Computer Engineering Commons^™

Analyzing The Benthic Cover Of Crustose Coralline Algae Using Mask-R Cnn, Rachana Ravindra

Master's Projects

Coral reefs, supporting 25% of marine biodiversity, confront challenges from local and global impacts like overfishing, runoff, acidification, and warming. Crustose Coralline Algae (CCA), pivotal for reef structure and coral settlement, are underrepresented in research. Current methods like Coral Point Count with Excel Extensions (CPCe) have limitations, relying on image quality and being time-consuming. This paper proposes computer vision and Mask R-CNN, a supervised machine learning model, for CCA analysis in reef images, considering color, texture, and shape. Results indicate promise in clustering and classifying organisms. The innovative technology reduces manual labor, enhancing image analysis, simplifying the understanding of CCA’s …

Detection Of Various Dental Conditions On Dental Panoramic Radiography Using Faster R-Cnn, Shih Lun Chen, Tsung Yi Chen, Yi Cheng Mao, Szu Yin Lin, Ya Yun Huang, Chiung An Chen, Yuan Jin Lin, Mian Heng Chuang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% …

Research On Visual Inspection Algorithm Of Crimping Appearance Defects For Wiring Harness Terminals, Bingan Yuan, Mingen Zhong, Jingxin Ni

Journal of System Simulation

Abstract: Aiming at the low efficiency and high missing rate of wiring harness terminals, an image detection method based on machine vision is proposed. The characteristic parameters of five typical defects in three main parts of wiring harness terminals are analyzed and defined. Tthe algorithms of extracting positioning datum, segmenting inspected-parts adaptively, extracting the defect features and calculating the characteristic parameters are designed respectively, and the defects criterions are given. The experimental results show that the algorithms are suitable for single defect and multi-class defects, both the miss detection rate and the false positiveness rate are low. The accuracy and …

Monocular Semantic Slam Method Based On Object Relation Description, Shiqi Lin, Jikai Wang, Haoyuan Pei, Hao Zhao, Zonghai Chen

Journal of System Simulation

Abstract: Semantic information perception of the external environment and accurate positioning are the keys to autonomous navigation and operation of mobile robots. This paper proposes a method of semantic simultaneous localization and mapping (SLAM) based on a monocular camera. The system completes three-dimensional (3D) object detection while estimating the trajectory. We model the 3D objects with cuboids. Then, the semantic meanings, color distribution, size and neighborhood topology of the objects are extracted as descriptors for the accurate matching of objects between different frames. The camera pose, map points and object landmarks are optimized jointly in the backend of the system. …

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

Machine Learning-Based Recognition On Crowdsourced Food Images, Aditya Kulkarni

Honors Scholar Theses

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further.

Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in …

Infrared Image Segmentation Of Aircraft Skin Based On Otsu And Improved I-Ching Divination Evolutionary Algorithm, Kun Wang, Ji Yao, Peilun Liu, Wang Li

Journal of System Simulation

Abstract: Infrared thermal imaging non-destructive testing is one of the commonly used methods for aircraft skin detection. Aiming at Otsu's large computational complexity and poor real-time performance, an aircraft skin infrared image segmentation method based on Otsu and an improved I-Ching divination evolutionary algorithm (IDEA) is proposed. The roulette selection operator is improved by using roulette selection for the I-Ching map of state size 3n, from which the n individuals with the maximum fitness values are then selected as new populations. The experimental results show that the proposed algorithm is superior to several other improved optimization algorithms both in terms …

Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed …

Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided …

Color-Texture Image Segmentation Approach Designed By Integrating Geodesic Active Contour Model And Multilayer Graph Cut Model, Yang Yong, Liangren Zheng, Guo Ling, Yangdong Ye

Journal of System Simulation

Abstract: An approach of color-texture image segmentation was proposed based on geodesic active contour model and multilayer graph cut model. As the clustering centers were commonly descripted with constant densities, the two phase Chan-vese model was extended to multiphase geodesic active contour model by using the Gaussian distribution to describe the changing density for each phase, and meanwhile the geodesic active contour was added into the proposed model, so that the presented approach could capture out the concave edge. For the minimization of the proposed energy function, a corresponded multilayer graph cut model was designed for resolving global approximated …

