Timothy Gerstner

Bio: Timothy Gerstner is an academic researcher from Rutgers University. The author has contributed to research in topics: Pixel & Palette (computing). The author has an hindex of 4, co-authored 4 publications receiving 94 citations.

Pixelated image abstraction.

Abstract: We present an automatic method that can be used to abstract high resolution images into very low resolution outputs with reduced color palettes in the style of pixel art. Our method simultaneously solves for a mapping of features and a reduced palette needed to construct the output image. The results are an approximation to the results generated by pixel artists. We compare our method against the results of a naive process common to image manipulation programs, as well as the hand-crafted work of pixel artists. Through a formal user study and interviews with expert pixel artists we show that our results offer an improvement over the naive methods.

Pixelated image abstraction

Abstract: We present an automatic method that can be used to abstract high resolution images into very low resolution outputs with reduced color palettes in the style of pixel art. Our method simultaneously solves for a mapping of features and a reduced palette needed to construct the output image. The results are an approximation to the results generated by pixel artists. We compare our method against the results of a naive process common to image manipulation programs, as well as the hand-crafted work of pixel artists. Through a formal user study and interviews with expert pixel artists we show that our results offer an improvement over the naive methods.

Agency and rationality: Adopting the intentional stance toward evolved virtual agents

Abstract: The interpretation of other agents as intentional actors equipped with mental states has been connected to the attribution of rationality to their behavior. But a workable definition of “rationality” is difficult to formulate in complex situations, where standard normative definitions are difficult to apply. In this study, we explore a notion of rationality based on the idea of evolutionary fitness. We ask whether agents that are more adapted to their environment are, consequently, perceived as more rational and intentional. We created a 2-D virtual environment populated with autonomous virtual agents, each of which behaves according to a built-in program equipped with simulated perception, memory, and decision making. We then introduced a process of simulated evolution that pressured the agents’ programs toward behavior more adapted to the simulated environment. We showed these agents to human subjects in 2 experiments, in which we respectively asked them to judge their intelligence and to dynamically estimate their “mental states.” The results confirm that subjects construed evolved agents as more intelligent, and judged evolved agents’ mental states more accurately, relative to nonevolved agents. These results corroborate a view that the interpretation of agent behavior is connected to a concept of rationality based on the apparent fit between an agent’s actions and its environment.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Neural Style Transfer: A Review

Abstract: The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at this https URL.

Integrating Personality Structure, Personality Process, and Personality Development:

Abstract: In this target article, we argue that personality processes, personality structure, and personality development have to be understood and investigated in integrated ways in order to provide compreh...

Image and Video-based Artistic Stylisation

Abstract: Non-photorealistic rendering (NPR) is a combination of computer graphics and computer vision that produces renderings in various artistic, expressive or stylized ways such as painting and drawing. This book focuses on image and video based NPR, where the input is a 2D photograph or a video rather than a 3D model. 2D NPR techniques have application in areas as diverse as consumer and professional digital photography and visual effects for TV and film production. The book covers the full range of the state of the art of NPR with every chapter authored by internationally renowned experts in the field, covering both classical and contemporary techniques. It will enable both graduate students in computer graphics, computer vision or image processing and professional developers alike to quickly become familiar with contemporary techniques, enabling them to apply 2D NPR algorithms in their own projects.

ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments

Abstract: Fast and robust pupil detection is an essential prerequisite for video-based eye-tracking in real-world settings. Several algorithms for image-based pupil detection have been proposed, their applicability is mostly limited to laboratory conditions. In realworld scenarios, automated pupil detection has to face various challenges, such as illumination changes, reflections (on glasses), make-up, non-centered eye recording, and physiological eye characteristics. We propose ElSe, a novel algorithm based on ellipse evaluation of a filtered edge image. We aim at a robust, resource-saving approach that can be integrated in embedded architectures e.g. driving. The proposed algorithm was evaluated against four state-of-the-art methods on over 93,000 hand-labeled images from which 55,000 are new images contributed by this work. On average, the proposed method achieved a 14.53% improvement on the detection rate relative to the best state-of-the-art performer. download:this ftp URL de (password:eyedata).