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Title: 2D/3D face recognition.
Creator: Guan, Xin., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the...
Show moreThis dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the simple PCA algorithm, this new approach can yield successful recognition rates using 2D probing images and 3D gallery images. The insight gained from the 2D/3D face recognition study was also extended to the case of involving 2D probing and 2D gallery images, which offers a more flexible approach since it is much easier and practical to acquire 2D photos for recognition. To test the effectiveness of the proposed approach, the public AT&T face database, which had 2D only face photos of 40 subjects, with 10 different images each, was utilized in the experimental study. The results from this investigation show that with our approach, the 3D recognition algorithm can be successfully applied to 2D only images. The performance of the proposed approach was further compared with some of the existing face recognition techniques. Studies on imperfect conditions such as domain and pose/illumination variations were also carried out. Additionally, the performance of the algorithms on noisy photos was evaluated. Pros and cons of the proposed face recognition technique along with suggestions for future studies are also given in the dissertation.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342104
Subject Headings: Pattern recognition systems, Optical pattern recognition, Biometric identification, Face perception, Artificial intellingence
Format: Document (PDF)

Title: Automated biometrics of audio-visual multiple modals.
Creator: Huang, Lin, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Biometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by...
Show moreBiometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by problems like noisy sensor data, intra-class variations, distinctiveness and non-universality. Applying multiple modal systems that consolidate evidence from multiple biometric modalities can alleviate those problems of single modal ones. Integration of evidence obtained from multiple cues, also known as fusion, is a critical part in multiple modal systems, and it may be consolidated at several levels like feature fusion level, matching score fusion level and decision fusion level. Among biometric modalities, both audio and face modalities are easy to use and generally acceptable by users. Furthermore, the increasing availability and the low cost of audio and visual instruments make it feasible to apply such Audio-Visual (AV) systems for security applications. Therefore, this dissertation proposes an algorithm of face recognition. In addition, it has developed some novel algorithms of fusion in different levels for multiple modal biometrics, which have been tested by a virtual database and proved to be more reliable and robust than systems that rely on a single modality.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/1927864
Subject Headings: Pattern recognition systems, Optical pattern recognition, Biometric identification, Identification, Automation, Automatic speech recognition
Format: Document (PDF)

Title: Event detection in surveillance video.
Creator: Castellanos Jimenez, Ricardo Augusto., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video...
Show moreDigital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video sequences. This thesis presents a surveillance system based on optical flow and background subtraction concepts to detect events based on a motion analysis, using an event probability zone definition. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction to a predefined condition or running in the scene, with precision greater than 50% and recall greater than 80%.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/1870694
Subject Headings: Computer systems, Security measures, Image processing, Digital techniques, Imaging systems, Mathematical models, Pattern recognition systems, Computer vision, Digital video
Format: Document (PDF)

Title: Generating narratives: a pattern language.
Creator: Greene, Samuel., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In order to facilitate the development, discussion, and advancement of the relatively new subfield of Artificial Intelligence focused on generating narrative content, the author has developed a pattern language for generating narratives, along with a new categorization framework for narrative generation systems. An emphasis and focus is placed on generating the Fabula of the story (the ordered sequence of events that make up the plot). Approaches to narrative generation are classified into...
Show moreIn order to facilitate the development, discussion, and advancement of the relatively new subfield of Artificial Intelligence focused on generating narrative content, the author has developed a pattern language for generating narratives, along with a new categorization framework for narrative generation systems. An emphasis and focus is placed on generating the Fabula of the story (the ordered sequence of events that make up the plot). Approaches to narrative generation are classified into one of three categories, and a pattern is presented for each approach. Enhancement patterns that can be used in conjunction with one of the core patterns are also identified. In total, nine patterns are identified - three core narratology patterns, four Fabula patterns, and two extension patterns. These patterns will be very useful to software architects designing a new generation of narrative generation systems.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3355559
Subject Headings: Computational intelligence, Pattern recognition systems, Computer vision, Artificial intelligence, Image processing, Digital techiques
Format: Document (PDF)

Title: Intelligent systems using GMDH algorithms.
Creator: Gupta, Mukul., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based...
Show moreDesign of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2976442
Subject Headings: GMDH algorithms, Genetic algorithms, Pattern recognition systems, Expert systems (Computer science), Neural networks (Computer science), Fuzzy logic, Intelligent control systems
Format: Document (PDF)

