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Skip to Search Results- 10Convolutional Neural Networks
- 4Computer Vision
- 2Machine Learning
- 1Automatic experimental variogram calculation
- 1Automatic variogram modeling
- 1BIM
- 1Abdollahnejad, Sina
- 1Amjad, Faraz
- 1Dietz, Bryson
- 1Fatemi Pour, Farnoosh
- 1Hess, Andy T
- 1Mokdad, Abdelkerim
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An Interactive 3D Visualization Tool to Analyze the Functionality of Convolutional Neural Networks
DownloadFall 2022
Convolutional Neural Networks have outperformed traditional image processing approaches for many image classification tasks. However, despite achieving high classification accuracy on multiple datasets, these machine learning approaches are not transparent and act more like a black box. For this...
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Fall 2019
Velocity analysis can be a time-consuming task when it is performed manually. Methods have been proposed to automate the process of velocity analysis, which, however, typically requires significant manual effort. We propose using the Convolutional Neural Network (CNN) to estimate stacking...
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Fall 2023
Calculating and modeling a variogram from sparsely sampled data can be complex and time-consuming due to the need for expertise in selecting the appropriate parameters and fitting functions to the experimental variogram. To assist with variogram modeling, a novel approach based on Convolutional...
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Fall 2015
Determining the viewpoint (pose) of rigid objects in images is a classic vision problem with applications to robotic grasping, autonomous navigation, augmented reality, semantic SLAM and scene understanding in general. While most existing work is characterized by phrases such as "coarse pose...
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Fall 2019
Object detection is an image processing technology to detection different classes of objects using computer vision, i.e. putting bounding boxes over objects from a camera video feed. A landmark detection method was the Viola-Jones Algorithm introduced in 2001. The object classifier in this...
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Extending Differentiable Programming to include Non-differentiable Modules using Differentiable Bypass for Combining Convolutional Neural Networks and Dynamic Programming into an End-to-end Trainable Framework
DownloadSpring 2019
Differentiable Programming is the paradigm where different functions or modules are combined into a unified pipeline with the purpose of applying end-to-end learning or optimization. A natural impediment is the non-differentiability characteristic of many modules. This thesis proposes a new way...
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Fall 2020
Oil and gas projects are known for their size and complexity and incorporate multiple disciplines such as concrete, steel, and piping. Each discipline is executed within a confined area during a limited timeframe. The execution of each discipline requires careful planning and coordination between...
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Fall 2022
Convolution Neural Networks (CNNs) have rapidly evolved since their neuroscience beginnings. These models efficiently and accurately classify images by optimizing the model’s hidden representations to these images through training. These representa- tions have been shown to resemble neural data...
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Machine Learning for Robust Tracking of Interface Level Inside a Primary Separation Vessel in the Presence of Occlusions and Noise
DownloadSpring 2021
A Primary Separation Vessel (PSV), used in the oil sands industry, is an important process equipment, where Bitumen is separated from the oil sand using a density based separation process. The interface level between a bitumen rich layer (froth) and a layer that has moderate amounts of bitumen in...
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Fall 2020
External beam radiotherapy (EBRT) utilizes high energy radiation (primarily photons or electrons) generated from a linear accelerator (linac) for the treatment of cancer. Current linac technologies lack the ability to continuously monitor the tumour and surround area during treatment. Real-time...