Engineering Commons^™

A Novel Synergistic Diagnosis Methodology For Identifying Abnormalities In Wireless Capsule Endoscopy Videos, Alexandros Karargyris

Browse all Theses and Dissertations

Wireless Capsule Endoscopy (WCE) is a new technology that allows medical personnel to view the gastrointestinal (GI) mucosa. It is a swallowable miniature capsule device the size of a pill that transmits thousands of screenshots of the digestive tract to a wearable receiver. When the procedure finishes the video is uploaded to a workstation for viewing. Capsule Endoscopy has been established as a tool to identify various gastrointestinal (GI) conditions, such as blood-based abnormalities, polyps, ulcers, Crohn's disease in the small intestine, where the classical endoscopy is not regularly used.

As of 2009 the market is dominated by Given Imaging …

Structural Health Monitoring With Emphasis On Computer Vision, Damage Indices, And Statistical Analysis, Ricardo Zaurin

Electronic Theses and Dissertations

Structural Health Monitoring (SHM) is the sensing and analysis of a structure to detect abnormal behavior, damage and deterioration during regular operations as well as under extreme loadings. SHM is designed to provide objective information for decision-making on safety and serviceability. This research focuses on the SHM of bridges by developing and integrating novel methods and techniques using sensor networks, computer vision, modeling for damage indices and statistical approaches. Effective use of traffic video synchronized with sensor measurements for decision-making is demonstrated. First, some of the computer vision methods and how they can be used for bridge monitoring are presented …

Evaluation Of An Image Processing Algorithm For Scene Change Detection, Daniel Flores

Open Access Theses & Dissertations

Despite the efforts to reduce traffic accidents by government entities and automobile manufactures, the numbers of accidents are not considerably reduced. An evaluation of an algorithm based on the fly's eye is done on this research applying its principles to detect scene change on images for printed circuit boards providing initial steps to implement this algorithm on vehicular traffic to keep track of objects moving on the field for collision avoidance purposes. The effectiveness of the algorithm is addressed through a comparison of its performance with that found in experimental data.

A Survey Of Neural Computation On Graphics Processing Hardware, Ryan J. Meuth, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. From machine vision to finite element analysis, CPU's are being used in diverse applications, collectively called general purpose graphics processor utilization. This paper explores the capabilities and limitations of modern GPU's and surveys the neural computation technologies that have been applied to these devices.

Object Association Across Multiple Moving Cameras In Planar Scenes, Yaser Sheikh

Electronic Theses and Dissertations

In this dissertation, we address the problem of object detection and object association across multiple cameras over large areas that are well modeled by planes. We present a unifying probabilistic framework that captures the underlying geometry of planar scenes, and present algorithms to estimate geometric relationships between different cameras, which are subsequently used for co-operative association of objects. We first present a local1 object detection scheme that has three fundamental innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of …

Melanoma And Seborrheic Keratosis Differentiation Using Texture Features, Srinivas V. Deshabhoina, Scott E. Umbaugh, William V. Stoecker, Randy Hays Moss, Subhashini K. Srinivasan

Chemistry Faculty Research & Creative Works

Purpose: To explore texture features in two-dimensional images to differentiate seborrheic keratosis from melanoma.

Methods: A systematic approach to consistent classification of skin tumors is described. Texture features, based on the second-order histogram, were used to identify the features or a combination of features that could consistently differentiate a malignant skin tumor (melanoma) from a benign one (seborrheic keratosis). Two hundred and seventy-one skin tumor images were separated into training and test sets for accuracy and consistency. Automatic induction was applied to generate classification rules. Data analysis and modeling tools were used to gain further insight into the feature space. …

A Biologically Inspired Connectionist Architecture For Directing Attention To Salient Visual Field Objects, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

In an attempt to incorporate basic visual attention abilities into existing artificial vision systems, a neural model of the bidirectional interactions within and between the brain regions believed to be involved in human visual attention has been developed. This model currently gives an artificial vision system the ability to attend to salient, or pop-out features and objects within the vision system''s field of view. After a review of the physiology of human visual attention, a network model of the aforementioned neural interactions is presented, followed by a demonstration of its performance.

