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Title: Visual cues in active monocular vision for autonomous navigation.
Creator: Yang, Lingdi., Florida Atlantic University, Raviv, Daniel, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this dissertation, visual cues using an active monocular camera for autonomous vehicle navigation are investigated. A number of visual cues suitable to such an objective are proposed and effective methods to extract them are developed. Unique features of these visual cues include: (1) There is no need to reconstruct the 3D scene; (2) they utilize short image sequences taken by a monocular camera; and (3) they operate on local image brightness information. Taking these features into account...
Show moreIn this dissertation, visual cues using an active monocular camera for autonomous vehicle navigation are investigated. A number of visual cues suitable to such an objective are proposed and effective methods to extract them are developed. Unique features of these visual cues include: (1) There is no need to reconstruct the 3D scene; (2) they utilize short image sequences taken by a monocular camera; and (3) they operate on local image brightness information. Taking these features into account, the algorithms developed are computationally efficient. Simulation and experimental studies confirm the efficacy of the algorithms developed. The major contribution of the research work in this dissertation is the extraction of visual information suitable for autonomous navigation in an active monocular camera without 3D reconstruction by use of local image information. In the studies addressed, the first visual cue is related to camera focusing parameters. An objective function relating focusing parameters to local image brightness is proposed. A theoretical development is conducted to show that by maximizing the objective function one can focus successfully the camera by choosing the focusing parameters. As a result, the dense distance map between a camera and a front scene can be estimated without using the Gaussian spread function. The second visual cue, namely, the clearance invariant (first proposed by Raviv (97)), is extended here to include arbitrary translational motion of a camera. It is shown that the angle between the optical axis and moving direction of a camera can be estimated by minimizing the relevant estimated error residual. This method needs only one image projection from a 3D surface point at an arbitrary time instant. The third issue discussed in this dissertation refers to extracting the looming and the magnitude of rotation using a new visual cue designated as the rotation invariant under the camera fixation. An algorithm to extract the looming is proposed using the image information available from only one 3D surface point at an arbitrary time instant. Further, an additional algorithm is proposed to estimate the magnitude of rotational velocity of the camera by using the image projections of only two 3D surface points measured over two time instants. Finally, a method is presented to extract the focus of expansion robustly without using image brightness derivatives. It decomposes an image projection trajectory into two independent linear models, and applies the Kalman filters to estimate the focus of expansion.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/12527
Subject Headings: Computer vision, Robot vision
Format: Document (PDF)

Title: Visual threat cues for autonomous navigation.
Creator: Kundur, Sridhar Reddy, Florida Atlantic University, Raviv, Daniel, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation deals with novel vision-based motion cues called the Visual Threat Cues (VTCs), suitable for autonomous navigation tasks such as collision avoidance and maintenance of clearance. The VTCs are time-based and provide some measure for a relative change in range as well as clearance between a 3D surface and a moving observer. They are independent of the 3D environment around the observer and need almost no a-priori knowledge about it. For each VTC presented in this dissertation,...
Show moreThis dissertation deals with novel vision-based motion cues called the Visual Threat Cues (VTCs), suitable for autonomous navigation tasks such as collision avoidance and maintenance of clearance. The VTCs are time-based and provide some measure for a relative change in range as well as clearance between a 3D surface and a moving observer. They are independent of the 3D environment around the observer and need almost no a-priori knowledge about it. For each VTC presented in this dissertation, there is a corresponding visual field associated with it. Each visual field constitutes a family of imaginary 3D surfaces attached to the moving observer. All the points that lie on a particular imaginary 3D surface, produce the same value of the VTC. These visual fields can be used to demarcate the space around the moving observer into safe and danger zones of varying degree. Several approaches to extract the VTCs from a sequence of monocular images have been suggested. A practical method to extract the VTCs from a sequence of images of 3D textured surfaces, obtained by a visually fixation, fixed-focus moving camera is also presented. This approach is based on the extraction of a global image dissimilarity measure called the Image Quality Measure (IQM), which is extracted directly from the raw data of the gray level images. Based on the relative variations of the measured IQM, the VTCs are extracted. This practical approach to extract the VTCs needs no 3D reconstruction, depth information, optical flow or feature tracking. This algorithm to extract the VTCs was tested on several indoor as well as outdoor real image sequences. Two vision-based closed-loop control schemes for autonomous navigation tasks were implemented in a-priori unknown textured environments using one of the VTCs as relevant sensory feedback information. They are based on a set of IF-THEN fuzzy rules and need almost no a-priori information about the vehicle dynamics, speed, direction of motion, etc. They were implemented in real-time using a camera mounted on a six degree-of-freedom flight simulator.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/12476
Subject Headings: Computer vision, Robot vision, Visual perception
Format: Document (PDF)

