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Title: Image classification and image resolution issues for DOQQ analysis.
Creator: Boruff, Bryan Jeffery., Florida Atlantic University, Roberts, Charles
Abstract/Description: High-resolution imagery is becoming readily available to the public. Private firms and government organizations are using high-resolution images but are running into problems with storage space and processing time. High-resolution images are extremely large files, and have proven cumbersome to work with and control. By resampling fine resolution imagery to a lower resolution, storage and processing space can be dramatically reduced. Fine-resolution imagery is not needed to map most features...
Show moreHigh-resolution imagery is becoming readily available to the public. Private firms and government organizations are using high-resolution images but are running into problems with storage space and processing time. High-resolution images are extremely large files, and have proven cumbersome to work with and control. By resampling fine resolution imagery to a lower resolution, storage and processing space can be dramatically reduced. Fine-resolution imagery is not needed to map most features and resampled high-resolution imagery can be used as a replacement for low-resolution satellite imagery in some cases. The effects of resampling on the spectral quality of a high-resolution image can be demonstrated by answering the following questions: (1) Is the quality of spectral information on a color infrared DOQQ comparable to SPOT and TM Landsat satellite imagery for the purpose of digital image classification? (2) What is the appropriate resolution for mapping surface features using high-resolution imagery for spectral categories of information? (3) What is the appropriate resolution for mapping surface features using high-resolution imagery for land-use land-cover information?
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/15787
Subject Headings: Remote sensing, Image processing, Image analysis
Format: Document (PDF)

Title: SEAWALL DETECTION IN FLORIDA COASTAL AREA FROM HIGH RESOLUTION IMAGERY USING MACHINE LEARNING AND OBIA.
Creator: Paudel, Sanjaya, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: In this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image...
Show moreIn this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image analysis (OBIA) method were applied for image classification. However, Pixel based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning technique, knowledge-based technique and machine learning followed by knowledge-based technique were used to compare the most efficient method of classification. While performing the machine learning technique, three algorithms: Random Forest, support vector machine and decision tree were applied to test the best algorithm. Of all the approaches used, the combination of machine learning and a knowledge-based method was able to map the sea wall effectively.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013802
Subject Headings: Image analysis, Coasts--Florida, Machine learning
Format: Document (PDF)

Title: Improving In Vivo Two Photon Microscopy Without Adaptive Optics.
Creator: Estrada, Gerardo, Beetle, Christopher, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: Two photon microscopy is one of the fastest growing methods of in-vivo imaging of the brain. It has the capability of imaging structures on the scale of 1μm. At this scale the wavelength of the imaging field (usually near infra-red), is comparable to the size of the structures being imaged, which makes the use of ray optics invalid. A better understanding is needed to predict the result of introducing different media into the light path. We use Wolf's integral, which is capable of fulfilling...
Show moreTwo photon microscopy is one of the fastest growing methods of in-vivo imaging of the brain. It has the capability of imaging structures on the scale of 1μm. At this scale the wavelength of the imaging field (usually near infra-red), is comparable to the size of the structures being imaged, which makes the use of ray optics invalid. A better understanding is needed to predict the result of introducing different media into the light path. We use Wolf's integral, which is capable of fulfilling these needs without the shortcomings of ray optics. We predict the effects of aberrating media introduced into the light path like glass cover-slips and then correct the aberration using the same method. We also create a method to predict aberrations when the interfaces of the media in the light-path are not aligned with the propagation direction of the wavefront.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004495
Subject Headings: Cellular signal transmission -- Measurement, Image analysis, Imaging systems in medicine, Membranes (Biology) -- Imaging, Neurons -- Imaging, Optics, Adaptive
Format: Document (PDF)

Title: DEVELOPING A DEEP LEARNING PIPELINE TO AUTOMATICALLY ANNOTATE GOLD PARTICLES IN IMMUNOELECTRON MICROSCOPY IMAGES.
Creator: Jerez, Diego Alejandro, Hahn, William, Florida Atlantic University, Department of Mathematical Sciences, Charles E. Schmidt College of Science
Abstract/Description: Machine learning has been utilized in bio-imaging in recent years, however as it is relatively new and evolving, some researchers who wish to utilize machine learning tools have limited access because of a lack of programming knowledge. In electron microscopy (EM), immunogold labeling is commonly used to identify the target proteins, however the manual annotation of the gold particles in the images is a time-consuming and laborious process. Conventional image processing tools could provide...
Show moreMachine learning has been utilized in bio-imaging in recent years, however as it is relatively new and evolving, some researchers who wish to utilize machine learning tools have limited access because of a lack of programming knowledge. In electron microscopy (EM), immunogold labeling is commonly used to identify the target proteins, however the manual annotation of the gold particles in the images is a time-consuming and laborious process. Conventional image processing tools could provide semi-automated annotation, but those require that users make manual adjustments for every step of the analysis. To create a new high-throughput image analysis tool for immuno-EM, I developed a deep learning pipeline that was designed to deliver a completely automated annotation of immunogold particles in EM images. The program was made accessible for users without prior programming experience and was also expanded to be used on different types of immuno-EM images.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013628
Subject Headings: Electron microscopy, Immunogold labeling, Image analysis, Deep learning
Format: Document (PDF)

