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Title: An Attention-Driven Model for Grouping Similar Images with Image Retrieval Applications.
Creator: Marques, Oge, Mayron, Liam M, Borba, Gustavo B, Gamba, Humberto R
Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FAUIR000171
Format: Citation

Title: A Study in Implementing Autonomous Video Surveillance Systems Based on Optical Flow Concept.
Creator: Fonseca, Alvaro A., Zhuang, Hanqi, Marques, Oge, Florida Atlantic University
Abstract/Description: Autonomous video surveillance systems are usually built with several functional blocks such as motion detection, foreground and background separation, object tracking, depth estimation, feature extraction and behavioral analysis of tracked objects. Each of those blocks is usually designed with different techniques and algorithms, which may need significant computational and hardware resources. In this thesis we present a surveillance system based on an optical flow concept, as a main unit on...
Show moreAutonomous video surveillance systems are usually built with several functional blocks such as motion detection, foreground and background separation, object tracking, depth estimation, feature extraction and behavioral analysis of tracked objects. Each of those blocks is usually designed with different techniques and algorithms, which may need significant computational and hardware resources. In this thesis we present a surveillance system based on an optical flow concept, as a main unit on which other functional blocks depend. Optical flow limitations, capabilities and possible problem solutions are discussed in this thesis. Moreover, performance evaluation of various methods in handling occlusions, rigid and non-rigid object classification, segmentation and tracking is provided for a variety of video sequences under different ambient conditions. Finally, processing time is measured with software that shows an optical flow hardware block can improve system performance and increase scalability while reducing the processing time by more than fifty percent.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012516
Subject Headings: Electronic surveillance, Optical pattern recognition, Computer vision, Optical flow--Image analysis
Format: Document (PDF)

Title: Web-based academic advising system.
Creator: Ding, Xundong., Florida Atlantic University, Hsu, Sam, Marques, Oge
Abstract/Description: Academic advising is an important and time-consuming task and different tools and techniques can be used to make it an effective and efficient process. This thesis describes the design and development of a Web-based advising system that supplements the conventional advising process. The goals of the system include: to minimize repetitive tasks performed by advisors, to encourage students to adopt a proactive attitude towards advising, to make advising-related information available to remote...
Show moreAcademic advising is an important and time-consuming task and different tools and techniques can be used to make it an effective and efficient process. This thesis describes the design and development of a Web-based advising system that supplements the conventional advising process. The goals of the system include: to minimize repetitive tasks performed by advisors, to encourage students to adopt a proactive attitude towards advising, to make advising-related information available to remote students in a single place, in electronic format, and to minimize inconsistencies in the advising process. The system supports three different types of users (students, advisors, and secretaries). This thesis proposes a new Web-based advising system model. It also presents its architecture and an implementation of a prototype. Web-based advising system introduces a new approach towards advising over the Internet. Lessons learned from various experiments of the prototype are discussed in this thesis.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12880
Subject Headings: Counseling in higher education--Computer network resources, World Wide Web, Faculty advisors--Computer network resources
Format: Document (PDF)

Title: A method for adding multimedia knowledge for improving intrusion detection systems.
Creator: Baillargeon, Pierre Elliott., Florida Atlantic University, Marques, Oge
Abstract/Description: Intrusion Detection Systems (IDS) are security tools which monitor systems and networks for malicious activity. In saturated network links the amount of data present for analysis can overwhelm them, resulting in potentially undetected attacks. Many of these network links contain significant amounts of multimedia traffic which may seem to contribute to the problem, however our work suggests otherwise. This thesis proposes a novel method to classify and analyze multimedia traffic in an effort...
Show moreIntrusion Detection Systems (IDS) are security tools which monitor systems and networks for malicious activity. In saturated network links the amount of data present for analysis can overwhelm them, resulting in potentially undetected attacks. Many of these network links contain significant amounts of multimedia traffic which may seem to contribute to the problem, however our work suggests otherwise. This thesis proposes a novel method to classify and analyze multimedia traffic in an effort to maximize the efficiency of IDS. By embedding multimedia-specific knowledge into IDS, trusted multimedia contents can be identified and allowed to bypass the detection engine, thereby allowing IDS to focus its limited resources on other traffic. The proposed framework also enables IDS to detect multimedia-specific exploits which would otherwise pass under the radar. Results of our experiments confirm our claims and show substantial CPU savings in both streaming and non-streaming scenarios.
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Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/13242
Subject Headings: Computer networks--Security measures, Computers--Access control, Electronic countermeasures, Digital watermarking, Multimedia systems--Security measures
Format: Document (PDF)

