UIST ’25, Busan, Republic of Korea

Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements. To tackle this challenge, we introduce a high-dimensional neural representation of blur-the lens blur field-and a practical method for acquiring it. The lens blur field is a multilayer perceptron (MLP) designed to (1) accurately capture variations of the lens 2D point spread function over image plane location, focus setting and, optionally, depth and (2) represent these variations parametrically as a single, sensor-specific function. The representation models the combined effects of defocus, diffraction, aberration, and accounts for sensor features such as pixel color filters and pixel-specific micro-lenses. To learn the real-world blur field of a given device, we formulate a generalized non-blind deconvolution problem that directly optimizes the MLP weights using a small set of focal stacks as the only input. We also provide a first-of-its-kind dataset of 5D blur fields-for smartphone cameras, camera bodies equipped with a variety of lenses, etc. Lastly, we show that acquired 5D blur fields are expressive and accurate enough to reveal, for the first time, differences in optical behavior of smartphone devices of the same make and model.

Inspired by artistic practices such as beadwork and himmeli, we study the problem of threading a single string through a set of tubes, so that pulling the string forms a desired graph. More precisely, given a connected graph (where edges represent tubes and vertices represent junctions where they meet), we give a polynomial-time algorithm to find a minimum-length closed walk (representing a threading of string) that induces a connected graph of string at every junction. The algorithm is based on a surprising reduction to minimum-weight perfect matching. Along the way, we give tight worst-case bounds on the length of the optimal threading and on the maximum number of times this threading can visit a single edge. We also give more efficient solutions to two special cases: cubic graphs and the case when each edge can be visited at most twice.

Many sketch processing applications target precise vector drawings with accurately specified stroke intersections, yet free-form artist drawn sketches are typically inexact: strokes that are intended to intersect often stop short of doing so. While human observers easily perceive the artist intended stroke connectivity, manually, or even semi-manually, correcting drawings to generate correctly connected outputs is tedious and highly time consuming. We propose a novel, robust algorithm that extracts viewer-perceived stroke connectivity from inexact free-form vector drawings by leveraging observations about local and global factors that impact human perception of inter-stroke connectivity. We employ the identified local cues to train classifiers that assess the likelihood that pairs of strokes are perceived as forming end-to-end or T- junctions based on local context. We then use these classifiers within an incremental framework that combines classifier provided likelihoods with a more global, contextual and closure-based, analysis. We demonstrate our method on over 95 diversely sourced inputs, and validate it via a series of perceptual studies; participants prefer our outputs over the closest alternative by a factor of 9 to 1.

With the growing importance of dermoscopic analysis to diagnose skin diseases, the vocabulary of dermoscopy has rapidly expanded without standardized control. Many metaphoric terms are ambiguous and create barriers for general research and education. Ontologies are computable artifacts that represent and model information from a domain space that can later be leveraged by software to understand domain knowledge. We aim to standardize dermoscopic vocabulary through the introduction of two ontologies: 1. the Elements of Visuals Ontology (EVO), a foundational ontology to decompose the visual elements of physical entities; and 2. the Dermoscopy Elements of Visuals Ontology (DEVO), a domain ontology that harnesses EVO to formalize the definitions of dermoscopic metaphoric terms. We discuss how DEVO would enhance both trainee education and patient care, with the future goal of generating responses to queries about dermoscopic features and integrating these features with diagnostic rules for skin diseases.

Emotions influence our perceptions and decisions and are often felt more strongly in situations related to healthcare. Therefore, it is important to understand how both providers and patients express their emotions in face-to-face scenarios. An ontology is a way to represent domain concepts and the relationships between them in a polyarchical manner. We have created an ontological model called the Visualized Emotion Ontology (VEO) that expresses the semantic definitions and visualizations of 25 emotions based on published research. With VEO, we can augment patient-facing software tools, like embodied conversational agents, to improve patient-provider interaction in clinical environments.

Abstract We explored the social influence of humor in a virtual human counselor's self‐disclosure while also varying the ethnicity of the virtual counselor. In a 2 × 3 experiment (humor and ethnicity of the virtual human counselor), participants experienced counseling interview interactions via Skype on a smartphone. We measured user responses to and perceptions of the virtual human counselor. The results demonstrate that humor positively affects user responses to and perceptions of a virtual counselor. The results further suggest that matching styles of humor with a virtual counselor's ethnicity influences user responses and perceptions. The results offer insight into the effective design and development of realistic and believable virtual human counselors. Furthermore, they illuminate the potential use of humor to enhance self‐disclosure in human–agent interactions.