Regional Credible Fusion Based Color-Texture Image Segmentation Approach, Yang Yong, Guo Ling, Wenzheng Dai, Yangdong Ye

Journal of System Simulation

Abstract: An approach is proposed based on the combined color information and texture information for color-texture image segmentation. The compacted multi-scale texture information is extracted by using singular value decomposition and principal component analysis dimension reduction under decomposing the multi-scale structure tensor, and then it is integrated with scale information and color information for improving the description ability to color-texture. To avoid the phenomenon such as over-segmentation and error segmentation appeared, the regional credible fusion degree is computed by combining four kinds of region information, such as region adjacency relationship, region size, common edge between regions, and J-divergence distance. Meanwhile, …

Automatic Hair Contour Extraction Method With Complex Background, Yaoli Jin, Shiliang Wang, Xu Gang, Weihua Hu, Yigang Wang

Journal of System Simulation

Abstract: Hair contour extraction has important application in digital entertainment and simulation. An automatic hair contour extraction method for images with complex background was proposed. Image segmentation algorithm from foreground image was used to eliminate the interference of background image; face recognition and hair detection algorithms were employed to find the hair position and realize the automatic identification of the hair; in order to improve the accuracy of contour extraction, adaptive skin detection algorithm was used to eliminate the interference of face. The proposed method was applied on a lot of image examples. Experimental results demonstrate that this method can …

Image Segmentation And Offset Correction Based On Minimal Relative Entropy Theoryand Level Set Method, Xiuqiang Pan, Jinxiao Shan, Caifeng Yang

Journal of System Simulation

Abstract: The new variational level set method is achieved with the combination of the traditional level set method and the energy function which is established by means of statistical model according to the minimal relative entropy.The new method isappliedto object segmentation and offset correction in intensity heterogeneous image.Object segmentation and offset correction are unified according to the evolution of the level set function, anda deviation estimation function with intrinsic smooth feature is obtained.The results prove that the overlapping areas between different tissues are significantly decreased and more accurate results are achieved. In addition, this model is not …

Novel Automatic Pavement Crack Detection Algorithm, Shangbing Gao, Xie Zheng, Zhigeng Pan, Fangzhe Qin, Li Rui

Journal of System Simulation

Abstract: The complexity of noises covers a wide area of actual road images which causes that it is difficult to detect cracks. An automatic pavement crack detection algorithm was proposed in view of the characteristics of crack image in pavement disease. Gray-scale correction and filtering was used to preprocess the crack image. The maximum interclass variance method and Canny operator were used to detect the edge of the disease image, and then the localization and accurate segmentation algorithm was proposed for the crack image based on the maximum connectivity of the crack in the fracture image. The convolution neural network …

Semantic Segmentation Of Aerial Imagery Using U-Nets, Terence J. Yi

Theses and Dissertations

In situations where global positioning systems are unavailable, alternative methods of localization must be implemented. A potential step to achieving this is semantic segmentation, or the ability for a model to output class labels by pixel. This research aims to utilize datasets of varying spatial resolutions and locations to train a fully convolutional neural network architecture called the U-Net to perform segmentations of aerial images. Variations of the U-Net architecture are implemented and compared to other existing models in order to determine the best in detecting buildings and roads. A final dataset will also be created combining two datasets to …

Freelabel: A Publicly Available Annotation Tool Based On Freehand Traces, Philipe A. Dias, Zhou Shen, Amy Tabb, Henry P. Medeiros

Electrical and Computer Engineering Faculty Research and Publications

Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks recently achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed …

Overview Of Image Segmentation And Registration For Spine Biological Modeling, Juping Gu, Tianyu Cheng, Hua Liang, Jianping Wang, Zhao Jian, Cao Yong

Journal of System Simulation

Abstract: The prevalence of common spinal fractures is high in this day. Spinal image segmentation and registration are the key step and prerequisite of target recognition, biomechanical modeling and finite element analysis. They are also the key technology of noninvasive surgery navigation. Aiming at the problems of spinal image processing, the classical medical image segmentation and registration algorithm is introduced. It also analyzes the defects and prospects of the future development trend of spine image processing. The research results have certain significance for further understanding of spinal medical image processing and promoting the development of rehabilitation therapy for spinal fractures.

