Theory and Algorithms Commons^™

Detection Of Phase-Binning And Interpolation Artifacts In 4-Dimensional Computed Tomography Imaging Using Deep Learning And Rule-Based Approaches, Jorge Cisneros, Nathan H. Feldt, Yevgeniy Vinogradskiy, Richard Castillo, Edward Castillo

Department of Radiation Oncology Faculty Papers

BACKGROUND: Four-dimensional computed tomography (4DCT) imaging is a crucial component to lung cancer radiotherapy planning and enables CT-ventilation-based functional avoidance planning to mitigate radiation toxicity. However, 4DCT scans are frequently impaired by acquisition artifacts that corrupt downstream analyses that depend on lung segmentation and deformable image registration, such as CT-ventilation and dose accumulation.

PURPOSE: This study develops 3D deep learning models to identify phase-binning artifacts at the voxel level and a heuristic, rule-based method to identify interpolation slices within 4DCT images.

METHODS: We introduce a generator that systematically inserts synthetic phase-binning and interpolation artifacts into any artifact-free breathing phase obtained …

Noise-Embedded Image Processing Based On Quantum Data Encodings, Yayu Mo

Multidisciplinary Studies Theses and Dissertations

Advancements in quantum information have significantly impacted the field of image processing, although challenges remain. Especially in the edge detection and image encoding area, distorted feature and noises would affect the further classification or super resolution tasks. In our work, we conduct researches on two stages to both evaluate the potential of Quantum-based Convolutional Structure in extracting distorted feature and further explore the effects of quantum noise channels on quantum image encodings.

In the first stage, we propose a method to extract distorted edge features by applying shallow layers in quantum convolutional neural networks (QCNN). By combining the advantages of …

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

Acquisition, Processing, And Analysis Of Video, Audio And Meteorological Data In Multi-Sensor Electronic Beehive Monitoring, Sarbajit Mukherjee

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In recent years, a widespread decline has been seen in honey bee population and this is widely attributed to colony collapse disorder. Hence, it is of utmost importance that a system is designed to gather relevant information. This will allow for a deeper understanding of the possible reasons behind the above phenomenon to aid in the design of suitable countermeasures.

Electronic Beehive Monitoring is one such way of gathering critical information regarding a colony’s health and behavior without invasive beehive inspections. In this dissertation, we have presented an electronic beehive monitoring system called BeePi that can be placed on top …

Structure-Priority Image Restoration Through Genetic Algorithm Optimization, Zhaoxia Wang, Haibo Pen, Ting Yang, Quan Wang

Research Collection School Of Computing and Information Systems

With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based …

Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …

Object Detection Using Contrast Enhancement And Dynamic Noise Reduction, Justin Lee Baker

UNLV Theses, Dissertations, Professional Papers, and Capstones

Edge detection is one of the most important steps a computer must perform to gain understanding of an object in a digital image either from disk or from video feed. Edge detection allows for the computer to describe the shape of the objects in an image and create a pixel boundary defining what is considered part of an object, and what is not. Cannys edge detection algorithm is one of the most robust and accurate of these edge detection algorithms. However, as with many algorithms in image processing, there are many cases where the algorithm does not perform as well …

Real Time Digital Night Vision Using Nonlinear Contrast Enhancement, Nishikar Sapkota

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis describes a nonlinear contrast enhancement technique to implement night vision in digital video. It is based on the global histogram equalization algorithm. First, the effectiveness of global histogram equalization is examined for images taken in low illumination environments in terms of Peak signal to noise ratio (PSNR) and visual inspection of images. Our analysis establishes the existence of an optimum intensity for which histogram equalization yields the best results in terms of output image quality in the context of night vision. Based on this observation, an incremental approach to histogram equalization is developed which gives better results than …

Image Processing Algorithms For Improving Planetary Exploration And Understanding, Ali Pouryazdanpanah

College of Engineering: Graduate Celebration Programs

• To design a fully automated tool-set that allows to detect and extract the sky region in planetary images.

