Custom Autonomous Robotic Painter

Today humans out perform robots in problem solving, adaptability, and creativity. The goal of this project was to bridge the gap between robotic and human capabilities through the development of an autonomous painting robot. The custom design of the mechanics, electronics, and software allowed for a versatile solution. Image decomposition techniques were used to break down input images into feature areas that were reconstructed by the robot. Vision feedback was also performed during the painting process to apply corrections to the artwork dynamically. Understanding the motions undertaken by painters and replicating it in a robotic platform can revolutionize the art form, contribute to the scientific advancement of robotic capabilities, and reduce the workload needed to construct paintings

Custom Autonomous Robotic Painter

Today humans out perform robots in problem solving, adaptability, and creativity. The goal of this project was to bridge the gap between robotic and human capabilities through the development of an autonomous painting robot. The custom design of the mechanics, electronics, and software allowed for a versatile solution. Image decomposition techniques were used to break down input images into feature areas that were reconstructed by the robot. Vision feedback was also performed during the painting process to apply corrections to the artwork dynamically. Understanding the motions undertaken by painters and replicating it in a robotic platform can revolutionize the art form, contribute to the scientific advancement of robotic capabilities, and reduce the workload needed to construct paintings

Visual-based decision for iterative quality enhancement in robot drawing.

Kwok, Ka Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 113-116).Abstracts in English and Chinese.ABSTRACT --- p.iChapter 1. --- INTRODUCTION --- p.1Chapter 1.1 --- Artistic robot in western art --- p.1Chapter 1.2 --- Chinese calligraphy robot --- p.2Chapter 1.3 --- Our robot drawing system --- p.3Chapter 1.4 --- Thesis outline --- p.3Chapter 2. --- ROBOT DRAWING SYSTEM --- p.5Chapter 2.1 --- Robot drawing manipulation --- p.5Chapter 2.2 --- Input modes --- p.6Chapter 2.3 --- Visual-feedback system --- p.8Chapter 2.4 --- Footprint study setup --- p.8Chapter 2.5 --- Chapter summary --- p.10Chapter 3. --- LINE STROKE EXTRACTION AND ORDER ASSIGNMENT --- p.11Chapter 3.1 --- Skeleton-based line trajectory generation --- p.12Chapter 3.2 --- Line stroke vectorization --- p.15Chapter 3.3 --- Skeleton tangential slope evaluation using MIC --- p.16Chapter 3.4 --- Skeleton-based vectorization using Bezier curve interpolation --- p.21Chapter 3.5 --- Line stroke extraction --- p.25Chapter 3.6 --- Line stroke order assignment --- p.30Chapter 3.7 --- Chapter summary --- p.33Chapter 4. --- PROJECTIVE RECTIFICATION AND VISION-BASED CORRECTION --- p.34Chapter 4.1 --- Projective rectification --- p.34Chapter 4.2 --- Homography transformation by selected correspondences --- p.35Chapter 4.3 --- Homography transformation using GA --- p.39Chapter 4.4 --- Visual-based iterative correction example --- p.45Chapter 4.5 --- Chapter summary --- p.49Chapter 5. --- ITERATIVE ENHANCEMENT ON OFFSET EFFECT AND BRUSH THICKNESS --- p.52Chapter 5.1 --- Offset painting effect by Chinese brush pen --- p.52Chapter 5.2 --- Iterative robot drawing process --- p.53Chapter 5.3 --- Iterative line drawing experimental results --- p.56Chapter 5.4 --- Chapter summary --- p.67Chapter 6. --- GA-BASED BRUSH STROKE GENERATION --- p.68Chapter 6.1 --- Brush trajectory representation --- p.69Chapter 6.2 --- Brush stroke modeling --- p.70Chapter 6.3 --- Stroke simulation using GA --- p.72Chapter 6.4 --- Evolutionary computing results --- p.77Chapter 6.5 --- Chapter summary --- p.95Chapter 7. --- BRUSH STROKE FOOTPRINT CHARACTERIZATION --- p.96Chapter 7.1 --- Footprint video capturing --- p.97Chapter 7.2 --- Footprint image property --- p.98Chapter 7.3 --- Experimental results --- p.102Chapter 7.4 --- Chapter summary --- p.109Chapter 8. --- CONCLUSIONS AND FUTURE WORKS --- p.111BIBLIOGRAPHY --- p.11

Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems

The Machine as Art/ The Machine as Artist

The articles collected in this volume from the two companion Arts Special Issues, “The Machine as Art (in the 20th Century)” and “The Machine as Artist (in the 21st Century)”, represent a unique scholarly resource: analyses by artists, scientists, and engineers, as well as art historians, covering not only the current (and astounding) rapprochement between art and technology but also the vital post-World War II period that has led up to it; this collection is also distinguished by several of the contributors being prominent individuals within their own fields, or as artists who have actually participated in the still unfolding events with which it is concerne

My wandering art in America

The wandering life of a Christian artist from China travelling to the United States and creating art that challenges the Chinese Communist Party, choosing to pursue freedom and reacting against the politics and oppression of various institutional forces

Stroke trajectory generation for a robotic Chinese calligrapher.