A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun

Electronic Theses and Dissertations

The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The proposed methods consist of two phases. …

Breast Boundary Segmentation In Thermography Images Based On Random Walkers, Mehrdad Moghbel, Syamsiah Mashohor, Rozi Mahmud, M.Iqbal Bin Saripan, Suzana Abd Hamid, Suraini Mohamad Sani, Saiful Nizam

Turkish Journal of Electrical Engineering and Computer Sciences

Breast and areola boundary detection and segmentation present the biggest challenge in breast segmentation from thermography images, as breast boundaries, especially in the upper quadrants of the breast, are nonexistent. Many segmentation approaches have been proposed for breast segmentation, such as active contours and snakes, circular Hough transforms, and live wires, but these methods often fail to achieve satisfactory results. With recent advances in image processing techniques, new segmentation concepts are being developed, such as random walkers, which have received high interest from the medical imaging community. In this study, 91 images acquired utilizing a FLIR A320 thermal camera are …

An Adaptive Clustering Segmentation Algorithm Based On Fcm, Jun Yang, Yun-Sheng Ke, Mao-Zheng Wang

Turkish Journal of Electrical Engineering and Computer Sciences

The cluster number and the initial clustering centers must be reasonably set before the analysis of clustering in most cases. Traditional clustering segmentation algorithms have many shortcomings, such as high reliance on the specially established initial clustering center, tendency to fall into the local maximum point, and poor performance with multithreshold values. To overcome these defects, an adaptive fuzzy C-means segmentation algorithm based on a histogram (AFCMH), which synthesizes both main peaks of the histogram and optimized Otsu criterion, is proposed. First, the main peaks of the histogram are chosen by operations like histogram smoothing, merging of adjacent peaks, and …

Image Segmentation Using Fuzzy-Spatial Taxon Cut, Lauren Barghout

MODVIS Workshop

Images convey multiple meanings that depend on the context in which the viewer perceptually organizes the scene. This presents a problem for automated image segmentation, because it adds uncertainty to the process of selecting which objects to include or not include within a segment. I’ll discuss the implementation of a fuzzy-logic-natural-vision-processing engine that solves this problem by assuming the scene architecture prior to processing. The scene architecture, a standardized natural-scene-perception-taxonomy comprised of a hierarchy of nested spatial-taxons. Spatial-taxons are regions (pixel-sets) that are figure-like, in that they are perceived as having a contour, are either `thing-like', or a `group of …

Image Segmentation For Quantification Of Air-Water Interface In Micro-Ct Soil Images, Kranthi Kumar Potteti

UNLV Theses, Dissertations, Professional Papers, and Capstones

Soils are complex environments comprising various biological (roots, water, air etc) and physical constituents (minerals, aggregates, etc). Synchrotron radiation based X-ray microtomography (XMT) is widely used in extracting qualitative and quantitative information regarding spatial distribution of biological and physical soil constituents. Segmentation of these micro-CT soil images is of interest to geologists, hydrologists, civil and petroleum engineers and soil scientists. In this present work, we study and implement segmentation algorithms for microhydrology studies, specifically for soil water conductivity. Three well-known image segmentation algorithms are studied for evaluating their performance for the task. We demonstrate the problems and ways to segment …

Automated Visual Monitoring Of Machining Equipment, Timothy Wayne Ragland

Masters Theses

Metalworking equipment is designed to modify a sheet, rod, or block of metal material in order to shape it for a specific application. This equipment can operate on the metal by bending it, drilling through it, or by cutting it. For small-scale operations, many tools require a significant amount of manual input. Unless the operator has extensive training and experience, the manual input may not be precise enough for fine details that may be needed in some applications. For example, with a bending brake, obtaining an accurate angle for the bend may be quite difficult. For a particular application, an …

An Investigation Into Segmenting Traffic Images Using Various Types Of Graph Cuts, Jonathan Dinger

All Theses

In computer vision, graph cuts are a way of segmenting an image into multiple areas. Graphs are built using one node for each pixel in the image combined with two extra nodes, known as the source and the sink. Each node is connected to several other nodes using edges, and each edge has a specific weight. Using different weighting schemes, different segmentations can be performed based on the properties used to create the weights. The cuts themselves are performed using an implementation of a solution to the maximum flow problem, which is then changed into a minimum cut according to …