Title: Computer vision techniques for quantifying, tracking, and identifying bioluminescent plankton.
Creator: Kocak, D. M., da Vitoria Lobo, N., Widder, Edith A., Harbor Branch Oceanographic Institute
Date Issued: 1999
PURL: http://purl.flvc.org/FCLA/DT/3183711
Subject Headings: Underwater imaging systems, Computer vision
Format: Document (PDF)

Title: Characterization of A Stereo Vision System For Object Classification For USV Navigation.
Creator: Kaplowitz, Chad, Dhanak, Manhar, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: This experiment used different methodologies and comparisons that helped to determine the direction of future research on water-based perception systems for unmanned surface vehicles (USV) platforms. This would be using a stereo-vison based system. Presented in this work is object color and shape classification in the real-time maritime environment. This was coupled with HSV color space that allowed for different thresholds to be identified and detected. The algorithm was then calibrated and...
Show moreThis experiment used different methodologies and comparisons that helped to determine the direction of future research on water-based perception systems for unmanned surface vehicles (USV) platforms. This would be using a stereo-vison based system. Presented in this work is object color and shape classification in the real-time maritime environment. This was coupled with HSV color space that allowed for different thresholds to be identified and detected. The algorithm was then calibrated and executed to configure the depth, color and shape accuracies. The approach entails the characterization of a stereo-vision camera and mount that was designed with 8.5° horizontal viewing increments and mounted on the WAMV. This characterization has depth, color and shape object detection and its classification. Different shapes and buoys were used to complete the testing with assorted colors and shapes. The main program used was OpenCV which entails Gaussian blurring, Morphological operators and Canny edge detection libraries with a ROS integration. The code focuses on the area size and the number of contours detected on the shape for successes. A summary of what this thesis entails is the installation and characterization of the stereovision system on the WAMV-USV by obtaining specific inputs to the high-level controller.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014035
Subject Headings: Computer vision, Unmanned surface vehicles
Format: Document (PDF)

Title: Selective texture characterization using Gabor filters.
Creator: Boutros, George., Florida Atlantic University, Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The objective of this dissertation is to develop effective algorithms for texture characterization, segmentation and labeling that operate selectively to label image textures, using the Gabor representation of signals. These representations are an analog of the spatial frequency tuning characteristics of the visual cortex cells. The Gabor function, of all spatial/spectral signal representations, provides optimal resolution between both domains. A discussion of spatial/spectral representations...
Show moreThe objective of this dissertation is to develop effective algorithms for texture characterization, segmentation and labeling that operate selectively to label image textures, using the Gabor representation of signals. These representations are an analog of the spatial frequency tuning characteristics of the visual cortex cells. The Gabor function, of all spatial/spectral signal representations, provides optimal resolution between both domains. A discussion of spatial/spectral representations focuses on the Gabor function and the biological analog that exists between it and the simple cells of the striate cortex. A simulation generates examples of the use of the Gabor filter as a line detector with synthetic data. Simulations are then presented using Gabor filters for real texture characterization. The Gabor filter spatial and spectral attributes are selectively chosen based on the information from a scale-space image in order to maximize resolution of the characterization process. A variation of probabilistic relaxation that exploits the Gabor filter spatial and spectral attributes is devised, and used to force a consensus of the filter responses for texture characterization. We then perform segmentation of the image using the concept of isolation of low energy states within an image. This iterative smoothing algorithm, operating as a Gabor filter post-processing stage, depends on a line processes discontinuity threshold. Selection of the discontinuity threshold is obtained from the modes of the histogram of the relaxed Gabor filter responses using probabilistic relaxation to detect the significant modes. We test our algorithm on simple synthetic and real textures, then use a more complex natural texture image to test the entire algorithm. Limitations on textural resolution are noted, as well as for the resolution of the image segmentation process.
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Date Issued: 1993
PURL: http://purl.flvc.org/fcla/dt/12342
Subject Headings: Image processing--Digital techniques, Computer vision
Format: Document (PDF)