Lam, Hiu Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 84-89).Abstracts in English and Chinese.Chapter Chapter 1: --- Introduction --- p.1Chapter 1.1. --- Overview on Robotics --- p.1Chapter 1.2. --- Literture Review on Art-Robot --- p.1Chapter 1.3. --- Robot artist for Chinese Calligraphy and Paintings --- p.3Chapter 1.4. --- Motivation and Research Objective --- p.4Chapter 1.5. --- Thesis Outline --- p.5Chapter Chapter 2: --- Intelligent Robotic Art System --- p.6Chapter 2.1. --- Previous Configuration --- p.6Chapter 2.1.1. --- 3 DOF Manipulator --- p.7Chapter 2.1.2. --- Digital Image Input System --- p.7Chapter 2.2. --- Hardware Modification --- p.8Chapter 2.2.1. --- Additional Degree of Freedoms --- p.8Chapter 2.2.2. --- Infra-red Sensing System for Manipulator Positioning --- p.9Chapter 2.2.3. --- Axial-rotary Brush --- p.11Chapter 2.2.4. --- Interface program --- p.13Chapter 2.2.5. --- Vibration Reduction --- p.16Chapter Chapter 3: --- Skeletonization Based on Delaunay Triangulation and Bezier Interpolation --- p.18Chapter 3.1. --- Background Theory --- p.20Chapter 3.1.1. --- Smoothed Local Symmetry --- p.20Chapter 3.1.2. --- Delaunay Triangulation --- p.21Chapter 3.1.3. --- Bezier Curve --- p.23Chapter 3.2. --- Algorithm --- p.24Chapter 3.2.1. --- Edge Sampling --- p.24Chapter 3.2.2. --- Triangle Modification --- p.26Chapter 3.2.3. --- Triangle Filtering and Replacement --- p.28Chapter 3.2.4. --- Internal Edge Refinement --- p.30Chapter 3.2.5. --- Skeletal Interpolation --- p.31Chapter 3.3. --- Experiments --- p.32Chapter 3.4. --- Chapter Summary --- p.36Chapter Chapter 4: --- Stroke Segmentation for Chinese Words --- p.37Chapter 4.1. --- Rule-based Spurious Branches Removal --- p.38Chapter 4.1.1. --- Spurious Branch in Stroke Terminal --- p.40Chapter 4.1.2. --- Spurious Branch Caused by Turning Stroke --- p.42Chapter 4.2. --- Stroke Connectivity Determination --- p.44Chapter 4.2.1. --- Gradient of Medial Axis --- p.45Chapter 4.2.2. --- Gradient of Branch Boundary --- p.47Chapter 4.2.3. --- Branch Width --- p.49Chapter 4.2.4. --- Combined Objective Function --- p.50Chapter 4.3. --- Stroke Generation --- p.51Chapter 4.3.1. --- Stroke Connection between Branches --- p.52Chapter 4.3.2. --- Stroke Generation in Stroke Terminal --- p.53Chapter 4.4. --- Experiment Using Intelligent Robotic Art System --- p.54Chapter 4.5. --- Discussion --- p.59Chapter Chapter 5: --- Experimental Acquisition of Brush Footprints --- p.61Chapter 5.1. --- Brush Footprint Extraction --- p.62Chapter 5.2. --- Graphical Interface for Inputting Sample Points of Brush Footprints --- p.64Chapter 5.3. --- Curve Fitting for Brush Footprint Sample Points --- p.70Chapter 5.3.1. --- Curve Fitting Using Genetic Algorithm --- p.70Chapter 5.3.2. --- Curve Fitting by Least Squares Regression --- p.72Chapter 5.4. --- Discussion --- p.74Chapter Chapter 6: --- Trajectory Generation for Robotic Chinese Calligraphy --- p.75Chapter 6.1. --- Stroke Trajectory Searching with According Stroke Width --- p.75Chapter 6.2. --- Improvement in Stroke Trajectory --- p.77Chapter 6.3. --- Experiment --- p.80Conclusion and Future Work --- p.82References --- p.84Appendix --- p.90Chapter 9.1. --- Segmented Strokes of Bada Shanren's Calligraphy --- p.9