ClearPhoto - augmented photography

The widespread use of mobile devices has made known to the general public new areas that were hitherto confined to specialized devices. In general, the smartphone came to give all users the ability to execute multiple tasks, and among them, take photographs using the integrated cameras. Although these devices are continuously receiving improved cameras, their manufacturers do not take advantage of their full potential, since the operating systems normally offer simple APIs and applications for shooting. Therefore, taking advantage of this environment for mobile devices, we find ourselves in the best scenario to develop applications that help the user obtaining a good result when shooting. In an attempt to provide a set of techniques and tools more applied to the task, this dissertation presents, as a contribution, a set of tools for mobile devices that provides information in real-time on the composition of the scene before capturing an image. Thus, the proposed solution gives support to a user while capturing a scene with a mobile device. The user will be able to receive multiple suggestions on the composition of the scene, which will be based on rules of photography or other useful tools for photographers. The tools include horizon detection and graphical visualization of the color palette presented on the scenario being photographed. These tools were evaluated regarding the mobile device implementation and how users assess their usefulness

COMPUTATIONAL MODELLING OF HUMAN AESTHETIC PREFERENCES IN THE VISUAL DOMAIN: A BRAIN-INSPIRED APPROACH

Following the rise of neuroaesthetics as a research domain, computational aesthetics has also known a regain in popularity over the past decade with many works using novel computer vision and machine learning techniques to evaluate the aesthetic value of visual information. This thesis presents a new approach where low-level features inspired from the human visual system are extracted from images to train a machine learning-based system to classify visual information depending on its aesthetics, regardless of the type of visual media. Extensive tests are developed to highlight strengths and weaknesses of such low-level features while establishing good practices in the domain of study of computational aesthetics. The aesthetic classification system is not only tested on the most widely used dataset of photographs, called AVA, on which it is trained initially, but also on other photographic datasets to evaluate the robustness of the learnt aesthetic preferences over other rating communities. The system is then assessed in terms of aesthetic classification on other types of visual media to investigate whether the learnt aesthetic preferences represent photography rules or more general aesthetic rules. The skill transfer from aesthetic classification of photos to videos demonstrates a satisfying correct classification rate of videos without any prior training on the test set created by Tzelepis et al. Moreover, the initial photograph classifier can also be used on feature films to investigate the classifier’s learnt visual preferences, due to films providing a large number of frames easily labellable. The study on aesthetic classification of videos concludes with a case study on the work by an online content creator. The classifier recognised a significantly greater percentage of aesthetically high frames in videos filmed in studios than on-the-go. The results obtained across datasets containing videos of diverse natures manifest the extent of the system’s aesthetic knowledge. To conclude, the evolution of low-level visual features is studied in popular culture such as in paintings and brand logos. The work attempts to link aesthetic preferences during contemplation tasks such as aesthetic rating of photographs with preferred low-level visual features in art creation. It questions whether favoured visual features usage varies over the life of a painter, implicitly showing a relationship with artistic expertise. Findings display significant changes in use of universally preferred features over influential vi abstract painters’ careers such an increase in cardinal lines and the colour blue; changes that were not observed in landscape painters. Regarding brand logos, only a few features evolved in a significant manner, most of them being colour-related features. Despite the incredible amount of data available online, phenomena developing over an entire life are still complicated to study. These computational experiments show that simple approaches focusing on the fundamentals instead of high-level measures allow to analyse artists’ visual preferences, as well as extract a community’s visual preferences from photos or videos while limiting impact from cultural and personal experiences

Incorporating characteristics of human creativity into an evolutionary art algorithm (journal article)

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically

Semi-automated level design via auto-playtesting for handheld casual game creation

We provide a proof of principle that novel and engaging mobile casual games with new aesthetics, game mechanics and player interactions can be designed and tested directly on the device for which they are intended. We describe the Gamika iOS application which includes generative art assets; a design interface enabling the making of physics-based casual games containing multiple levels with aspects ranging from Frogger-like to Asteroids-like and beyond; a configurable automated playtester which can give feedback on the playability of levels; and an automated fine-tuning engine which searches for level parameterisations that enable the game to pass a battery of tests, as evaluated by the auto-playtester. Each aspect of the implementation represents a baseline with much room for improvement, and we present some experimental results and describe how these will guide the future directions for Gamika

2015-2016 Course Catalog

2015-2016 Course Catalo

Aesthetic choices: Defining the range of aesthetic views in interactive digital media including games and 3D virtual environments (3D VEs)

Defining aesthetic choices for interactive digital media such as games is a challenging task. Objective and subjective factors such as colour, symmetry, order and complexity, and statistical features among others play an important role for defining the aesthetic properties of interactive digital artifacts. Computational approaches developed in this regard also consider objective factors such as statistical image features for the assessment of aesthetic qualities. However, aesthetics for interactive digital media, such as games, requires more nuanced consideration than simple objective and subjective factors, for choosing a range of aesthetic features. From the study it was found that the there is no one single optimum position or viewpoint with a corresponding relationship to the aesthetic considerations that influence interactive digital media. Instead, the incorporation of aesthetic features demonstrates the need to consider each component within interactive digital media as part of a range of possible features, and therefore within a range of possible camera positions. A framework, named as PCAWF, emphasized that combination of features and factors demonstrated the need to define a range of aesthetic viewpoints. This is important for improved user experience. From the framework it has been found that factors including the storyline, user state, gameplay, and application type are critical to defining the reasons associated with making aesthetic choices. The selection of a range of aesthetic features and characteristics is influenced by four main factors and sub-factors associated with the main factors. This study informs the future of interactive digital media interaction by providing clarity and reasoning behind the aesthetic decision-making inclusions that are integrated into automatically generated vision by providing a framework for choosing a range of aesthetic viewpoints in a 3D virtual environment of a game. The study identifies critical juxtapositions between photographic and cinema-based media aesthetics by incorporating qualitative rationales from experts within the interactive digital media field. This research will change the way Artificial Intelligence (AI) generated interactive digital media in the way that it chooses visual outputs in terms of camera positions, field-view, orientation, contextual considerations, and user experiences. It will impact across all automated systems to ensure that human-values, rich variations, and extensive complexity are integrated in the AI-dominated development and design of future interactive digital media production

2016-2017 Course Catalog

2016-2017 Course Catalo

2017-2018 Course Catalog

2017-2018 Course Catalo

Critiquing-based Modeling of Subjective Preferences

Funding Information: This work has been supported by Helsinki Institute for Information Technology HIIT. Publisher Copyright: © 2022 ACM.Applications designed for entertainment and other non-instrumental purposes are challenging to optimize because the relationships between system parameters and user experience can be unclear. Ideally, we would crowdsource these design questions, but existing approaches are geared towards evaluation or ranking discrete choices and not for optimizing over continuous parameter spaces. In addition, users are accustomed to informally expressing opinions about experiences as critiques (e.g. it's too cold, too spicy, too big), rather than giving precise feedback as an optimization algorithm would require. Unfortunately, it can be difficult to analyze qualitative feedback, especially in the context of quantitative modeling. In this article, we present collective criticism, a critiquing-based approach for modeling relationships between system parameters and subjective preferences. We transform critiques, such as "it was too easy/too challenging", into censored intervals and analyze them using interval regression. Collective criticism has several advantages over other approaches: "too much/too little"-style feedback is intuitive for users and allows us to build predictive models for the optimal parameterization of the variables being critiqued. We present two studies where we model: These studies demonstrate the flexibility of our approach, and show that it produces robust results that are straightforward to interpret and inline with users' stated preferences.Peer reviewe