Analysis of extracted fingerprint texture features

This paper describes an approach for electronic feature extraction from fingerprint images for identification purposes. Statistical analysis of the features extracted from the fingerprint provides the advantage of real-time analysis of the fingerprint information, providing a method to remove unwanted information prior to the matching stage required in electronic fingerprint verification. This method can also detect of noise within the fingerprint image and location of critical data in the fingerprint. In order to demonstrate the effectiveness of this work, experiments have been developed and conducted with the results and analysis provided at the end of this paper for discussion.

The power of play-break for automatic detection and browsing of self-consumable sport video highlights

To enable content-based retrieval, highlights extraction from broadcasted sport video has been an active research topic in the last decade. There is a well-known theory that high-level semantic, such as goal in soccer can be detected based on the occurrences of specific audio and visual features that can be extracted automatically. However, there is yet a definitive solution for the scope (i.e. start and end) of the detection for self consumable highlights. Thus, in this paper we will primarily demonstrate the benefits of using play-break for this purpose. Moreover, we also propose a browsing scheme that is based on integrated play-break and highlights (extended from [1]). To validate our approach, we will present the results from some experiments and a user study.

Fingerprint recognition system using hybrid matching techniques

With an increasing emphasis on the emerging automatic person identification application, biometrics based, especially fingerprint-based identification, is receiving a lot of attention. This research developed an automatic fingerprint recognition system (AFRS) based on a hybrid between minutiae and correlation based techniques to represent and to match fingerprint; it improved each technique individually. It was noticed that, in the hybrid approach, as a result of an improvement of minutiae extraction algorithm in post-process phase that combines the two algorithms, the performance of the minutia algorithm improved. An improvement in the ridge algorithm that used centre point in fingerprint instead of reference point was also observed. Experiments indicate that the hybrid technique performs much better than each algorithm individually.

An authorization policy management framework for dynamic medical data sharing

In this paper, we propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.

An image retrieval system based on fractal dimension

This paper presents a new kind of image retrieval system which obtains the feature vectors of images by estimating their fractal dimension; and at the same time establishes a tree-structure image database. After preprocessing and feature extracting, a given image is matched with the standard images in the image database using a hierarchical method of image indexing.

Supporting knowledge management infrastructure : a multi-agent approach

Knowledge management has by now exerted a large influence over industrial practice. We propose a multi-agent based system to provide infrastructure service to meet various requirements of knowledge management in view of the complex nature of the task, i.e., covering science and technologies across different disciplines. The paper discusses the use of a multi-agent system to support the knowledge management infrastructure. It will also discuss the design rational and implementation details as well as an application to the problem of feature extraction in weather prediction.

Impact of image organizations on multimedia document retrieval

In this paper we compare ranking effectiveness of heterogeneous multimedia document retrieval when different image organizations are used for formulating queries. The quality of image queries depends on the organization of images used to make queries which in turn significantly impacts retrieval precision. CBIR (content based information retrieval) needs an effective and efficient organization of images including user interface which must be part of the configuration parameters of image retrieval research.

Image processing and pattern recognition in textiles

Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into / with artificial neural networks, the image processing technique has provided a useful tool to solve complex problems in textile technology. Three different approaches are used in this work forfiber classification and pattern recognition: feature extraction with image process, pattern recognition and classification with artificial neural networks, and feature recognition and classification with artificial neural network. All of them yieldssatisfactory results by giving a high level of accuracy in classification.