Title: CRACKING THE SPARSE CODE: LATERAL COMPETITION FORMS ROBUST V1-LIKE REPRESENTATIONS IN CONVOLUTIONAL NEURAL NETWORKS.
Creator: Teti, Michael, Barenholtz, Elan, Hahn, William, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
Abstract/Description: Although state-of-the-art Convolutional Neural Networks (CNNs) are often viewed as a model of biological object recognition, they lack many computational and architectural motifs that are postulated to contribute to robust perception in biological neural systems. For example, modern CNNs lack lateral connections, which greatly outnumber feed-forward excitatory connections in primary sensory cortical areas and mediate feature-specific competition between neighboring neurons to form robust,...
Show moreAlthough state-of-the-art Convolutional Neural Networks (CNNs) are often viewed as a model of biological object recognition, they lack many computational and architectural motifs that are postulated to contribute to robust perception in biological neural systems. For example, modern CNNs lack lateral connections, which greatly outnumber feed-forward excitatory connections in primary sensory cortical areas and mediate feature-specific competition between neighboring neurons to form robust, sparse representations of sensory stimuli for downstream tasks. In this thesis, I hypothesize that CNN layers equipped with lateral competition better approximate the response characteristics and dynamics of neurons in the mammalian primary visual cortex, leading to increased robustness under noise and/or adversarial attacks relative to current robust CNN layers. To test this hypothesis, I develop a new class of CNNs called LCANets, which simulate recurrent, feature-specific lateral competition between neighboring neurons via a sparse coding model termed the Locally Competitive Algorithm (LCA). I first perform an analysis of the response properties of LCA and show that sparse representations formed by lateral competition more accurately mirror response characteristics of primary visual cortical populations and are more useful for downstream tasks like object recognition than previous sparse CNNs, which approximate competition with winner-take-all mechanisms implemented via thresholding.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014050
Subject Headings: Neural networks (Computer science), Machine learning, Computer vision
Format: Document (PDF)

Title: Using Deep Learning Semantic Segmentation to Estimate Visual Odometry.
Creator: Blankenship, Jason R., Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: In this research, image segmentation and visual odometry estimations in real time are addressed, and two main contributions were made to this field. First, a new image segmentation and classification algorithm named DilatedU-NET is introduced. This deep learning based algorithm is able to process seven frames per-second and achieves over 84% accuracy using the Cityscapes dataset. Secondly, a new method to estimate visual odometry is introduced. Using the KITTI benchmark dataset as a baseline,...
Show moreIn this research, image segmentation and visual odometry estimations in real time are addressed, and two main contributions were made to this field. First, a new image segmentation and classification algorithm named DilatedU-NET is introduced. This deep learning based algorithm is able to process seven frames per-second and achieves over 84% accuracy using the Cityscapes dataset. Secondly, a new method to estimate visual odometry is introduced. Using the KITTI benchmark dataset as a baseline, the visual odometry error was more significant than could be accurately measured. However, the robust framerate speed made up for this, able to process 15 frames per second.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00005990
Subject Headings: Image segmentation, Computer vision, Deep learning, Visual odometry
Format: Document (PDF)

Title: Low-level and high-level correlation for image registration.
Creator: Mandalia, Anil Dhirajlal., Florida Atlantic University, Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The fundamental goal of a machine vision system in the inspection of an assembled printed circuit board is to locate the integrated circuit(IC) components. These components are then checked for their position and orientation with respect to a given position and orientation of the model and to detect deviations. To this end, a method based on a modified two-level correlation scheme is presented in this thesis. In the first level, Low-Level correlation, a modified two-stage template matching...
Show moreThe fundamental goal of a machine vision system in the inspection of an assembled printed circuit board is to locate the integrated circuit(IC) components. These components are then checked for their position and orientation with respect to a given position and orientation of the model and to detect deviations. To this end, a method based on a modified two-level correlation scheme is presented in this thesis. In the first level, Low-Level correlation, a modified two-stage template matching method is proposed. It makes use of the random search techniques, better known as the Monte Carlo method, to speed up the matching process on binarized version of the images. Due to the random search techniques, there is uncertainty involved in the location where the matches are found. In the second level, High-Level correlation, an evidence scheme based on the Dempster-Shafer formalism is presented to resolve the uncertainty. Experiment results performed on a printed circuit board containing mounted integrated components is also presented to demonstrate the validity of the techniques.
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Date Issued: 1990
PURL: http://purl.flvc.org/fcla/dt/14635
Subject Headings: Image processing--Digital techniques, Computer vision, Integrated circuits
Format: Document (PDF)

