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Automatic Pain Assessment Through Facial Expressions, Ilham Seladji
Automatic Pain Assessment Through Facial Expressions, Ilham Seladji
Theses
Pain is a strong symptom of diseases. Being an involuntary unpleasant feeling, it can be considered as a reliable indicator of health issues. Pain has always been expressed verbally, but in some cases, traditional patient self-reporting is not efficient. On one side, there are patients who have neurological disorders and cannot express themselves accurately, as well as patients who suddenly lose consciousness due to an abrupt faintness. On another side, medical staff working in crowded hospitals need to focus on emergencies and would opt for the automation of the task of looking after hospitalized patients during their entire stay, in …
Open Set Classification For Deep Learning In Large-Scale And Continual Learning Models, Ryne Roady
Open Set Classification For Deep Learning In Large-Scale And Continual Learning Models, Ryne Roady
Theses
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers require the ability to recognize inputs from outside the training set as unknowns and update representations in near real-time to account for novel concepts unknown during offline training. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition; however, for convolutional neural networks, there have been two major approaches: 1) inference methods to separate known inputs from unknown inputs and 2) feature space regularization …
Point Completion Networks And Segmentation Of 3d Mesh, Naga Durga Harish Kanamarlapudi
Point Completion Networks And Segmentation Of 3d Mesh, Naga Durga Harish Kanamarlapudi
Theses
Deep learning has made many advancements in fields such as computer vision, natural language processing and speech processing. In autonomous driving, deep learning has made great improvements pertaining to the tasks of lane detection, steering estimation, throttle control, depth estimation, 2D and 3D object detection, object segmentation and object tracking. Understanding the 3D world is necessary for safe end-to-end self-driving. 3D point clouds provide rich 3D information, but processing point clouds is difficult since point clouds are irregular and unordered. Neural point processing methods like GraphCNN and PointNet operate on individual points for accurate classification and segmentation results. Occlusion of …
Context Sensitive Image Denoising And Enhancement Using U-Nets, Sahaj Tushar Gandhi
Context Sensitive Image Denoising And Enhancement Using U-Nets, Sahaj Tushar Gandhi
Theses
Noise in images gets introduced at almost every stage of the camera image signal processing pipeline (ISP). Camera companies provide software that cleans most of the noise added at each stage. Even after noise removal is done by the camera software, different noise patterns with different intensities remain in the image. With advances in deep learning, the algorithms are archi- tectured end-to-end. In the present time, machine learning and deep learning models work as end-to-end systems with a special-purpose feature extraction phase. This thesis focuses on the removal of any residual noise in images as performed during the feature extraction …
Gaze Estimation Based On Multi-View Geometric Neural Networks, Devarth Parikh
Gaze Estimation Based On Multi-View Geometric Neural Networks, Devarth Parikh
Theses
Gaze and head pose estimation can play essential roles in various applications, such as human attention recognition and behavior analysis. Most of the deep neural network-based gaze estimation techniques use supervised regression techniques where features are extracted from eye images by neural networks and regress 3D gaze vectors. I plan to apply the geometric features of the eyes to determine the gaze vectors of observers relying on the concepts of 3D multiple view geometry. We develop an end to-end CNN framework for gaze estimation using 3D geometric constraints under semi-supervised and unsupervised settings and compare the results. We explore the …
Self-Supervision Initialization For Semantic Segmentation Networks, Kenneth Alexopoulos
Self-Supervision Initialization For Semantic Segmentation Networks, Kenneth Alexopoulos
Theses
Convolutional neural networks excel at extracting features from signals. These features are able to be utilized for many downstream tasks. These tasks include object recognition, object detection, depth estimation, pixel level semantic segmentation, and more. These tasks can be used for applications such as autonomous driving where images captured by a camera can be used to give a detailed understanding of the scene. While these models are impressive, they can fail to generalize to new environments. This forces the cumbersome process of collecting images from multifarious environments and annotating them by hand. Annotating thousands or millions of images is both …