Title: A hybrid color‐based foreground object detection method for automated marine surveillance.
Creator: Furht, Borko, Kalva, Hari, Marques, Oge, Culibrk, Dubravko, Socek, Daniel
Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/358420
Subject Headings: Computer vision., Automatic tracking., Digital video., Image processing., Optical pattern recognition.
Format: Document (PDF)

Title: New approaches to encryption and steganography for digital videos.
Creator: Furht, Borko, Socek, Daniel, Kalva, Hari, Magliveras, Spyros S., Marques, Oge, Culibrk, Dubravko
Date Issued: 2007
PURL: http://purl.flvc.org/fcla/dt/337435
Subject Headings: Multimedia systems --Security measures., Digital video., Digital watermarking., Data encryption (Computer science) --Technological innovations., Cryptography.
Format: Document (PDF)

Title: An Intelligent Method For Violence Detection in Live Video Feeds.
Creator: Eneim, Maryam, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection...
Show moreIn the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection of live video, in addition to prerecorded video. The results for violence detection in prerecorded videos are comparable to other popular detection systems and the results for live video are also very encouraging, making the work proposed in this thesis a solid foundation for improved live violence detection systems.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004775, http://purl.flvc.org/fau/fd/FA00004775
Subject Headings: Multimedia systems., Image analysis., Computer vision., Visual communication--Social aspects., Social problems--21st century., Pattern recognition systems., Optical pattern recognition.
Format: Document (PDF)

Title: Face Processing Using Mobile Devices.
Creator: James, Jhanon, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Image Processing and Computer Vision solutions have become commodities for software developers, thanks to the growing availability of Application Program- ming Interfaces (APIs) that encapsulate rich functionality, powered by advanced al- gorithms. To understand and create an e cient method to process faces in images by computers, one must understand how the human visual system processes them. Face processing by computers has been an active research area for about 50 years now. Face detection...
Show moreImage Processing and Computer Vision solutions have become commodities for software developers, thanks to the growing availability of Application Program- ming Interfaces (APIs) that encapsulate rich functionality, powered by advanced al- gorithms. To understand and create an e cient method to process faces in images by computers, one must understand how the human visual system processes them. Face processing by computers has been an active research area for about 50 years now. Face detection has become a commodity and is now incorporated into simple devices such as digital cameras and smartphones. An iOS app was implemented in Objective-C using Microsoft Cognitive Ser- vices APIs, as a tool for human vision and face processing research. Experimental work on image compression, upside-down orientation, the Thatcher e ect, negative inversion, high frequency, facial artifacts, caricatures and image degradation were completed on the Radboud and 10k US Adult Faces Databases along with other images.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004770, http://purl.flvc.org/fau/fd/FA00004770
Subject Headings: Image processing--Digital techniques., Mobile communication systems., Mobile computing., Artificial intelligence., Human face recognition (Computer science), Computer vision., Optical pattern recognition.
Format: Document (PDF)

Title: Design and implementation of driver drowsiness detection system.
Creator: Colic, Aleksandar, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: There is a substantial amount of evidence that suggests that driver drowsiness plays a significant role in road accidents. Alarming recent statistics are raising the interest in equipping vehicles with driver drowsiness detection systems. This dissertation describes the design and implementation of a driver drowsiness detection system that is based on the analysis of visual input consisting of the driver's face and eyes. The resulting system combines off-the-shelf software components for face...
Show moreThere is a substantial amount of evidence that suggests that driver drowsiness plays a significant role in road accidents. Alarming recent statistics are raising the interest in equipping vehicles with driver drowsiness detection systems. This dissertation describes the design and implementation of a driver drowsiness detection system that is based on the analysis of visual input consisting of the driver's face and eyes. The resulting system combines off-the-shelf software components for face detection, human skin color detection and eye state classification in a novel way. It follows a behavioral methodology by performing a non-invasive monitoring of external cues describing a driver's level of drowsiness. We look at this complex problem from a systems engineering point of view in order to go from a proof-of-concept prototype to a stable software framework. Our system utilizes two detection and analysis methods: (i) face detection with eye region extrapolation and (ii) eye state classification. Additionally, we use two confirmation processes - one based on custom skin color detection, the other based on nod detection - to make the system more robust and resilient while not sacrificing speed significantly. The system was designed to be dynamic and adaptable to conform to the current conditions and hardware capabilities.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004275, http://purl.flvc.org/fau/fd/FA00004275
Subject Headings: Circadian rhythms, Computer vision, Driver assistance systems, Drowsy driving, Fatigue -- Prevention
Format: Document (PDF)