Title: IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING.
Creator: Golchubian, Arash, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding...
Show moreThe field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014029
Subject Headings: Deep learning (Machine learning), Computer vision, Aesthetics, Image Quality
Format: Document (PDF)

Title: An Exploration into Synthetic Data and Generative Aversarial Networks.
Creator: Shorten, Connor M., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as Generative Adversarial Networks (GANs). A survey on GANs was conducted to understood recent developments and the problems related to training them. Following this survey, four experiments were proposed to test the application of GANs for data augmentation and to contribute to the quality improvement in GAN-generated data....
Show moreThis Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as Generative Adversarial Networks (GANs). A survey on GANs was conducted to understood recent developments and the problems related to training them. Following this survey, four experiments were proposed to test the application of GANs for data augmentation and to contribute to the quality improvement in GAN-generated data. Experimental results demonstrate the effectiveness of GAN-generated data as a pre-training metric. The other experiments discuss important characteristics of GAN models such as the refining of prior information, transferring generative models from large datasets to small data, and automating the design of Deep Neural Networks within the context of the GAN framework. This Thesis will provide readers with a complete introduction to Data Augmentation and Generative Adversarial Networks, as well as insights into the future of these techniques.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013263
Subject Headings: Neural networks (Computer science), Computer vision, Images, Generative adversarial networks, Data sets
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: Context-based Image Concept Detection and Annotation.
Creator: Zolghadr, Esfandiar, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Scene understanding attempts to produce a textual description of visible and latent concepts in an image to describe the real meaning of the scene. Concepts are either objects, events or relations depicted in an image. To recognize concepts, the decision of object detection algorithm must be further enhanced from visual similarity to semantical compatibility. Semantically relevant concepts convey the most consistent meaning of the scene. Object detectors analyze visual properties (e.g., pixel...
Show moreScene understanding attempts to produce a textual description of visible and latent concepts in an image to describe the real meaning of the scene. Concepts are either objects, events or relations depicted in an image. To recognize concepts, the decision of object detection algorithm must be further enhanced from visual similarity to semantical compatibility. Semantically relevant concepts convey the most consistent meaning of the scene. Object detectors analyze visual properties (e.g., pixel intensities, texture, color gradient) of sub-regions of an image to identify objects. The initially assigned objects names must be further examined to ensure they are compatible with each other and the scene. By enforcing inter-object dependencies (e.g., co-occurrence, spatial and semantical priors) and object to scene constraints as background information, a concept classifier predicts the most semantically consistent set of names for discovered objects. The additional background information that describes concepts is called context. In this dissertation, a framework for building context-based concept detection is presented that uses a combination of multiple contextual relationships to refine the result of underlying feature-based object detectors to produce most semantically compatible concepts. In addition to the lack of ability to capture semantical dependencies, object detectors suffer from high dimensionality of feature space that impairs them. Variances in the image (i.e., quality, pose, articulation, illumination, and occlusion) can also result in low-quality visual features that impact the accuracy of detected concepts. The object detectors used to build context-based framework experiments in this study are based on the state-of-the-art generative and discriminative graphical models. The relationships between model variables can be easily described using graphical models and the dependencies and precisely characterized using these representations. The generative context-based implementations are extensions of Latent Dirichlet Allocation, a leading topic modeling approach that is very effective in reduction of the dimensionality of the data. The discriminative contextbased approach extends Conditional Random Fields which allows efficient and precise construction of model by specifying and including only cases that are related and influence it. The dataset used for training and evaluation is MIT SUN397. The result of the experiments shows overall 15% increase in accuracy in annotation and 31% improvement in semantical saliency of the annotated concepts.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004745, http://purl.flvc.org/fau/fd/FA00004745
Subject Headings: Computer vision--Mathematical models., Pattern recognition systems., Information visualization., Natural language processing (Computer science), Multimodal user interfaces (Computer systems), Latent structure analysis., Expert systems (Computer science)
Format: Document (PDF)

