Engineering Commons^™

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise, Mehdi Mafi

FIU Electronic Theses and Dissertations

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise.

In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, …

Height Measurement Of Basil Crops For Smart Irrigation Applications In Greenhouses Using Commercial Sensors, Leila Bahman

Electronic Thesis and Dissertation Repository

Plant height is a key phenotypic attribute that directly represents how well a plant grows. It can also be a useful parameter in computing other important features such as yield and biomass. As the number of greenhouses increase, the traditional method of measuring plant height requires more time and labor, which increases demand for developing a reliable and affordable method to perform automated height measurements of plants. This research is aimed to develop a solution to automatically measure plant height in greenhouses using low cost sensors and computer vision techniques. For this purpose, the performance of various depth sensing technologies …

System And Method For Radio Tomographic Image Formation, Richard K. Martin

AFIT Patents

A system and method for generating radio tomographic images is provided. A plurality of transceivers positioned around a region to be imaged is divided into a plurality of pixels. A control apparatus is configured to cause each of the plurality of transceivers in turn to send a signal to each of the other transceivers. The control apparatus is further configured to determine an attenuation in the received signals, generate weighing, derivative, and attenuation matrices from the signals, group the pixels into a plurality of provinces, select each province in turn and solve for a change in attenuation in each of …

Erratum: "Imaging The Three‐Dimensional Orientation And Rotational Mobility Of Fluorescent Emitters Using The Tri‐Spot Point Spread Function", Oumeng Zhang, Jin Lu, Tianben Ding, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

In the original paper, a calibration error exists in the image-formation model used to analyze experimental images taken by our microscope, causing a bias in the orientation measurements in Figs. 2 and 3. The updated measurements are shown in Fig. E1. We have also updated the supplementary material for the original article to discuss the revised PSF model and estimation algorithms (supplementary material 2) and show the revised model and measurements (Figs. S1, S3, S7, S8, and S10–S13).

Full-Scale Study Of Infrared Thermography For Assessing Surface And Subsurface Defects In Pavements And Other Civil Infrastructure, Aidin J. Golrokh

Boise State University Theses and Dissertations

Infrared thermography (IRT) is an effective non-destructive testing method in the field of concrete and asphalt pavements inspection. IRT is used to have an initial evaluation of the surface and near surface of pavements in a very time effective manner compared to other types of nondestructive testing (NDT) methods. Different aspects of IRT and its use to assess surface and subsurface defects in different types of pavements are being studied and evaluated in our research group. The effect of the depth of delamination inside concrete pavement on IRT technique is being studied. It is suggested by our group that there …

A Multi-Sensor Phenotyping System: Applications On Wheat Height Estimation And Soybean Trait Early Prediction, Wenan Yuan

Department of Biological Systems Engineering: Dissertations and Theses

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) …

In Vivo Human-Like Robotic Phenotyping Of Leaf And Stem Traits In Maize And Sorghum In Greenhouse, Abbas Atefi

Department of Biological Systems Engineering: Dissertations and Theses

In plant phenotyping, the measurement of morphological, physiological and chemical traits of leaves and stems is needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, tedious, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, which enables automatic monitoring with minimal human intervention. In this study, two plant phenotyping robotic systems were developed to realize automated measurement of plant leaf properties and stem diameter which could reduce the tediousness of data collection compare to manual measurements. The robotic systems comprised of a four degree …

Low-Energy Acceleration Of Binarized Convolutional Neural Networks Using A Spin Hall Effect Based Logic-In-Memory Architecture, Ashkan Samiee, Payal Borulkar, Ronald F. Demara, Peiyi Zhao, Yu Bai

Engineering Faculty Articles and Research

Deep Learning (DL) offers the advantages of high accuracy performance at tasks such as image recognition, learning of complex intelligent behaviors, and large-scale information retrieval problems such as intelligent web search. To attain the benefits of DL, the high computational and energy-consumption demands imposed by the underlying processing, interconnect, and memory devices on which software-based DL executes can benefit substantially from innovative hardware implementations. Logic-in-Memory (LIM) architectures offer potential approaches to attaining such throughput goals within area and energy constraints starting with the lowest layers of the hardware stack. In this paper, we develop a Spintronic Logic-in-Memory (S-LIM) XNOR neural …

Segmentation And Registration Based Automatic Cancer Proton Treatment Analysis, Yang Zhang

Open Access Theses & Dissertations

Due to its low side effects, proton therapy is rapidly developing around the world. However, the time and labor it takes to deliver the treatment have prevented widespread use in the clinic setting. For this reason, an automatic proton treatment analysis system is needed to improve efficiency during the treatment process.