Title: ASSESSING METHODS AND TOOLS TO IMPROVE REPORTING, INCREASE TRANSPARENCY, AND REDUCE FAILURES IN MACHINE LEARNING APPLICATIONS IN HEALTHCARE.
Creator: Garbin, Christian, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Artificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The...
Show moreArtificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The software engineering community has accumulated a large body of knowledge over the decades on how to develop, release, and maintain products. AI products, being software products, benefit from some of that accumulated knowledge, but not all of it. AI products diverge from traditional software products in fundamental ways: their main component is not a specific piece of code, written for a specific purpose, but a generic piece of code, a model, customized by a training process driven by hyperparameters and a dataset. Datasets are usually large and models are opaque. We cannot directly inspect them as we can inspect the code of traditional software products. We need other methods to detect failures in AI products.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013580
Subject Headings: Machine learning, Artificial intelligence, Healthcare
Format: Document (PDF)

Title: Sparse and low rank constraints on optical flow and trajectories.
Creator: Gibson, Joel, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this dissertation we apply sparse constraints to improve optical flow and trajectories. We apply sparsity in two ways. First, with 2-frame optical flow, we enforce a sparse representation of flow patches using a learned overcomplete dictionary. Second, we apply a low rank constraint to trajectories via robust coupling. We begin with a review of optical flow fundamentals. We discuss the commonly used flow estimation strategies and the advantages and shortcomings of each. We introduce the...
Show moreIn this dissertation we apply sparse constraints to improve optical flow and trajectories. We apply sparsity in two ways. First, with 2-frame optical flow, we enforce a sparse representation of flow patches using a learned overcomplete dictionary. Second, we apply a low rank constraint to trajectories via robust coupling. We begin with a review of optical flow fundamentals. We discuss the commonly used flow estimation strategies and the advantages and shortcomings of each. We introduce the concepts associated with sparsity including dictionaries and low rank matrices.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004286, http://purl.flvc.org/fau/fd/FA00004286
Subject Headings: Approximation theory -- Mathematical models, Computer vision, Image processing -- Digital techniques, Information visualization
Format: Document (PDF)

Title: Skin lesion segmentation and classification using deep learning.
Creator: Burdick, John B., Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Melanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed or left undiagnosed. Advances in artificial intelligence, particularly deep learning, have enabled the design and implementation of intelligent solutions to skin lesion detection and classification from visible light images, which are capable of performing early and accurate diagnosis of melanoma and other types of skin diseases. This work presents solutions to the problems of skin lesion segmentation and...
Show moreMelanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed or left undiagnosed. Advances in artificial intelligence, particularly deep learning, have enabled the design and implementation of intelligent solutions to skin lesion detection and classification from visible light images, which are capable of performing early and accurate diagnosis of melanoma and other types of skin diseases. This work presents solutions to the problems of skin lesion segmentation and classification. The proposed classification approach leverages convolutional neural networks and transfer learning. Additionally, the impact of segmentation (i.e., isolating the lesion from the rest of the image) on the performance of the classifier is investigated, leading to the conclusion that there is an optimal region between “dermatologist segmented” and “not segmented” that produces best results, suggesting that the context around a lesion is helpful as the model is trained and built. Generative adversarial networks, in the context of extending limited datasets by creating synthetic samples of skin lesions, are also explored. The robustness and security of skin lesion classifiers using convolutional neural networks are examined and stress-tested by implementing adversarial examples.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013021
Subject Headings: Melanoma, Medical imaging, Deep learning, Skin diseases--Classification, Image segmentation
Format: Document (PDF)