Semi-Automatic Image Segmentation For Volumetric Visualization Of Pelvis Ct Scan-Images, Suprijanto Suprijanto, Farida I. Muchtadi, Irwan Setiawan

Makara Journal of Technology

Semi-Automatic Image Segmentation for Volumetric Visualization of Pelvis CT Scan-Images. The current development of computerized tomography (CT) has enable us to obtain cross sectional image using multi slicing techniques in an order of few seconds. The obtained images represent several tissue structures on cross section slice being imaged. One challenge to help diagnosis using CT images is extracting an anatomic structure of interest using a method of image segmentation and volumetric visualization with the assistance of computers. In case of volumetric visualization of pelvis bones extracted from multi-slice CT images, whole images which are containing part of pelvis bone structures …

Ballot Mark Detection, Elisa H. Barney Smith, Daniel Lopresti, George Nagy

Electrical and Computer Engineering Faculty Publications and Presentations

Optical mark sensing, i.e., detecting whether a "bubble" has been filled in, may seem straightforward. However, on US election ballots the shape, intensity, size and position of the marks, while specified, are highly variable due to a diverse electorate. The ballots may be produced and scanned by poorly maintained equipment. Yet near-perfect results are required. To improve the current technology, which has been subject to criticism, components of a process for identifying marks on an optical sense ballot are evaluated. When marked synthetic ballots are compared to an unmarked ballot, the absolute difference of adaptive thresholded images gives best detection …

Segmentation Of Overlapping Particles In Automatic Size Analysis Using Multi-Flash Imaging, Tze K Koh, Nicholas Miles, Steve Morgan, Barrie Hayes-Gill

Research Collection College of Integrative Studies

In this paper, we propose a novel hardware approach to image segmentation, specifically in the case of overlapping particles. Our research is based on multi-flash imaging (MFI), originally developed to detect depth discontinuities. Multiple images captured with different illumination conditions provide additional information about a scene compared to conventional segmentation techniques. Shadows are used to identify true object edges and underlying particles. We applied the new approach in automated particle size analysis and evaluated it against the watershed and canny edge detection techniques. Evaluation results confirm that MFI can be applied in image segmentation and reveals the superiority of the …

An Algorithm For Image Clustering And Compression, Meti̇n Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a new approach to image compression based on fuzzy clustering. This new approach includes pre-filtering, and fuzzy logic image enhancing to reduce undesirable noise effects on segmentation result; separation of image into 4x4 blocks and two dimensional discrete cosine transform; obtaining of peak values of cosine membership functions by combining of performing the zig-zag method with discrete cosine transform coefficients; obtaining of membership values and cluster centroids; and finally, creation of segmented image and compression. After applying the new method on sample images at different number of clusters, better compression ratio, performing time and good validity measure …

Image Segmentation With Ratio Cut, Song Wang, Jeffrey Mark Siskind

Faculty Publications

This paper proposes a new cost function, cut ratio, for segmenting images using graph-based methods. The cut ratio is defined as the ratio of the corresponding sums of two different weights of edges along the cut boundary and models the mean affinity between the segments separated by the boundary per unit boundary length. This new cost function allows the image perimeter to be segmented, guarantees that the segments produced by bipartitioning are connected, and does not introduce a size, shape, smoothness, or boundary-length bias. The latter allows it to produce segmentations where boundaries are aligned with image edges. Furthermore, the …

Estimation Of Error In Large Area Underwater Photomosaics Using Vehicle Navigation Data, C. Roman, H. Singh

Christopher N. Roman

Creating geometrically accurate photomosaics of underwater sites using images collected from an AUV or ROV is a difficult task due to dimensional errors which grow as a function of 3D image distortion and the mosaicking process. Although photomosiacs are accurate locally their utility for accurately representing a large survey area is jeopardized by this error growth. Evaluating the error in a mosaic is the first step in creating globally accurate photomosaics of an unstructured environment with bounded error. Using vehicle navigation data and sensor offsets it is possible to estimate the error present in large area photomosaics independent of the …