Clearing The Clouds: Extracting 3d Information From Amongst The Noise, Alexander Fafard
Clearing The Clouds: Extracting 3d Information From Amongst The Noise, Alexander Fafard
Theses
Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation …
High-Capacity Directional Graph Networks, Miguel Dominguez
High-Capacity Directional Graph Networks, Miguel Dominguez
Theses
Deep Neural Networks (DNN) have proven themselves to be a useful tool in many computer vision problems. One of the most popular forms of the DNN is the Convolutional Neural Network (CNN). The CNN effectively learns features on images by learning a weighted sum of local neighborhoods of pixels, creating filtered versions of the image. Point cloud analysis seems like it would benefit from this useful model. However, point clouds are much less structured than images. Many analogues to CNNs for point clouds have been proposed in the literature, but they are often much more constrained networks than the typical …
Self-Supervised Learning For Segmentation Using Image Reconstruction, Srivallabha Karnam
Self-Supervised Learning For Segmentation Using Image Reconstruction, Srivallabha Karnam
Theses
Deep learning is the engine that is piloting tremendous growth in various segments of the industry by consuming valuable fuel called data. We are witnessing many businesses adopting this technology be it healthcare, transportation, defense, semiconductor, or retail. But most of the accomplishments that we see now rely on supervised learning. Supervised learning needs a substantial volume of labeled data which are usually annotated by humans- an arduous and expensive task often leading to datasets that are insufficient in size or human labeling errors. The performance of deep learning models is only as good as the data. Self-supervised learning minimizes …
Ar Comic Chat, Dylan Bowald
Ar Comic Chat, Dylan Bowald
Theses
Live speech transcription and captioning are important for the accessibility of deaf and hard of hearing individuals, especially in situations with no visible ASL translators. If live captioning is available at all, it is typically rendered in the style of closed captions on a display such as a phone screen or TV and away from the real conversation. This can potentially divide the focus of the viewer and detract from the experience. This paper proposes an investigation into an alternative, Augmented Reality driven approach to the display of these captions, using deep neural networks to compute, track and associate deep …
Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning, Robert Relyea
Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning, Robert Relyea
Theses
Autonomous agents in any environment require accurate and reliable position and motion estimation to complete their required tasks. Many different sensor modalities have been utilized for this task such as GPS, ultra-wide band, visual simultaneous localization and mapping (SLAM), and light detection and ranging (LiDAR) SLAM. Many of the traditional positioning systems do not take advantage of the recent advances in the machine learning field. In this work, an omnidirectional camera position estimation system relying primarily on a learned model is presented. The positioning system benefits from the wide field of view provided by an omnidirectional camera. Recent developments in …
Advancing Multi-Modal Deep Learning: Towards Language-Grounded Visual Understanding, Kushal Kafle
Advancing Multi-Modal Deep Learning: Towards Language-Grounded Visual Understanding, Kushal Kafle
Theses
Using deep learning, computer vision now rivals people at object recognition and detection, opening doors to tackle new challenges in image understanding. Among these challenges, understanding and reasoning about language grounded visual content is of fundamental importance to advancing artificial intelligence. Recently, multiple datasets and algorithms have been created as proxy tasks towards this goal, with visual question answering (VQA) being the most widely studied. In VQA, an algorithm needs to produce an answer to a natural language question about an image. However, our survey of datasets and algorithms for VQA uncovered several sources of dataset bias and sub-optimal evaluation …
Information Discovery In Coronagraph Images, Jeren Suzuki
Information Discovery In Coronagraph Images, Jeren Suzuki
Theses
Three areas of research for information discovery in solar coronal mass ejections (CMEs) are presented. These include CME leading front detection, clustering approaches on CME catalog data, and neural network-based classication of CMEs into speed categories. In addition to describing explored methodologies, the experimental results and analyses are presented.