13452585
Robust Clustering based on Winner-Population Markov Chain
In this paper, we propose an unsupervised genetic clustering algorithm, which produces a new chromosome without any conventional genetic operators, and instead according to the gene reproducing probabilities determined by Markov chain modeling. Selection of cluster centers from the dataset enables construction of a look-up table that saves the distances between all pairs of data points. The experimental results show that the proposed algorithm not only solves the premature problem to provide a more stable clustering performance in terms of number of clusters and clustering results, but also improves the time efficiency
31996759
Analysis and Design of Parallel Thinning Algorithms - A Generic Approach
In this paper, a generic approach for the analysis and design of parallel thinning algorithms is presented. The procedures for designing 4-subcycle/iteration, 2-subcycle/iteration, and 1-subcycle/iteration thinning algorithms are developed. The experimental results show that these thinning algorithms designed by proposed approach preserve image connectivity, produce thinner results, and obtain faster speed than many existing key thinning algorithms. As an example, a 1-subcycle/iteration algorithm and its experimental results were illustrated, which is in general about two times faster than others in the literature.
2857808
A knowledge-based segmentation algorithm for enhanced recognition of handwritten courtesy amounts
Abstract A knowledge-based segmentation algorithm to enhance recognition of courtesy amounts on bank checks is proposed in this paper. This algorithm uses multiple contextual cues to enhance segmentation and recognition. The system described extracts context from the handwritten numerals and uses a syntax parser based on a deterministic finite automaton to provide adequate feedback to enhance recognition. Further feedback is provided by a simple legal amount decoder that determines word count and recognizes several key words (e.g. thousand and hundred). This provides an additional semantic constraint on the dollar section. The segmentation analysis module presented is capable of handling a number of commonly used styles for courtesy amount representation. Both handwritten and machine written courtesy and legal amounts were utilized to test the efficacy of the preprocessor for the check recognition system described in this paper. The substitution error was reduced by 30–40% depending on the input check mix.
207116582
A Thinning Algorithm Based on the Force Between Charged Particles
A new thinning algorithm based on the well known concept of the force of attraction or repulsion between charged particles is presented. This algorithm generates connected skeletons which preserve the shape and end-points of the original patterns. Its performance is experimentally compared with four other known algorithms published in the literature. For the sake of comparison, a reasoned set of test data is introduced. The results of our comparison reveal that the proposed CPM (Charge Particle Method) algorithm is almost as fast as the fastest of those compared. For thin images obtainable from low resolution scanners or coarse scanned images, the CPM algorithm is the fastest.
38659056
Intelligent Pattern Recognition and Applications
16442037
Handwritten Bank Check Recognition Of Courtesy Amounts
In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates.
41563247
Analytical design of digital nonrecursive maximally flat fractional Hilbert transformer
Recently, a generalization of the Hilbert transformer, the fractional Hilbert transformer, was defined and developed. In this paper, we propose a design of the FIR filter to realize the fractional Hilbert transformer based on the maximally flat approach. The impulse response is obtained explicitly. An efficient realization structure is constructed according to some properties of the impulse response.
487167
On Self-organising Diagnostics in Impact Sensing Networks
Structural health management (SHM) of safety-critical structures requires multiple capabilities: sensing, assessment, diagnostics, prognostics, repair, etc. This paper presents a capability for self-organising diagnosis by a group of autonomous sensing agents in a distributed sensing and processing SHM network. The diagnostics involves acoustic emission waves emitted as a result of a sudden release of energy during impacts and detected by the multi-agent network. Several diagnostic techniques identifying the nature and severity of damage at multiple sites are investigated, and the self-organising maps (Kohonen neural networks) are shown to outperform the standard k-means algorithm in both time- and frequency domains.
17885591
Towards Adaptive Clustering in Self-monitoring Multi-agent Networks
A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.
2424601
Users' interaction with World Wide Web resources: an exploratory study using a holistic approach
Abstract This paper presents the first part of the research on user–Web interaction: a multidimensional model, methodology, and general findings. The objectives of this study are three-fold: (1) to explore factors of user–Web interaction in finding factual information and what happens during this interaction; (2) to develop a conceptual framework for studying user–Web interaction; and (3) to apply a process-tracing method for conducting holistic user–Web studies. The proposed model consists of three components: user, interface, and the World Wide Web. User–Web interaction is viewed as a communication process facilitated through an interface. A process-tracing technique has been designed to capture the processes of user-Web interactions. Twenty-four graduate students participated in this study. Prior to the interaction, each participant was given a questionnaire to report his/her computer and Web experience, the State Trait Anxiety Inventory (form Y1 and form Y2) to measure affective states, and an individually administered Embedded Figure Test to measure cognitive style. Each participant used the Web to find answers to two factual questions. Both the processes (continuous screen shots) and the concurrent verbalizations of thoughts were recorded in synchronized video–audio data. The findings provided rich information on users’ cognitive, affective and physical behaviors. The proposed model is used to present the findings of user behavior in connections with interfaces and the Web.
6309219
Contextual browsing for highlights in sports video
A novel highlight browsing approach for sports videos, based on contextual cue categorization, is proposed in this paper. Contextual cues are those salient audio and visual cues around highlight segments, and they are also informative and attractive to audiences. With further analysis and categorization of these contextual cues, the user could access highlights more flexibly and effectively. An experimental system is built for broadcasting diving videos in our current work, in which contextual cues such as slow-motion replays and game statistics information are extracted and categorized for highlight browsing. An efficient algorithm for slow-motion replay detection and categorization in diving videos is also proposed. Experimental results on 2 hours broadcasting diving videos have demonstrated the effectiveness and efficiency of the proposed approach.