Title: Content-based image retrieval using relevance feedback.
Creator: Marques, Oge, Florida Atlantic University, Furht, Borko, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation presents the results of research that led to the development of a complete, fully functional, image search and retrieval system with relevance feedback capabilities, called MUSE (MUltimedia SEarch and Retrieval Using Relevance Feedback). Two different models for searching for a target image using relevance feedback have been proposed, implemented, and tested. The first model uses a color-based feature vector and employs a Bayesian learning algorithm that updates the...
Show moreThis dissertation presents the results of research that led to the development of a complete, fully functional, image search and retrieval system with relevance feedback capabilities, called MUSE (MUltimedia SEarch and Retrieval Using Relevance Feedback). Two different models for searching for a target image using relevance feedback have been proposed, implemented, and tested. The first model uses a color-based feature vector and employs a Bayesian learning algorithm that updates the probability of each image in the database being the target based on the user's actions. The second model uses cluster analysis techniques, a combination of color-, texture-, and edge(shape)-based features, and a novel approach to learning the user's goals and the relevance of each feature for a particular search. Both models follow a purely content-based image retrieval paradigm. The search process is based exclusively on image contents automatically extracted during the (off-line) feature extraction stage. Moreover, they minimize the number and complexity of required user's actions, in contrast with the complexity of the underlying search and retrieval engine. Results of experiments show that both models exhibit good performance for moderate-size, unconstrained databases and that a combination of the two outperforms any of them individually, which is encouraging. In the process of developing this dissertation, we also implemented and tested several image features and similarity measurement combinations. The result of these tests---performed under the query-by-example (QBE) paradigm---served as a reference in the choice of which features to use in the relevance feedback mode and confirmed the difficulty in encoding the understanding of image similarity into a combination of features and distances without human assistance. Most of the code written during the development of this dissertation has been encapsulated into a multifunctional prototype that combines image searching (with or without an example), browsing, and viewing capabilities and serves as a framework for future research in the subject.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/11954
Subject Headings: Information storage and retrieval systems, Image processing--Digital techniques, Feedback control systems
Format: Document (PDF)

Title: OPTIMIZED DEEP LEARNING ARCHITECTURES AND TECHNIQUES FOR EDGE AI.
Creator: Zaniolo, Luiz, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected...
Show moreThe recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected technical issues associated with edge AI, proposes novel solutions to them, and demonstrates the effectiveness of the proposed approaches. The technical contributions of this dissertation include: (i) architectural optimizations to deep neural networks, particularly the use of patterned stride in convolutional neural networks used for image classification; (ii) use of weight quantization to reduce model size without hurting its accuracy; (iii) systematic evaluation of the impact of image imperfections on skin lesion classifiers' performance in the context of teledermatology; and (iv) a new approach for code prediction using natural language processing techniques, targeted at edge devices.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013822
Subject Headings: Artificial intelligence, Deep learning (Machine learning), Neural networks (Computer science)
Format: Document (PDF)

Title: DIGITAL TRANSFORMATION OF HEALTHCARE USING ARTIFICIAL INTELLIGENCE.
Creator: Gogova, Jennifer, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Digital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare...
Show moreDigital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare professionals. Challenge #2 explores the concept of transdisciplinary teams needing a work protocol to deliver successful results. This is addressed by Contribution #2 which is a step-by-step protocol for medical and AI researchers working on data-intensive projects. Challenge #3 states that the NIH All of Us Research Hub has a steep learning curve, and this is addressed by Contribution #3 which is a pilot project involving transdisciplinary teams using All of Us datasets.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014179
Subject Headings: Healthcare, Medical care, Artificial intelligence—Medical applications
Format: Document (PDF)