Performance Of Ai Methods In Detecting Melanoma, Arve Kjoelen, M. J. Thompson, Scott E. Umbaugh, Randy Hays Moss, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

This research has shown that features extracted from color skin tumor images by computer vision methods can be reliable discriminators of malignant tumors from benign ones. Reliability was demonstrated by the monotonically increasing success ratios with increasing training set size and by the small standard deviations from the mean success rates. An average success rate of 70 percent in diagnosing melanoma was attained for a training set size of 60 percent. The presence or absence of atypical moles in the training and test sets was shown to have a dramatic impact on the effectiveness of the generated classification rules. This …

Moving Object Recognition And Guidance Of Robots Using Neural Networks, Abhijit Neogy, S. N. Balakrishnan, Cihan H. Dagli

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The design of a robust guidance system for a robot is discussed. The two major tasks for this guidance system are the online recognition of a moving object invariant to rotation and translation, and tracking the moving object using a neural-network-driven vision system. This system included computer software ported to the IBM PC and interfaced with an IBM 7535 robot. The operation of this guidance system involved recognition of a moving object and the ability to track it till the robot and effector was in close proximity of the object. It was found that the robot was able to track …

Integrated Neural Network And Machine Vision Approach For Intelligent State Identification, Cihan H. Dagli, Timothy Andrew Bauer

Engineering Management and Systems Engineering Faculty Research & Creative Works

An interfacing of neural networks (NNs) and machine vision to provide the next state of a system given an image of the present state of the system is presented. This interfacing is applied to a loading operation. First, a NN is trained for part recognition under conditions of rotation, location, object distortion, and background noise given an image of the part. Then, a second NN, given the output of the first NN and an image of a pallet being loaded, is trained for optimal part loading onto the pallet under conditions of noise in the image. The paradigm used is …

Applying Artificial Intelligence To The Identification Of Variegated Coloring In Skin Tumors, Scott E. Umbaugh, Randy Hays Moss, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

The importance of color information for the automatic diagnosis of skin tumors by computer vision is demonstrated. The utility of the relative color concept is proved by the results in identifying variegated coloring. A feature file paradigm is shown to provide an effective methodology for the independent development of software modules for expert system/computer vision research. An automatic induction tool is used effectively to generate rules for identifying variegated coloring. Variegated coloring can be identified at rates as high as 92% when using the automatic induction technique in conjunction with the color segmentation method

A Stereo Vision Technique Based On The Multi-Positioned Camera Criterion, Jie Bao

Theses

A modified feature based stereo vision [sic] technique is described in this thesis. The technique uses the curve-segments as the feature primitives in the matching process. The local characteristics of the curve-segments are extracted by the, Generalized Hough Transform.

A set of images of a scene, which are taken by a multi-positioned camera satisfying the parallelism criterion, are first filtered by the Laplacian of a Gaussian operator in different widths, i.e. coarse to fine channels. At each channel, the Generalized Hough Transform is applied to the curve-segments in each image. The curve position, the curve-length, the curve centroid, the average …

Automatic Color Segmentation Of Images With Application To Detection Of Variegated Coloring In Skin Tumors, Scott E. Umbaugh, Randy Hays Moss, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

A description is given of a computer vision system, developed to serve as the front-end of a medical expert system, that automates visual feature identification for skin tumor evaluation. The general approach is to create different software modules that detect the presence or absence of critical features. Image analysis with artificial intelligence (AI) techniques, such as the use of heuristics incorporated into image processing algorithms, is the primary approach. On a broad scale, this research addressed the problem of segmentation of a digital image based on color information. The algorithm that was developed to segment the image strictly on the …