The challenge lies in improving the accuracy and speed of the current proton treatment analysis system. The Range shifter correction factor is considered the same value under any given condition in our treatment system. This approximate algorithm limits the accuracy of the proton treatment analysis system. In addition, medical …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

Image Processing Algorithms For Elastin Lamellae Inside Cardiovascular Arteries, Mahmoud Habibnezhad

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Automated image processing methods are greatly needed to replace the tedious, manual histology analysis still performed by many physicians. This thesis focuses on pathological studies that express the essential role of elastin lamella in the resilience and elastic properties of the arterial blood vessels. Due to the stochastic nature of the shape and distribution of the elastin layers, their morphological features appear as the best candidates to develop a mathematical formulation for the resistance behavior of elastic tissues. However, even for trained physicians and their assistants, the current measurement procedures are highly error-prone and prolonged. This thesis successfully integrates such …

Multi-Pig Part Detection And Association With A Fully-Convolutional Network, Eric T. Psota, Mateusz Mittek, Lance C. Pérez, Ty Schmidt, Benny Mote

Department of Electrical and Computer Engineering: Faculty Publications

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new …

A Novel Method Of Near-Miss Event Detection With Software Defined Radar In Improving Railyard Safety, Subharthi Banerjee, Jose Santos, Michael Hempel, Pejman Ghasemzadeh, Hamid Sharif

Department of Electrical and Computer Engineering: Faculty Publications

Railyards are one of the most challenging and complex workplace environments in any industry. Railyard workers are constantly surrounded by dangerous moving objects, in a noisy environment where distractions can easily result in accidents or casualties. Throughout the years, yards have been contributing 20–30% of the total accidents that happen in railroads. Monitoring the railyard workspace to keep personnel safe from falls, slips, being struck by large object, etc. and preventing fatal accidents can be particularly challenging due to the sheer number of factors involved, such as the need to protect a large geographical space, the inherent dynamicity of the …

Tracking Hand Trajectory As A Preliminary Study For Hand Hygiene Stages, Rashmi Bakshi, Jane Courtney, Damon Berry, Graham Gavin

Session 5: Medical and Biomedical Imaging

The process of hand washing involves complex hand movements. There are six principal sequential steps for washing hands as per the World Health Organisation (WHO) guidelines. In this work, a preliminary analysis was undertaken in order to develop an automated image processing system for tracking and classification of two-handed dynamic gestures involved in hand washing. To facilitate this study, videos of healthcare workers who were engaged in washing hands were sourced from the internet. The videos were analysed in order to extract the unique features of two-handed gestures associated with all hand hygiene (HH) stages. The combination of these unique …

Segmentation And Registration Based Automatic Cancer Proton Treatment Analysis, Yang Zhang

Open Access Theses & Dissertations

Due to its low side effects, proton therapy is rapidly developing around the world. However, the time and labor it takes to deliver the treatment have prevented widespread use in the clinic setting. For this reason, an automatic proton treatment analysis system is needed to improve efficiency during the treatment process.

The challenge lies in improving the accuracy and speed of the current proton treatment analysis system. The Range shifter correction factor is considered the same value under any given condition in our treatment system. This approximate algorithm limits the accuracy of the proton treatment analysis system. In addition, medical …

Ghost Towns: Semantically Labelled Object Removal From Video, William Clifford, Charles Markham

Session 4: 2D, 3D Scene Analysis and Visualisation

This paper describes a method used to produce a video of a road in which the foreground itemswhich obstruct the view of the road have been removed i.e. other vehicles. Once these regions have been identified they are replaced using suitable images that closely resemble the original background. The work considers an approach that uses multiple video sequences of the same road (C1...Cn). One video is identified as video Cp , that requires the least repair. All instances of vehicles in each frame of video were identified using a Convolutional Neural Network (CNN). The regions associated with each vehicle were …

Benchmarking Image Processing Algorithms For Unmanned Aerial System-Assisted Crack Detection In Concrete Structures, Sattar Dorafshan, Robert J. Thomas, Marc Maguire

Durham School of Architectural Engineering and Construction: Faculty Publications

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six …

A Statistical Approach To Provide Explainable Convolutional Neural Network Parameter Optimization, Saman Akbarzadeh, Selam Ahderom, Kamal Alameh

Research outputs 2014 to 2021

Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications. Finding the best network and optimizing its hyperparameters for a specific application are central challenges for CNNs. Most state-of-the-art CNNs are manually designed, while techniques for automatically finding the best architecture and hyperparameters are computationally intensive, and hence, there is a need to severely limit their search space. This paper proposes a fast statistical method for CNN parameter optimization, which can be applied in many CNN applications …

Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar

Turkish Journal of Electrical Engineering and Computer Sciences

Camera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals. To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast …

Recognition Of Incomplete Objects Based On Synthesis Of Views Using A Geometric Based Local-Global Graphs, Michael Christopher Robbeloth

Browse all Theses and Dissertations

The recognition of single objects is an old research field with many techniques and robust results. The probabilistic recognition of incomplete objects, however, remains an active field with challenging issues associated to shadows, illumination and other visual characteristics. With object incompleteness, we mean missing parts of a known object and not low-resolution images of that object. The employment of various single machine-learning methodologies for accurate classification of the incomplete objects did not provide a robust answer to the challenging problem. In this dissertation, we present a suite of high-level, model-based computer vision techniques encompassing both geometric and machine learning approaches …