Title: Model-based classification of speech audio.
Creator: Thoman, Chris., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This work explores the process of model-based classification of speech audio signals using low-level feature vectors. The process of extracting low-level features from audio signals is described along with a discussion of established techniques for training and testing mixture model-based classifiers and using these models in conjunction with feature selection algorithms to select optimal feature subsets. The results of a number of classification experiments using a publicly available speech...
Show moreThis work explores the process of model-based classification of speech audio signals using low-level feature vectors. The process of extracting low-level features from audio signals is described along with a discussion of established techniques for training and testing mixture model-based classifiers and using these models in conjunction with feature selection algorithms to select optimal feature subsets. The results of a number of classification experiments using a publicly available speech database, the Berlin Database of Emotional Speech, are presented. This includes experiments in optimizing feature extraction parameters and comparing different feature selection results from over 700 candidate feature vectors for the tasks of classifying speaker gender, identity, and emotion. In the experiments, final classification accuracies of 99.5%, 98.0% and 79% were achieved for the gender, identity and emotion tasks respectively.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/210518
Subject Headings: Signal processing, Digital techniques, Speech processing systems, Sound, Recording and reproducing, Digital techniques, Pattern recognition systems
Format: Document (PDF)

Title: A novel NN paradigm for the prediction of hematocrit value during blood transfusion.
Creator: Thakkar, Jay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data...
Show moreDuring the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3174078
Subject Headings: Neural networks (Computer science), Scientific applications, GMDH algorithms, Pattern recognition systems, Genetic algorithms, Fuzzy logic
Format: Document (PDF)

Title: Object recognition on Android mobil platform using speeded up robust features.
Creator: Tyagi, Vivek K., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In recent years there has been great interest in implementing object recognition frame work on mobile phones. This has stemmed from the fact the advances in object recognition algorithm and mobile phone capabilities have built a congenial ecosystem. Application developers on mobile platforms are trying to utilize the object recognition technology to build better human computer interfaces. This approach is in the nascent phase and proper application framework is required. In this thesis, we...
Show moreIn recent years there has been great interest in implementing object recognition frame work on mobile phones. This has stemmed from the fact the advances in object recognition algorithm and mobile phone capabilities have built a congenial ecosystem. Application developers on mobile platforms are trying to utilize the object recognition technology to build better human computer interfaces. This approach is in the nascent phase and proper application framework is required. In this thesis, we propose a framework to overcome design challenges and provide an evaluation methodology to assess the system performance. We use the emerging Android mobile platform to implement and test the framework. We performed a case study using the proposal and reported the test result. This assessment will help developers make wise decisions about their application design. Furthermore, the Android API developers could use this information to provide better interfaces to the third party developers. The design and evaluation methodology could be extended to other mobile platforms for a wider consumer base.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2683531
Subject Headings: Mobile computing, Application software, Development, Object-oriented programming (Computer software), Pattern recognition systems, Development
Format: Document (PDF)

Title: Patterns for secure interactions in social networks in Web 2.0.
Creator: Marin, Carolina, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A social network is a structure of individuals and organizations, which are connected by one or more types of interdependency, such as friendship, affinity, common interests or knowledge. Social networks use Web 2.0 technology, which is mostly based on a service-oriented architecture. We are studying patterns for social networks in this environment. A pattern is an encapsulated solution to a software problem in a given context, secure threats are possible in this context. We present a...
Show moreA social network is a structure of individuals and organizations, which are connected by one or more types of interdependency, such as friendship, affinity, common interests or knowledge. Social networks use Web 2.0 technology, which is mostly based on a service-oriented architecture. We are studying patterns for social networks in this environment. A pattern is an encapsulated solution to a software problem in a given context, secure threats are possible in this context. We present a collection of patterns associated with the most important aspects of social networks, with emphasis on controlling the actions of the users of these networks.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342203
Subject Headings: Web 2.0, Computer network architectures, Online social networks, Security measures, Social media, Pattern recognition systems
Format: Document (PDF)