Title: Image retrieval using visual attention.
Creator: Mayron, Liam M., College of Engineering and Computer Science, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user's high-level interpretation of an image and the information that can be extracted from an image's physical properties. Content based image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. High-level...
Show moreThe retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user's high-level interpretation of an image and the information that can be extracted from an image's physical properties. Content based image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. High-level descriptions of images are more capable of capturing the semantic meaning of image content, but it is not always practical to collect this information. Thus, both content-based and human-generated information is considered in this work. A content-based method of retrieving images using a computational model of visual attention was proposed, implemented, and evaluated. This work is based on a study of contemporary research in the field of vision science, particularly computational models of bottom-up visual attention. The use of computational models of visual attention to detect salient by design regions of interest in images is investigated. The method is then refined to detect objects of interest in broad image databases that are not necessarily salient by design. An interface for image retrieval, organization, and annotation that is compatible with the attention-based retrieval method has also been implemented. It incorporates the ability to simultaneously execute querying by image content, keyword, and collaborative filtering. The user is central to the design and evaluation of the system. A game was developed to evaluate the entire system, which includes the user, the user interface, and retrieval methods.
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Date Issued: 2008
PURL: http://purl.flvc.org/fcla/flaent/EN00154040/68_1/98p0137i.pdf, http://purl.flvc.org/FAU/58006
Subject Headings: Image processing, Digital techniques, Database systems, Cluster analysis, Multimedia systems
Format: Document (PDF)

Title: Developments in laser-line scanned undersea surface mapping and image analysis systems for scientific applications.
Creator: Caimi, F. M., Kocak, D. M., Asper, V. L., Harbor Branch Oceanographic Institute
Date Issued: 1996
PURL: http://purl.flvc.org/FCLA/DT/3183695
Subject Headings: Underwater imaging systems, Image analysis, Topographical surveying--Laser use in
Format: Document (PDF)

Title: COMBINING TRADITIONAL AND IMAGE ANALYSIS TECHNIQUES FOR UNCONSOLIDATED EXPOSED TERRIGENOUS BEACH SAND CHARACTERIZATION.
Creator: Smith, Molly Elizabeth, Zhang, Caiyun, Oleinik, Anton, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
Abstract/Description: Traditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote...
Show moreTraditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote sensing digital image analysis techniques, such as object-based image analysis (OBIA), machine learning, digital image analysis, and photogrammetry to automate or semi-automate the traditional sand analysis procedure. Here presented is a framework combining OBIA and machine learning classification of microscope imagery for use with unconsolidated terrigenous beach sand samples. Five machine learning classifiers (RF, DT, SVM, k-NN, and ANN) are used to model mineral composition from images of ten terrigenous beach sand samples. Digital image analysis and photogrammetric techniques are applied and evaluated for use to characterize sand grain size and grain circularity (given as a digital proxy for traditional grain sphericity). A new segmentation process is also introduced, where pixel-level SLICO superpixel segmentation is followed by spectral difference segmentation and further levels of superpixel segmentation at the object-level. Previous methods of multi-resolution and superpixel segmentation at the object level do not provide the level of detail necessary to yield optimal sand grain-sized segments. In this proposed framework, the DT and RF classifiers provide the best estimations of mineral content of all classifiers tested compared to traditional compositional analysis. Average grain size approximated from photogrammetric procedures is comparable to traditional sieving methods, having an RMSE below 0.05%. The framework proposed here reduces the number of trained personnel needed to perform sand-related research. It requires minimal sand sample preparation and minimizes user-error that is typically introduced during traditional sand analysis.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013517
Subject Headings: Sand, Image analysis, Remote sensing, Photogrammetry--Digital techniques, Machine learning
Format: Document (PDF)

Title: How the Spatial Organization of Objects Affects Perceptual Processing of a Scene.
Creator: Rashford, Stacey, Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description: How does spatial organization of objects affect the perceptual processing of a scene? Surprisingly, little research has explored this topic. A few studies have reported that, when simple, homogenous stimuli (e.g., dots), are presented in a regular formation, they are judged to be more numerous than when presented in a random configuration (Ginsburg, 1976; 1978). However, these results may not apply to real-world objects. In the current study, fewer objects were believed to be on organized...
Show moreHow does spatial organization of objects affect the perceptual processing of a scene? Surprisingly, little research has explored this topic. A few studies have reported that, when simple, homogenous stimuli (e.g., dots), are presented in a regular formation, they are judged to be more numerous than when presented in a random configuration (Ginsburg, 1976; 1978). However, these results may not apply to real-world objects. In the current study, fewer objects were believed to be on organized desks than their disorganized equivalents. Objects that are organized may be more likely to become integrated, due to classic Gestalt principles. Consequently, visual search may be more difficult. Such object integration may diminish saliency, making objects less apparent and more difficult to find. This could explain why, in the present study, objects on disorganized desks were found faster.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004537, http://purl.flvc.org/fau/fd/FA00004537
Subject Headings: Image analysis, Optical pattern recognition, Pattern recognition systems, Phenomenological psychology, Visual perception
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: Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery.
Creator: Johnson, Brian A., Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description: Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of...
Show moreFine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods., Since the two dissertation methods used an automated algorithm (Random Forest) for image classification, they are also less subjective and easier to apply to other study areas than most existing multi-scale object-based methods that rely on expert knowledge (i.e. decision rules developed based on detailed visual inspection of image segments) for classifying each type of land cover.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342110
Subject Headings: Image processing, Digital techniques, Remote sensing, Mathematics, Remote-sensing images, Computational intelligence, Cities and towns, Remote sensing, Environmental sciences, Remote sensing, Spatial analysis (Statistics)
Format: Document (PDF)

