factorial representation of Gaussian mixture models for observation densities in hidden Markov models (HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized ...

This paper focuses on directional texture analysis. We propose a new approach for orientation estimation. This approach hinges on two classes of convolution masks, i.e. the gradient and the valleyness operators. We provide a framework for their ...

Many problems in information processing involve some form of dimensionality reduction, such as face recognition, image/text retrieval, data visualization, etc. The typical linear dimensionality reduction algorithms include principal component analysis (...

The emerging field of bioinformatics has recently created much interest in the computer science and engineering communities. With the wealth of sequence data in many public online databases and the huge amount of data generated from the Human Genome ...

We discuss the realization of a fast, robust and accurate pattern matching algorithm for comparison of digital images implemented by discrete Circular Harmonic expansions based on sampling theory. The algorithm and its performance for re-assembling ...

Stack filters are operators that commute with the thresholding operation, i.e., thresholding a signal, applying the binary filter on each thresholded binary signals, and then summing up (stacking) the results yields the same result as applying the multi-...

A method for segmentation and recognition of image structures based on graph homomorphisms is presented in this paper. It is a model-based recognition method where the input image is over-segmented and the obtained regions are represented by an ...

This paper presents a method for determining the up/down orientation of text in a scanned document of unknown orientation, so that it can be appropriately rotated and processed by an optical character recognition (OCR) engine. The method analyzes the ''...

This paper presents a system for assisting in human identification using dental radiographs. The goal of the system is to archive antemortem (AM) dental images and enable content-based retrieval of AM images that have similar teeth shapes to a given ...

Traditional image based hand tracking algorithms use a single model Kalman filter to estimate and predict the hand state (position, velocity, and acceleration) and do not consider multiple measurements with noise and false alarms. However, these ...

In this paper, a feature space trajectory (FST) classifier is applied to identify an unknown radar target. To improve the identification accuracy, we make use of information at multiple aspects of a radar target, and the FST classifier is combined with ...

Data fusion and decision fusion classification strategies are introduced to predict dyslexia from multi-channel event related potentials (ERPs) recorded, at birth, in response to multiple stimuli. Two data and two decision fusion strategies are ...