Computer Engineering Commons^™

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% …

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 …

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 …

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 …

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 …

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 …