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- 32Computer Vision
- 9Machine Learning
- 7Deep Learning
- 4Convolutional Neural Networks
- 33D Reconstruction
- 3Artificial Intelligence
- 18Department of Computing Science
- 7Department of Electrical and Computer Engineering
- 2Department of Mechanical Engineering
- 1Department of Chemical and Materials Engineering
- 1Department of Civil and Environmental Engineering
- 1Department of Mathematical and Statistical Sciences
The ubiquity of the Internet-of-Things (IoT) devices in everyday life allows various sensors to be utilized in networked systems for solving a number of real-world problems. Models utilizing specific sensing modalities achieve impressive performance in understanding human activity and are used in...
Data augmentation is a strong tool for enhancing the performance of deep learning models using different techniques to increase both the quantity and diversity of training data. Cutout was previously proposed, in the context of image classification, as a simple regularization technique that...
Advancing Forest Health Monitoring: Harnessing the Power of Deep Learning Computer Vision for Remote Sensing ApplicationsDownload
Forests provide immense economic, ecological, and societal values, making forest health monitoring (FHM) a crucial task for guiding conservation and management of these essential ecosystems. Drones have seen increased popularity in this domain due to their ability to collect high-resolution,...
3D reconstruction of quadruped animals is a challenging problem, where key issues lie in their large shape variety and deformation within the same animal species as well as the lack of sufficient training data. In this thesis, we present two approaches toward this task. Our first approach is a...
This thesis applies computer vision and machine learning techniques to three engineering projects: a self-driving vehicle, a predictive display system, and a vision-based robot manipulator joint detector. In the first project, we build a remote-controlled car and implement three core self-driving...
Predicting a dense depth map from LiDAR scans and synced RGB images with a small deep neural network is a challenging task. Most top-accuracy methods boost precision by having a very large number of parameters and as a result huge memory consumption. Whereas, depth completion tasks are commonly...
To achieve a fully autonomous unmanned aerial vehicle (UAV) the vehicle needs a high level of self awareness. At a minimum it needs to know where it is and where it wants to go. Computer vision (CV) is a logical solution to this problem. However, using CV to solve motion control problems for UAVs...
Micropalaeontology, a discipline that contributes to climate research and hydrocarbon exploration, is driven by the taxonomic analysis of huge volumes of microfossils. Unfortunately, this repetitive analysis is a serious bottleneck to progress because it depends on the scarce time of experts....
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...
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...