On Board Georeferencing Using Fpga-Based Optimized Second Order Polynomial Equation, Dequan Liu, Guoqing Zhou, Jingjin Huang, Rongting Zhang, Lei Shu, Xiang Zhou, Chun Sheng Xin

Electrical & Computer Engineering Faculty Publications

For real-time monitoring of natural disasters, such as fire, volcano, flood, landslide, and coastal inundation, highly-accurate georeferenced remotely sensed imagery is needed. Georeferenced imagery can be fused with geographic spatial data sets to provide geographic coordinates and positing for regions of interest. This paper proposes an on-board georeferencing method for remotely sensed imagery, which contains five modules: input data, coordinate transformation, bilinear interpolation, and output data. The experimental results demonstrate multiple benefits of the proposed method: (1) the computation speed using the proposed algorithm is 8 times faster than that using PC computer; (2) the resources of the field programmable …

Accurate And Compact Stochastic Computations By Exploiting Correlation, Hamdan Abdellatef, Mohamed Khalil Hani, Nasir Shaikh-Husin

Turkish Journal of Electrical Engineering and Computer Sciences

Recent studies have shown, contrary to what was previously believed, that by exploiting correlation in stochastic computing (SC) designs, more accurate SC circuits with low area cost can be realized. However, if these basic SC circuits or blocks are cascaded in series to form a large complex system, correlation between stochastic numbers (SNs) from one block to the next would be lost; thus, inaccuracies are introduced. In this study, we propose correlating circuits to be used in building complex correlated SC systems. One of the circuits is the correlator that restores lost correlations between two SNs due to previous processing. …

A Multiseed-Based Svm Classification Technique For Training Sample Reduction, Imran Sharif, Debasis Chaudhuri

Turkish Journal of Electrical Engineering and Computer Sciences

A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets …

Fundamental Mechanism Of Time Dependent Failure In Shale, Neel Gupta

Graduate Theses, Dissertations, and Problem Reports

In underground coal mines, mining activity disturbs the natural equilibrium state of in-situ stresses. The induced and in-situ stresses deform the rockmass surrounding the mine openings. Primary roof supports impede the deformation of the rockmass overlying the entries. However, failure can occur in the bolted rockmass, causing fatalities and injuries. The rockmass failure is erratic, and its occurrence often varies from a few days to months and years after the opening of the entry. One of the often-neglected factors in this process is the effect of time on the stability of mine openings, which can often be observed in the …

Non-Contact Evaluation Methods For Infrastructure Condition Assessment, Sattar Dorafshan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections.

Spin-Controlled Wavefront Shaping With Plasmonic Chiral Geometric Metasurfaces, Yang Chen, Xiaodong Yang, Jie Gao

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Metasurfaces, as a two-dimensional (2D) version of metamaterials, have drawn considerable attention for their revolutionary capability in manipulating the amplitude, phase, and polarization of light. As one of the most important types of metasurfaces, geometric metasurfaces provide a versatile platform for controlling optical phase distributions due to the geometric nature of the generated phase profile. However, it remains a great challenge to design geometric metasurfaces for realizing spin-switchable functionalities because the generated phase profile with the converted spin is reversed once the handedness of the incident beam is switched. Here, we propose and experimentally demonstrate chiral geometric metasurfaces based on …

Vision-Based Framework For Monitoring Of Eating Behavior Of Persons With Alzheimer’S Disease, Haitham Al-Anssari

Dissertations

Dementia is a syndrome used to describe an array of significant declines in cognitive abilities due to progressive and irreversible loss of neurons and brain functioning. This neurodegeneration seriously affects daily life activities like driving, shopping, working and speaking. Among these, Alzheimer’s disease is the most common type of dementia, with individuals experiencing loss of memory and thinking and reasoning skills. Due to cognitive decline, individuals with Alzheimer’s often suffer from malnutrition, since they do not eat, even when food is presented, and must be fed with assistance. This assistance presents a significant burden of time to caregivers, and consequently …

Ultrafast X-Ray Imaging Of Laser-Metal Additive Manufacturing Processes, Niranjan D. Parab, Cang Zhao, Ross Cunningham, Luis I. Escano, Kamel Fezzaa, Wes Everhart, Anthony D. Rollett, Lianyi Chen, Tao Sun

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The high-speed synchrotron X-ray imaging technique was synchronized with a custom-built laser-melting setup to capture the dynamics of laser powder-bed fusion processes in situ. Various significant phenomena, including vapor-depression and melt-pool dynamics and powder-spatter ejection, were captured with high spatial and temporal resolution. Imaging frame rates of up to 10 MHz were used to capture the rapid changes in these highly dynamic phenomena. At the same time, relatively slow frame rates were employed to capture large-scale changes during the process. This experimental platform will be vital in the further understanding of laser additive manufacturing processes and will be particularly …

Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire

Civil and Environmental Engineering Faculty Publications

This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they …

Segmentation, Tracking, And Kinematics Of Lung Parenchyma And Lung Tumors From 4d Ct With Application To Radiation Treatment Planning., Jungwon Cha

Electronic Theses and Dissertations

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of …