Title: Determination of Horizontal Motion through Optical Flow Computations.
Creator: Chih-Ho, Yu, Caimi, F. M., Harbor Branch Oceanographic Institute
Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/3318842
Subject Headings: Remote submersibles, Remote submersibles --Automatic control, Computer vision, Optical measurements --Remote sensing
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: Automatic parking lot occupancy computation using motion tracking.
Creator: Justo Torres, Francisco Alberto, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Nowadays it is very hard to find available spots in public parking lots and even harder at facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This thesis presents a system for automatic parking lot occupancy computation using motion tracking. The use of computer vision techniques and low cost video sensors makes it possible to have an accurate system...
Show moreNowadays it is very hard to find available spots in public parking lots and even harder at facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This thesis presents a system for automatic parking lot occupancy computation using motion tracking. The use of computer vision techniques and low cost video sensors makes it possible to have an accurate system that allows drivers to find a parking spot. Video bitrate and quality reduction and its impact on performance were studied. It was concluded that high quality video is not necessary for the proposed algorithm to obtain accurate results. The results show that relatively inexpensive and low bandwidth networks can be used to develop large scale parking occupancy applications.
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Date Issued: 2013
PURL: http://purl.flvc.org/fcla/dt/3362483
Subject Headings: Traffic estimation, Automobile parking, Transportation engineering, Transportation demand management, Electronics in transportation, Computer vision
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: 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: A systematic evaluation of object detection and recognition approaches with context capabilities.
Creator: Giusti Urbina, Rafael J., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Contemporary computer vision solutions to the problem of object detection aim at incorporating contextual information into the process. This thesis proposes a systematic evaluation of the usefulness of incorporating knowledge about the geometric context of a scene into a baseline object detection algorithm based on local features. This research extends publicly available MATLABRª implementations of leading algorithms in the field and integrates them in a coherent and extensible way....
Show moreContemporary computer vision solutions to the problem of object detection aim at incorporating contextual information into the process. This thesis proposes a systematic evaluation of the usefulness of incorporating knowledge about the geometric context of a scene into a baseline object detection algorithm based on local features. This research extends publicly available MATLABRª implementations of leading algorithms in the field and integrates them in a coherent and extensible way. Experiments are presented to compare the performance and accuracy between baseline and context-based detectors, using images from the recently published SUN09 dataset. Experimental results demonstrate that adding contextual information about the geometry of the scene improves the detector performance over the baseline case in 50% of the tested cases.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3183127
Subject Headings: Imaging systems, Mathematical models, Cognitive science, Optical pattern recognition, Computer vision, Logistic regression analysis
Format: Document (PDF)

Title: Statistical and Entropy Considerations for Ultrasound Tissue Characterization.
Creator: Navumenka, Khrystsina, Aalo, Valentine A., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Modern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been...
Show moreModern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been found to be a good statistical tool to analyze the envelope and/or the intensity of backscattered signal in ultrasound tissue characterization. However, its use has usually been limited due to the fact that its probability density function (PDF) is not available in closed-form. In this work we present a novel closed-form representation for the H-K distribution. In addition, we propose using the first order approximation of the H-K distribution, the I-K distribution that has a closed-form, for the ultrasound tissue characterization applications. More specifically, we show that some tissue conditions that cause the backscattered signal to have low effective density values, can be successfully modeled by the I-K PDF. We introduce the concept of using H-K PDF-based and I-K PDF-based entropies as additional tools for characterization of ultrasonic breast tissue images. The entropy may be used as a goodness of fit measure that allows to select a better-fitting statistical model for a specific data set. In addition, the values of the entropies as well as the values of the statistical distribution parameters, allow for more accurate classification of tumors.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004922, http://purl.flvc.org/fau/fd/FA00004922
Subject Headings: Ultrasonics in medicine., Artificial intelligence., Computer vision in medicine., Diagnostic ultrasonic imaging., Bioinformatics.
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)

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