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Full-Text Articles in Electrical and Computer Engineering
Learning Domain Invariant Information To Enhance Presentation Attack Detection In Visible Face Recognition Systems, Jennifer Hamblin
Learning Domain Invariant Information To Enhance Presentation Attack Detection In Visible Face Recognition Systems, Jennifer Hamblin
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multi-spectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack …
An Analysis Of Camera Configurations And Depth Estimation Algorithms For Triple-Camera Computer Vision Systems, Jared Peter-Contesse
An Analysis Of Camera Configurations And Depth Estimation Algorithms For Triple-Camera Computer Vision Systems, Jared Peter-Contesse
Master's Theses
The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of …
An End-To-End Trainable Method For Generating And Detecting Fiducial Markers, J Brennan Peace
An End-To-End Trainable Method For Generating And Detecting Fiducial Markers, J Brennan Peace
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Existing fiducial markers are designed for efficient detection and decoding. The methods are computationally efficient and capable of demonstrating impressive results, however, the markers are not explicitly designed to stand out in natural environments and their robustness is difficult to infer from relatively limited analysis. Worsening performance in challenging image capture scenarios - such as poorly exposed images, motion blur, and off-axis viewing - sheds light on their limitations. The method introduced in this work is an end-to-end trainable method for designing fiducial markers and a complimentary detector. By introducing back-propagatable marker augmentation and superimposition into training, the method learns …
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Electronic Thesis and Dissertation Repository
Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …
Optimal Compression Of Point Clouds, Benjamin Robert Smith
Optimal Compression Of Point Clouds, Benjamin Robert Smith
Graduate Theses, Dissertations, and Problem Reports
Image-based localization is a crucial step in many 3D computer vision applications, e.g., self-driving cars, robotics, and augmented reality among others. Unfortunately, many image-based-localization applications require the storage of large scenes, and many camera pose estimators struggle to scale when the scene representation is large. To alleviate the aforementioned problems, many applications compress a scene representation by reducing the number of 3D points of a point cloud. The state-of-the-art compresses a scene representation by using a K-cover-based algorithm. While the state-of-the-art selects a subset of 3D points that maximizes the probability of accurately estimating the camera pose of a new …
Lionfish Detection System, Carmelo Furlan, Andrew Boniface
Lionfish Detection System, Carmelo Furlan, Andrew Boniface
Computer Engineering
Deep neural networks have proven to be an effective method in classification of images. The ability to recognize objects has opened the door for many new systems which use image classification to solve challenging problems where conventional image classification would be inadequate. We trained a large, deep convolutional neural network to identify lionfish from other species that might be found in the same habitats. Google’s Inception framework served as a powerful platform for our fish recognition system. By using transfer learning, we were able to obtain exceptional results for the classification of different species of fish. The convolutional neural network …
Figure-Ground Organization Using 3d Symmetry, Aaron Michaux, Vijai Jayadevan, Edward Delp, Zygmunt Pizlo
Figure-Ground Organization Using 3d Symmetry, Aaron Michaux, Vijai Jayadevan, Edward Delp, Zygmunt Pizlo
MODVIS Workshop
We present a novel approach to object localization using mirror symmetry as a general purpose and biologically motivated prior. 3D symmetry leads to good segmentation because (i) almost all objects exhibit symmetry, and (ii) configurations of objects are not likely to be symmetric unless they share some additional relationship. Furthermore, psychophysical evidence suggests that the human vision system makes use symmetry in constructing 3D percepts, indicating that symmetry may be important in object localization. No general purpose approach is known for solving 3D symmetry correspondence in 2D camera images, because few invariants exist. Therefore, to test symmetry as a clustering …
Hybrid Single And Dual Pattern Structured Light Illumination, Minghao Wang
Hybrid Single And Dual Pattern Structured Light Illumination, Minghao Wang
Theses and Dissertations--Electrical and Computer Engineering
Structured Light Illumination is a widely used 3D shape measurement technique in non-contact surface scanning. Multi-pattern based Structured Light Illumination methods reconstruct 3-D surface with high accuracy, but are sensitive to object motion during the pattern projection and the speed of scanning process is relatively long. To reduce this sensitivity, single pattern techniques are developed to achieve a high speed scanning process, such as Composite Pattern (CP) and Modified Composite Pattern (MCP) technique. However, most of single patter techniques have a significant banding artifact and sacrifice the accuracy. We focus on developing SLI techniques can achieve both high speed, high …