• To develop the new method for rock segmentation in planetary stereo images.
• To develop the new method for shadow detection in planetary images

Mathematical Model Development Of Super-Resolution Image Wiener Restoration, Amr H. Yousef, Jiang Li, Mohammad A. Karim

Electrical & Computer Engineering Faculty Publications

In super-resolution (SR), a set of degraded low-resolution (LR) images are used to reconstruct a higher-resolution image that suffers from acquisition degradations. One way to boost SR images visual quality is to use restoration filters to remove reconstructed images artifacts. We propose an efficient method to optimally allocate the LR pixels on the high-resolution grid and introduce a mathematical derivation of a stochastic Wiener filter. It relies on the continuous-discrete-continuous model and is constrained by the periodic and nonperiodic interrelationships between the different frequency components of the proposed SR system. We analyze an end-to-end model and formulate the Wiener filter …

Fast Stochastic Wiener Filter For Super-Resolution Image Restoration With Information Theoretic Visual Quality Assessment, Amr Hussein Yousef, Jiang Li, Mohammad Karim, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. The reconstructed image suffers from degradations such as blur, aliasing, photo-detector noise and registration and fusion error. Wiener filter can be used to remove artifacts and enhance the visual quality of the reconstructed images. In this paper, we introduce a new fast stochastic Wiener filter for SR reconstruction and restoration that can be implemented efficiently in the frequency domain. Our derivation depends on the continuous-discrete-continuous (CDC) model that represents most of the degradations encountered during the image-gathering …

Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli

Electrical & Computer Engineering Theses & Dissertations

Demands for reliable person identification systems have increased significantly due to highly security risks in our daily life. Recently, person identification systems are built upon the biometrics techniques such as face recognition. Although face recognition systems have reached a certain level of maturity, their accomplishments in practical applications are restricted by some challenges, such as illumination variations. Current visual face recognition systems perform relatively well under controlled illumination conditions while thermal face recognition systems are more advantageous for detecting disguised faces or when there is no illumination control. A hybrid system utilizing both visual and thermal images for face recognition …

Expression Invariant Face Recognition Using Shifted Phase-Encoded Joint Transform Correlation Technique, Trisha Ahmed

Electrical & Computer Engineering Theses & Dissertations

A new face recognition algorithm using a synthetic discriminant function based shifted phase-encoded fringe-adjusted joint transform correlation (SDF-SPFJTC) technique is proposed. The dark region in an input image is enhanced by using a nonlinear technique named ratio enhancement in gaussian neighborhood (REIGN). Histogram equalization and Gaussian smoothing are then performed to the enhanced face images and the synthetic discriminant function (SDF) image before they are subjected to the joint transform correlation process. The two distinct correlation peaks produced on extreme ends of the SPFJTC plane signifies the recognition of a potential target. A post processing step utilizes the peak-to-clutter ratio …

Robust Edge-Detection Algorithm For Runway Edge Detection, Swathi Tandra

Electrical & Computer Engineering Theses & Dissertations

Fog and other such weather conditions hamper the visibility of runway surfaces and any obstacles present on the runway, creating a situation where a pilot may not be able to safely land the aircraft. Assisting the pilot to land the aircraft safely in such conditions is an active area of research. A new method is being investigated that combines non-linear image enhancement with classification of runway edges to detect objects on the runway. The image is segmented into runway and non-runway regions, and objects that are found in the runway regions are deemed to constitute potential hazards. For runway edge …

Object Detection In Poor Visibility Conditions Using Image Segmentation, Triveni Vuppalapati

Electrical & Computer Engineering Theses & Dissertations

It can be dangerous for a pilot to attempt to land an aircraft safely in poor visibility conditions such as rain, fog, haze, snow and low light, but external imagery of a runway can be enhanced to provide increased situational awareness. Objects that are detected in a scene may or may not be a hazard, so interpretation is left to the pilot. In order to detect whether an object is a hazard to safe landing, it is necessary to determine whether the object is on the runway. For this purpose, two image segmentation algorithms: histogram based d-peak algorithm and local …

Image Pre-Processing Techniques For Hazard Detection In Poor Visibility Conditions, Girish Singh Rajput

Electrical & Computer Engineering Theses & Dissertations

Runway incursion is a persistent problem that has resulted in some of the most devastating accidents in aviation history. With ever increasing air traffic and more passengers, runway safety is of utmost priority to the Federal Aviation Administration (FAA) and other agencies concerned with aviation. As the issue of aviation safety becomes increasingly important, developing a consistent application that detects runway incursions in various visibility conditions is crucial for the aviation industry. This thesis presents a novel method for detecting runway hazards in poor visibility conditions using image processing techniques. The first step is to obtain images of a runway …