Title: Proud elastic target discrimination using low-frequency sonar signatures.
Creator: Mallen, Brenton., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: This thesis presents a comparative analysis of various low-frequency sonar signature representations and their ability to discriminate between proud targets of varying physical parameters. The signature representations used include: synthetic aperture sonar (SAS) beamformed images, acoustic color plot images, and bispectral images. A relative Mean-Square Error (rMSE) performance metric and an effective Signal-to-Noise Ratio (SNReff) performance metric have been developed and implemented to...
Show moreThis thesis presents a comparative analysis of various low-frequency sonar signature representations and their ability to discriminate between proud targets of varying physical parameters. The signature representations used include: synthetic aperture sonar (SAS) beamformed images, acoustic color plot images, and bispectral images. A relative Mean-Square Error (rMSE) performance metric and an effective Signal-to-Noise Ratio (SNReff) performance metric have been developed and implemented to quantify the target differentiation. The analysis is performed on a subset of the synthetic sonar stave data provided by the Naval Surface Warfare Center - Panama City Division (NSWC-PCD). The subset is limited to aluminum and stainless steel, thin-shell, spherical targets in contact with the seafloor (proud). It is determined that the SAS signature representation provides the best, least ambiguous, target differentiation with a minimum mismatch difference of 14.5802 dB. The acoustic color plot and bispectrum representations resulted in a minimum difference of 9.1139 dB and 1.8829 dB, respectively
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342210
Subject Headings: Pattern recognition systems, Frequency response (Dynamics), Signal theory (Telecommunication), Random noise theory
Format: Document (PDF)

Title: Signature system for video identification.
Creator: Medellin, Sebastian Possos., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Video signature techniques based on tomography images address the problem of video identification. This method relies on temporal segmentation and sampling strategies to build and determine the unique elements that will form the signature. In this thesis an extension for these methods is presented; first a new feature extraction method, derived from the previously proposed sampling pattern, is implemented and tested, resulting in a highly distinctive set of signature elements, second a robust...
Show moreVideo signature techniques based on tomography images address the problem of video identification. This method relies on temporal segmentation and sampling strategies to build and determine the unique elements that will form the signature. In this thesis an extension for these methods is presented; first a new feature extraction method, derived from the previously proposed sampling pattern, is implemented and tested, resulting in a highly distinctive set of signature elements, second a robust temporal video segmentation system is used to replace the original method applied to determine shot changes more accurately. Under a very exhaustive set of tests the system was able to achieve 99.58% of recall, 100% of precision and 99.35% of prediction precision.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2683534
Subject Headings: Biometric identification, Image processing, Digital techniques, Pattern recognition systems, Data encryption (Computer science)
Format: Document (PDF)

Biometric identification (3)	+ -
Digital techniques (3)	+ -
Image processing (3)	+ -
Computer vision (2)	+ -
Fuzzy logic (2)	+ -

GMDH algorithms (2)	+ -
Genetic algorithms (2)	+ -
Neural networks (Computer science) (2)	+ -
Optical pattern recognition (2)	+ -
Security measures (2)	+ -
Application software (1)	+ -
Artificial intelligence (1)	+ -
Artificial intellingence (1)	+ -
Automatic speech recognition (1)	+ -
Automation (1)	+ -
Computational intelligence (1)	+ -
Computer network architectures (1)	+ -
Computer systems (1)	+ -
Data encryption (Computer science) (1)	+ -
Development (1)	+ -
Digital techiques (1)	+ -
Digital video (1)	+ -
Expert systems (Computer science) (1)	+ -
Face perception (1)	+ -
Frequency response (Dynamics) (1)	+ -
Identification (1)	+ -
Imaging systems (1)	+ -
Intelligent control systems (1)	+ -
Mathematical models (1)	+ -

Department of Computer and Electrical Engineering and Computer Science (10)	+ -
Castellanos Jimenez, Ricardo Augusto. (1)	+ -
Department of Ocean and Mechanical Engineering (1)	+ -
Greene, Samuel. (1)	+ -
Guan, Xin. (1)	+ -

Gupta, Mukul. (1)	+ -
Huang, Lin (1)	+ -
Mallen, Brenton. (1)	+ -
Marin, Carolina (1)	+ -
Medellin, Sebastian Possos. (1)	+ -
Thakkar, Jay. (1)	+ -
Thoman, Chris. (1)	+ -
Tyagi, Vivek K. (1)	+ -

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