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Full-Text Articles in Artificial Intelligence and Robotics
Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad
Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad
Graduate Theses, Dissertations, and Problem Reports
Categorizing neurons into different types to understand neural circuits and ultimately brain function is a major challenge in neuroscience. While electrical properties are critical in defining a neuron, its morphology is equally important. Advancements in single-cell analysis methods have allowed neuroscientists to simultaneously capture multiple data modalities from a neuron. We propose a method to classify neurons using both morphological structure and electrophysiology. Current approaches are based on a limited analysis of morphological features. We propose to use a new graph neural network to learn representations that more comprehensively account for the complexity of the shape of neuronal structures. In …
Deep Fingerprint Matching From Contactless To Contact Fingerprints For Increased Interoperability, Alexander James Wilson
Deep Fingerprint Matching From Contactless To Contact Fingerprints For Increased Interoperability, Alexander James Wilson
Graduate Theses, Dissertations, and Problem Reports
Contactless fingerprint matching is a common form of biometric security today. Most smartphones and associated apps now let users opt into using this form of biometric security. However, it’s difficult to match a finger-photo to a fingerprint because of perspective distortion occurring at the edges of the finger-photo, so direct matching using conventional methods will not be as accurate due to a lack of sufficient matching minutiae points. To address this issue, we propose a deep model, Perspective Distortion Rectification Model (PDRM), to estimate the fingerprint correspondence for finger-photo images in order to recover more minutiae points. Not only do …
Single And Differential Morph Attack Detection, Baaria Chaudhary
Single And Differential Morph Attack Detection, Baaria Chaudhary
Graduate Theses, Dissertations, and Problem Reports
Face recognition systems operate on the assumption that a person's face serves as the unique link to their identity. In this thesis, we explore the problem of morph attacks, which have become a viable threat to face verification scenarios precisely because of their inherent ability to break this unique link. A morph attack occurs when two people who share similar facial features morph their faces together such that the resulting face image is recognized as either of two contributing individuals. Morphs inherit enough visual features from both individuals that both humans and automatic algorithms confuse them. The contributions of this …
Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan
Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan
Graduate Theses, Dissertations, and Problem Reports
People with Autism Spectrum Disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories but was particularly successful at classifying photos of people with >80% accuracy. Importantly, the visualization of our model revealed critical features that led …