An Adaptive And Non-Linear Technique For Enhancement Of High Contrast Images, Saibabu Arigela

Electrical & Computer Engineering Theses & Dissertations

In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. Neither the object details in the low intensity areas nor in the high intensity areas can be clearly interpreted. Several image processing techniques have been developed to retrieve meaningful information under low lighting conditions. The algorithm based on integrated neighborhood dependency of pixel characteristics, and that based on the illuminance reflectance model perform well for improving the visual quality of digital images captured under extremely low and nonuniform lighting conditions. But these techniques cannot perform well in …

A Wavelet Based Complementary Approach For Image Enhancement, Ismail Kosum

Electrical & Computer Engineering Theses & Dissertations

Detail in an image means more meaningful information that is very important in many computer vision and pattern recognition applications. The object region visibility in an image plays an important role in obtaining accurate and desired information from the original image. In particular, image processing techniques developed for region segmentation and object classification have better results depending on the visibility in images. There are several enhancement techniques available which are capable of obtaining clear images with balanced lighting and contrast. In this thesis, a completely image dependent approach to enhance the luminance of images under extreme lighting conditions and a …

A Multilane Pipelined Architecture For Real Time Enhancement Of Color Video Streams, Adam Redd Livingston

Electrical & Computer Engineering Theses & Dissertations

Video stream enhancement is a key fixture in a wide variety of applications from video surveillance, automatic navigation, medical imagery, to facial/object recognition systems. When a video stream contains non-uniform lighting it can be difficult to obtain what is in the darker regions without over enhancing brighter regions. The Adaptive and Integrated Neighborhood Dependant Approach for Nonlinear Enhancement (AINDANE) algorithm combines a tunable nonlinear transfer function, convolution by a multi-scale Gaussian kernel, and tunable contrast enhancement to address this problem for a single image. Luminance values are tuned based on the global cumulative distribution function (CDF) of an image. Contrast …

A Non-Linear Technique For The Enhancement Of Extremely Non-Uniform Lighting Images, Ender Oguslu

Electrical & Computer Engineering Theses & Dissertations

At night scenes, either the low intensity areas that are under poor light or the high intensity areas that are overexposed cannot be clearly seen. Various image processing techniques have been developed to recover the meaningful information under extremely low lighting conditions. Among these, the algorithms based on integrated neighborhood dependency of pixel characteristics and based on the illuminance reflectance model perform well for improving the visual quality of digital images captured under nonuniform and extremely low lighting conditions. Although these techniques perform well in low lighting conditions, they cannot perform well in overexposed regions under dark environments such as …

Determination Of Structure From Motion Using Aerial Imagery, Paul R. Graham

Theses and Dissertations

The structure from motion process creates three-dimensional models from a sequence of images. Until recently, most research in this field has been restricted to land-based imagery. This research examines the current methods of land-based structure from motion and evaluates their performance for aerial imagery. Current structure from motion algorithms search the initial image for features to track though the subsequent images. These features are used to create point correspondences between the two images. The correspondences are used to estimate the motion of the camera and then the three-dimensional structure of the scene. This research tests current algorithms using synthetic data …

Fast And Space-Efficient Location Of Heavy Or Dense Segments In Run-Length Encoded Sequences, Ronald I. Greenberg

Computer Science: Faculty Publications and Other Works

This paper considers several variations of an optimization problem with potential applications in such areas as biomolecular sequence analysis and image processing. Given a sequence of items, each with a weight and a length, the goal is to find a subsequence of consecutive items of optimal value, where value is either total weight or total weight divided by total length. There may also be a specified lower and/or upper bound on the acceptable length of subsequences. This paper shows that all the variations of the problem are solvable in linear time and space even with non-uniform item lengths and divisible …

Translation And Rotation Invariant Multiscale Image Registration, Jennifer L. Manfra

Theses and Dissertations

The most recent research involved registering images in the presence of translations and rotations using one iteration of the redundant discrete wavelet transform. We extend this work by creating a new multiscale transform to register two images with translation or rotation differences, independent of scale differences between the images. Our two-dimensional multiscale transform uses an innovative combination of lowpass filtering and the continuous wavelet transform to mimic the two-dimensional redundant discrete wavelet transform. This allows us to obtain multiple subbands at various scales while maintaining the desirable properties of the redundant discrete wavelet transform. Whereas the discrete wavelet transform produces …