A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

Extracting fingerprint features using textures

Personal identification of individuals is becoming increasingly adopted in society today. Due to the large number of electronic systems that require human identification, faster and more secure identification systems are pursued. Biometrics is based upon the physical characteristics of individuals; of these the fingerprint is the most common as used within law enforcement. Fingerprint-based systems have been introduced into the society but have not been well received due to relatively high rejection rates and false acceptance rates. This limited acceptance of fingerprint identification systems requires new techniques to be investigated to improve this identification method and the acceptance of the technology within society. Electronic fingerprint identification provides a method of identifying an individual within seconds quickly and easily. The fingerprint must be captured instantly to allow the system to identify the individual without any technical user interaction to simplify system operation. The performance of the entire system relies heavily on the quality of the original fingerprint image that is captured digitally. A single fingerprint scan for verification makes it easier for users accessing the system as it replaces the need to remember passwords or authorisation codes. The identification system comprises of several components to perform this function, which includes a fingerprint sensor, processor, feature extraction and verification algorithms. A compact texture feature extraction method will be implemented within an embedded microprocessor-based system for security, performance and cost effective production over currently available commercial fingerprint identification systems. To perform these functions various software packages are available for developing programs for windows-based operating systems but must not constrain to a graphical user interface alone. MATLAB was the software package chosen for this thesis due to its strong mathematical library, data analysis and image analysis libraries and capability. MATLAB enables the complete fingerprint identification system to be developed and implemented within a PC environment and also to be exported at a later date directly to an embedded processing environment. The nucleus of the fingerprint identification system is the feature extraction approach presented in this thesis that uses global texture information unlike traditional local information in minutiae-based identification methods. Commercial solid-state sensors such as the type selected for use in this thesis have a limited contact area with the fingertip and therefore only sample a limited portion of the fingerprint. This limits the number of minutiae that can be extracted from the fingerprint and as such limits the number of common singular points between two impressions of the same fingerprint. The application of texture feature extraction will be tested using variety of fingerprint images to determine the most appropriate format for use within the embedded system. This thesis has focused on designing a fingerprint-based identification system that is highly expandable using the MATLAB environment. The main components that are defined within this thesis are the hardware design, image capture, image processing and feature extraction methods. Selection of the final system components for this electronic fingerprint identification system was determined by using specific criteria to yield the highest performance from an embedded processing environment. These platforms are very cost effective and will allow fingerprint-based identification technology to be implemented in more commercial products that can benefit from the security and simplicity of a fingerprint identification system.

Intelligent animal fibre identification and classification

To develop an objective and repeatable method of identification and classification of animal fibres, two different integrated systems were developed to mimic the human brain's ability to undertake feature extraction and discrimination of animal fibres. Both integrated systems are basically composed of an image processing system and an artificial neural network system.

Novel spectral descriptor for object shape

In this paper, we propose a novel descriptor for shapes. The proposed descriptor is obtained from 3D spherical harmonics. The inadequacy of 2D spherical harmonics is addressed and the method to obtain 3D spherical harmonics is described. 3D spherical harmonics requires construction of a 3D model which implicitly represents rich features of objects. Spherical harmonics are used to obtain descriptors from the 3D models. The performance of the proposed method is compared against the CSS approach which is the MPEG-7 descriptor for shape contour. MPEG-7 dataset of shape contours, namely, CE-1 is used to perform the experiments. It is shown that the proposed method is effective.

Spectral shape descriptor using spherical harmonics

In this paper, we propose a spectral descriptor for shapes of objects. The method relies on transforming the 2D objects into 3D space; distance transform and scale space theory is used to transform objects into 3D space. Spherical harmonics of the voxel grid are used to obtain shape descriptors. The proposed methods are compared against two existing methods which use spherical harmonics for shape based retrieval of images. Comparison is done based on ranking of images which is articulated in recall-precision curves. MPEG-7 Still Images Content Set is used for performing experiments. Experimental results show that the performance of the proposed descriptor is significantly better than other methods in the same category.

Vehicle detection and classification by measuring and processing magnetic signal

This paper presents novel vehicle detection and classification method by measuring and processing magnetic signal based on single micro-electro- mechanical system (MEMS) magnetic sensor. When a vehicle moves over the ground, it generates a succession of impacts on the earth's magnetic field, which can be detected by single magnetic sensor. The magnetic signal measured by the magnetic sensor is related to the moving direction and the type of the vehicle. Generally, the recognition rate using single sensor detector is not high. In order to improve the recognition rate, a novel feature extraction algorithm and a novel vehicle classification and recognition algorithm are presented. The concavity and convexity areas, and the angles of concave and convex parts of the waveform are extracted. An improved support vector machine (ISVM) classifier is developed to perform vehicle classification and recognition. The effectiveness of the proposed approach is verified by outdoor experiments.

A neural network based human identification framework using ear images

This paper presents a framework that uses ear images for human identification. The framework makes use of Principal Component Analysis (PCA) for ear image feature extraction and Multilayer Feed Forward Neural Network for classification. Framework are proposed to improve recognition accuracy of human identification. The framework was tested on an ear image database to evaluate its reliability and recognition accuracy. The experimental results showed that our framework achieved higher stable recognition accuracy and over-performed other existing methods. The recognition accuracy stability and computation time with respect to different image sizes and factors were investigated thoroughly as well in the experiments.