Physical Sciences and Mathematics Commons^™

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

Computer Vision-Based Traffic Sign Detection And Extraction: A Hybrid Approach Using Gis And Machine Learning, Zihao Wu

Electronic Theses and Dissertations

Traffic sign detection and positioning have drawn considerable attention because of the recent development of autonomous driving and intelligent transportation systems. In order to detect and pinpoint traffic signs accurately, this research proposes two methods. In the first method, geo-tagged Google Street View images and road networks were utilized to locate traffic signs. In the second method, both traffic signs categories and locations were identified and extracted from the location-based GoPro video. TensorFlow is the machine learning framework used to implement these two methods. To that end, 363 stop signs were detected and mapped accurately using the first method (Google …

Mathematical Modeling For Partial Object Detection., Ahmed Reda Amin El-Barkouky

Electronic Theses and Dissertations

From a computer vision point of view, the image is a scene consisting of objects of interest and a background represented by everything else in the image. The relations and interactions among these objects are the key factors for scene understanding. In this dissertation, a mathematical model is designed for the detection of partially occluded faces captured in unconstrained real life conditions. The proposed model novelty comes from explicitly considering certain objects that are common to occlude faces and embedding them in the face model. This enables the detection of faces in difficult settings and provides more information to subsequent …

A Study Of Localization And Latency Reduction For Action Recognition, Syed Zain Masood

Electronic Theses and Dissertations

The success of recognizing periodic actions in single-person-simple-background datasets, such as Weizmann and KTH, has created a need for more complex datasets to push the performance of action recognition systems. In this work, we create a new synthetic action dataset and use it to highlight weaknesses in current recognition systems. Experiments show that introducing background complexity to action video sequences causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning system parameters or by selecting better feature points. Instead, we show that the problem lies in the spatio-temporal cuboid volume extracted from the interest point …

Gradient Based Mrf Learning For Image Restoration And Segmentation, Kegan Samuel

Electronic Theses and Dissertations

The undirected graphical model or Markov Random Field (MRF) is one of the more popular models used in computer vision and is the type of model with which this work is concerned. Models based on these methods have proven to be particularly useful in low-level vision systems and have led to state-of-the-art results for MRF-based systems. The research presented will describe a new discriminative training algorithm and its implementation. The MRF model will be trained by optimizing its parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth. While previous work has …

High Dynamic Range Imaging For The Detection Of Motion., Jeffrey Robert Hay

Electronic Theses and Dissertations

High dynamic range imaging involves imaging at a bit depth higher than the typical 8-12 bits offered by standard video equipment. We propose a method of imaging a scene at high dynamic range, 14+ bits, to detect motion correlated with changes in the measured optical signal. Features within a scene, namely edges, can be tracked through a time sequence and produce a modulation in light levels associated with the edge moving across a region being sampled by the detector. The modulation in the signal is analyzed and a model is proposed that allows for an absolute measurement of the displacement …

Markerless Tracking Using Polar Correlation Of Camera Optical Flow, Prince Gupta

Electronic Theses and Dissertations

We present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of optical flow leading to an efficient tracking algorithm that captures five degrees of freedom including direction of translation and angular velocity. Experiments comparing our device with an electromagnetic tracker show that its average tracking accuracy is 80% over 185 frames, and it is …

Spatio-Temporal Maximum Average Correlation Height Templates In Action Recognition And Video Summarization, Mikel Rodriguez

Electronic Theses and Dissertations

Action recognition represents one of the most difficult problems in computer vision given that it embodies the combination of several uncertain attributes, such as the subtle variability associated with individual human behavior and the challenges that come with viewpoint variations, scale changes and different temporal extents. Nevertheless, action recognition solutions are critical in a great number of domains, such video surveillance, assisted living environments, video search, interfaces, and virtual reality. In this dissertation, we investigate template-based action recognition algorithms that can incorporate the information contained in a set of training examples, and we explore how these algorithms perform in action …

Multi-View Approaches To Tracking, 3d Reconstruction And Object Class Detection, Saad Khan

Electronic Theses and Dissertations

Multi-camera systems are becoming ubiquitous and have found application in a variety of domains including surveillance, immersive visualization, sports entertainment and movie special effects amongst others. From a computer vision perspective, the challenging task is how to most efficiently fuse information from multiple views in the absence of detailed calibration information and a minimum of human intervention. This thesis presents a new approach to fuse foreground likelihood information from multiple views onto a reference view without explicit processing in 3D space, thereby circumventing the need for complete calibration. Our approach uses a homographic occupancy constraint (HOC), which states that if …

Detecting Curved Objects Against Cluttered Backgrounds, Jan Prokaj

Electronic Theses and Dissertations

Detecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-level features into weak classifiers, which then produce a strong classifier using AdaBoost. The resulting …

Towards A Self-Calibrating Video Camera Network For Content Analysis And Forensics, Imran Junejo

Electronic Theses and Dissertations

Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how …

Using Linear Features For Aerial Image Sequence Mosaiking, Caixia Wang

Electronic Theses and Dissertations

With recent advances in sensor technology and digital image processing techniques, automatic image mosaicking has received increased attention in a variety of geospatial applications, ranging from panorama generation and video surveillance to image based rendering. The geometric transformation used to link images in a mosaic is the subject of image orientation, a fundamental photogrammetric task that represents a major research area in digital image analysis. It involves the determination of the parameters that express the location and pose of a camera at the time it captured an image. In aerial applications the typical parameters comprise two translations (along the x …

Depth From Defocused Motion, Zarina Myles

Electronic Theses and Dissertations

Motion in depth and/or zooming causes defocus blur. This work presents a solution to the problem of using defocus blur and optical flow information to compute depth at points that defocus when they move. We first formulate a novel algorithm which recovers defocus blur and affine parameters simultaneously. Next we formulate a novel relationship (the blur-depth relationship) between defocus blur, relative object depth and three parameters based on camera motion and intrinsic camera parameters. We can handle the situation where a single image has points which have defocused, got sharper or are focally unperturbed. Moreover, our formulation is valid regardless …

Detection And Generalization Of Spatio-Temporal Trajectories For Motion Imagery, Panayotis Partsinevelos

Electronic Theses and Dissertations

In today's world of vast information availability users often confront large unorganized amounts of data with limited tools for managing them. Motion imagery datasets have become increasingly popular means for exposing and disseminating information. Commonly, moving objects are of primary interest in modeling such datasets. Users may require different levels of detail mainly for visualization and further processing purposes according to the application at hand. In this thesis we exploit the geometric attributes of objects for dataset summarization by using a series of image processing and neural network tools. In order to form data summaries we select representative time instances …

Preprocessing Of Microcirculatory Images., Mohamed S. Mansour

Electronic Theses and Dissertations

This thesis project consists of the development of a computerized image processing system to preprocess microvascular images of the mouse Latisimus Dorsi Muscle (LDM). This research has been conducted in association with the Division of Plastic and Reconstructive Surgery at the University of Louisville. The input to the system is a set of 35 or more overlapping microscopic fields or sub_images containing segments of the LDM microcirculation, the output is an adjacent single montage encompassing the entire LDM microvasculature. The developed system presents practical solutions to the problems of image registration, overlap resolution, and image segmentation, in addition, the results …