An Objective Evaluation Of Four Sar Image Segmentation Algorithms, Jason B. Gregga

Theses and Dissertations

Because of the large number of SAR images the Air Force generates and the dwindling number of available human analysts, automated methods must be developed. A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods. This thesis evaluates four SAR image segmentation algorithms by running them on a common set of data and objectively comparing them to each other and to human segmentors. This objective comparison uses a multi-metric a approach with a set of …

Automatic Target Cueing Of Hyperspectral Image Data, Terry A. Wilson

Theses and Dissertations

Modern imaging sensors produce vast amounts data, overwhelming human analysts. One such sensor is the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The AVIRIS sensor simultaneously collects data in 224 spectral bands that range from 0.4µm to 2.5µm in approximately 10nm increments, producing 224 images, each representing a single spectral band. Autonomous systems are required that can fuse "important" spectral bands and then classify regions of interest if all of this data is to be exploited. This dissertation presents a comprehensive solution that consists of a new physiologically motivated fusion algorithm and a novel Bayes optimal self-architecting classifier …

A Computational Paradigm On Network-Based Models Of Computation, Venkatavasu Bokka

Computer Science Theses & Dissertations

The maturation of computer science has strengthened the need to consolidate isolated algorithms and techniques into general computational paradigms. The main goal of this dissertation is to provide a unifying framework which captures the essence of a number of problems in seemingly unrelated contexts in database design, pattern recognition, image processing, VLSI design, computer vision, and robot navigation. The main contribution of this work is to provide a computational paradigm which involves the unifying framework, referred to as the multiple Query problem, along with a generic solution to the Multiple Query problem.

To demonstrate the applicability of the paradigm, a …

Visual Speech Recognition Using Multiple Deformable Lip Models, Devi Chandramohan

Electrical & Computer Engineering Theses & Dissertations

Motivated by the fact that human speech perception is a bimodal process (auditory and visual), several researchers have designed and implemented automatic speech recognition (ASR) systems consisting of both audio and visual subsystems, and shown improved performance relative to traditional purely auditory systems. Several visual speech reading approaches have used deformable templates to model the shape of a speaker's lips. Deformable templates are models of image objects, which can be deformed by adjusting a set of parameters to match the object in some optimal way, as defined by a cost function. Using a single deformable lip model has disadvantages such …

Face Recognition With The Karhunen-Loeve Transform, Pedro F. Suarez

Theses and Dissertations

The major goal of this research was to investigate machine recognition of faces. The approach taken to achieve this goal was to investigate the use of Karhunen-Loe've Transform (KLT) by implementing flexible and practical code. The KLT utilizes the eigenvectors of the covariance matrix as a basis set. Faces were projected onto the eigenvectors, called eigenfaces, and the resulting projection coefficients were used as features. Face recognition accuracies for the KLT coefficients were superior to Fourier based techniques. Additionally, this thesis demonstrated the image compression and reconstruction capabilities of the KLT. This theses also developed the use of the KLT …

Optical Machine Recognition Of Lower-Case Greek Characters Of Any Size, Ivan X. D. D'Cunha

Electrical & Computer Engineering Theses & Dissertations

An algorithm utilizing a syntactic approach and a criterion based on normalized moments is defined for the reliable, automatic, machine recognition of handwritten and printed Greek characters of any size and font. In this approach a binary image of the character in question is obtained initially; its skeleton is then produced by utilizing a standard thinning algorithm. The classification process then incorporates the topological features of the characters such as existence of closed curves, number of intersections, number and location of free ends, axial symmetry, and the criteria derived from normalized moments to uniquely identify each pattern. Experiments conducted demonstrated …

Wind Streamline Ambiguity Removal Of Microwave Scatterometer Data, Hans W. Zaepfel

Electrical & Computer Engineering Theses & Dissertations

There is a class of satellite microwave scatterometers that return oceanographic information which includes wind vectors with directional ambiguity. The first part of the ambiguity removal process is to reduce the data to a directional ambiguity of 180°. This thesis shows that wind direction change contains the information required for streamline ambiguity removal and that syntactic pattern recognition techniques can be used to locate the areas of wind direction change. Spreading the information along the areas of wind direction change results in the removal of the streamline ambiguity. The algorithm is implemented and the results are presented showing that it …