Title: Raman and surface-enhanced raman spectroscopy of G-quadruplexes.
Creator: Friedman, Samantha, Terentis, Andrew C., Florida Atlantic University, Charles E. Schmidt College of Science, Department of Chemistry and Biochemistry
Abstract/Description: G-quadruplexes (G4s) are nucleic acid structures formed from π-stacked planar sets of four Hoogsteen hydrogen bonded guanine bases. G4s emerged as potential therapeutic targets based on their ability to modulate gene expression and inhibit the ability of telomerase to elongate chromosomal telomeres. Raman spectroscopy, polarized Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), and other optical spectroscopic techniques were used to characterize the G4s formed by four different...
Show moreG-quadruplexes (G4s) are nucleic acid structures formed from π-stacked planar sets of four Hoogsteen hydrogen bonded guanine bases. G4s emerged as potential therapeutic targets based on their ability to modulate gene expression and inhibit the ability of telomerase to elongate chromosomal telomeres. Raman spectroscopy, polarized Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), and other optical spectroscopic techniques were used to characterize the G4s formed by four different DNA sequences: human telomeric (HT), thrombin-binding aptamer (TBA), nuclease hypersensitive element III1 region of the c- Myc gene promoter (Myc), and a single loop-isomer of Myc (MycL1).
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004370, http://purl.flvc.org/fau/fd/FA00004370
Subject Headings: Nucleic acids, Binding sites (Biochemistry), Biochemical genetics, Raman spectroscopy, Raman effect, Surface enhanced, Spectroscopic imaging, Spectrum analysis
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: EVALUATING UNMANNED AIRCRAFT SYSTEM PHOTOGRAMMETRY FOR COASTAL FLORIDA EVERGLADES RESTORATION AND MANAGEMENT.
Creator: Durgan, Sara D., Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
Abstract/Description: The Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific...
Show moreThe Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific information on the current state of the coastal wetland habitats. In order to produce such assessments, digital datasets of the appropriate accuracy and scale are needed. These datasets include orthoimagery to delineate wetland areas and map vegetation cover as well as accurate 3-dimensional (3-D) models to characterize hydrology, physiochemistry, and habitat vulnerability.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013501
Subject Headings: Everglades (Fla )--Environmental conditions--Remote sensing, Aerial photogrammetry, Wetland restoration--Florida--Everglades, Image analysis, Aerial photogrammetry--Data processing, Drone aircraft
Format: Document (PDF)

FAU Theses and Dissertations (13)	+ -
HBOI Faculty Contributions (1)	+ -

Image analysis (8)	+ -
Image processing (3)	+ -
Optical pattern recognition (3)	+ -
Remote sensing (3)	+ -
Computer vision (2)	+ -

Digital techniques (2)	+ -
Machine learning (2)	+ -
Aerial photogrammetry (1)	+ -
Aerial photogrammetry--Data processing (1)	+ -
Binding sites (Biochemistry) (1)	+ -
Biochemical genetics (1)	+ -
Cellular signal transmission -- Measurement (1)	+ -
Cities and towns (1)	+ -
Cluster analysis (1)	+ -
Coasts--Florida (1)	+ -
Cognitive science (1)	+ -
Computational intelligence (1)	+ -
Computer vision. (1)	+ -
Database systems (1)	+ -
Deep learning (1)	+ -
Drone aircraft (1)	+ -
Electron microscopy (1)	+ -
Electronic surveillance (1)	+ -
Environmental sciences (1)	+ -
Everglades (Fla )--Environmental conditions--Remote sensing (1)	+ -
Image analysis. (1)	+ -
Imaging systems (1)	+ -
Imaging systems in medicine (1)	+ -
Immunogold labeling (1)	+ -
Logistic regression analysis (1)	+ -

Electronic Thesis or Dissertation (13)	+ -
article (1)	+ -

Florida Atlantic University (11)	+ -
Charles E. Schmidt College of Science (7)	+ -
College of Engineering and Computer Science (4)	+ -
Department of Computer and Electrical Engineering and Computer Science (3)	+ -
Department of Geosciences (3)	+ -

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