Title: INVESTIGATING AND IMPROVING FAIRNESS AND BIAS IN MACHINE LEARNING MODELS FOR DERMATOLOGY.
Creator: Corbin, Adam, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Advancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved their application in dermatology. However, bias issues in AI systems can result in missed diagnoses and disparities in healthcare, especially for individuals with different skin types. This dissertation aims to investigate and improve the fairness and bias in machine learning models for dermatology by evaluating and enhancing their performance across different Fitzpatrick skin types. The...
Show moreAdvancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved their application in dermatology. However, bias issues in AI systems can result in missed diagnoses and disparities in healthcare, especially for individuals with different skin types. This dissertation aims to investigate and improve the fairness and bias in machine learning models for dermatology by evaluating and enhancing their performance across different Fitzpatrick skin types. The technical contributions of the dissertation include generating metadata for Fitzpatrick Skin Type using Individual Typology Angle; outlining best practices for Explainable AI (XAI) and the use of colormaps; developing and enhancing ML models through skin color transformation and extending the models to include XAI methods for better interpretation and improvement of fairness and bias; and providing a list of steps for successful application of deep learning in medical image analysis. The research findings of this dissertation have the potential to contribute to the development of fair and unbiased AI/ML models in dermatology. This can ultimately lead to better health outcomes and reduced healthcare costs, particularly for individuals with different skin types.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014131
Subject Headings: Diagnostic Imaging, Machine learning, Dermatology, Artificial intelligence
Format: Document (PDF)

Title: Computer-aided diagnosis of skin cancers using dermatology images.
Creator: Gilani, Syed Qasim, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Skin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming,...
Show moreSkin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming, expensive, and necessitates expert annotation. Moreover, skin cancer datasets often suffer from imbalanced data distribution. Generative Adversarial Networks (GANs) can be used to overcome the challenges of data scarcity and lack of labels by automatically generating skin cancer images. However, training and testing data from different distributions can introduce domain shift and bias, impacting the model’s performance. This dissertation addresses this issue by developing deep learning-based domain adaptation models. Additionally, this research emphasizes deploying deep learning models on hardware to enable real-time skin cancer detection, facilitating accurate diagnoses by dermatologists. Deploying conventional deep learning algorithms on hardware is not preferred due to the problem of high resource consumption. Therefore, this dissertation presents spiking neural network-based (SNN) models designed specifically for hardware implementation. SNNs are preferred for their power-efficient behavior and suitability for hardware deployment.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014233
Subject Headings: Deep learning (Machine learning), Diagnostic imaging, Skin--Cancer--Diagnosis
Format: Document (PDF)

Title: Neural network approach to Bayesian background modeling for video object segmentation.
Creator: Culibrk, Dubravko., Florida Atlantic University, Furht, Borko, Marques, Oge, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Object segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based...
Show moreObject segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based approach to background modeling for motion-based object segmentation in video sequences. In particular, we show how Probabilistic Neural Network (PNN) architecture can be extended to form an unsupervised Bayesian classifier for the domain of video object segmentation. The constructed Background Modeling Neural Network (BNN) is capable of efficiently handling segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed neural network serve as an exclusive model of the background and are temporally updated to reflect the observed background statistics. The proposed approach is designed to enable an efficient, highly-parallelized hardware implementation. Such a system would be able to achieve real-time segmentation of high-resolution image sequences.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12214
Subject Headings: Neural networks (Computer science), Application software--Development, Data structures (Computer science), Bayesian field theory
Format: Document (PDF)

Title: Methods and Algorithms for Human Detection in Video Sequences.
Creator: Pertuz, Carlos, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Lower prices of video sensors, security concerns and the need for better and faster algorithms to extract high level information from video sequences are all factors which have stimulated research in the area of automated video surveillance systems. In the context of security the analysis of human interrelations and their environment provides hints to proactively identify anomalous behavior. However, human detection is a necessary component in systems where the automatic extraction of higher...
Show moreLower prices of video sensors, security concerns and the need for better and faster algorithms to extract high level information from video sequences are all factors which have stimulated research in the area of automated video surveillance systems. In the context of security the analysis of human interrelations and their environment provides hints to proactively identify anomalous behavior. However, human detection is a necessary component in systems where the automatic extraction of higher level information, such as recognizing individuals' activities, is required. The human detection problem is one of classification. In general, motion, appearance and shape are the classification approaches a system can employ to perform human detection. Techniques representative of these approaches, such us periodic motion detection, skin color detection and MPEG-7 shape descriptors are implemented in this work. An infrastructure that allows data collection for such techniques was also implemented.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012538
Subject Headings: MPEG (Video coding standard), Image processing--Digital techniques, Form perception, Computer algorithms, Video compression
Format: Document (PDF)

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