Digital Commons Network^™

Understanding High Resolution Aerial Imagery Using Computer Vision Techniques, Fan Wang

Theses

Computer vision can make important contributions to the analysis of remote sensing satellite or aerial imagery. However, the resolution of early satellite imagery was not sufficient to provide useful spatial features. The situation is changing with the advent of very-high-spatial-resolution (VHR) imaging sensors. This change makes it possible to use computer vision techniques to perform analysis of man-made structures. Meanwhile, the development of multi-view imaging techniques allows the generation of accurate point clouds as ancillary knowledge.

This dissertation aims at developing computer vision and machine learning algorithms for high resolution aerial imagery analysis in the context of application problems including …

The Emotional Impact Of Audio - Visual Stimuli, Titus Pallithottathu Thomas

Theses

Induced affect is the emotional effect of an object on an individual. It can be quantified through two metrics: valence and arousal. Valance quantifies how positive or negative something is, while arousal quantifies the intensity from calm to exciting. These metrics enable researchers to study how people opine on various topics. Affective content analysis of visual media is a challenging problem due to differences in perceived reactions. Industry standard machine learning classifiers such as Support Vector Machines can be used to help determine user affect. The best affect-annotated video datasets are often analyzed by feeding large amounts of visual and …

Visual-Linguistic Semantic Alignment: Fusing Human Gaze And Spoken Narratives For Image Region Annotation, Preethi Vaidyanathan

Theses

Advanced image-based application systems such as image retrieval and visual question answering depend heavily on semantic image region annotation. However, improvements in image region annotation are limited because of our inability to understand how humans, the end users, process these images and image regions. In this work, we expand a framework for capturing image region annotations where interpreting an image is influenced by the end user's visual perception skills, conceptual knowledge, and task-oriented goals. Human image understanding is reflected by individuals' visual and linguistic behaviors, but the meaningful computational integration and interpretation of their multimodal representations (e.g. gaze, text) remain …