Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials

Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this paper, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.Comment: 15 pages, 14 figures; includes supplemental figure

Investigating the Effect of Color Gamut Mapping Quantitatively and Visually

With the advent of various color management standards and tools, the print media industry has seen many advancements aimed towards quantitatively and qualitatively acceptable color reproduction. This research attempts to test one of the most fundamental and integral parts of a standard color management workflow, the profile. The gamut mapping techniques implemented by the ICC profiles created using different profiling application programs were tested for their congruity to the theoretical concepts, standards, and definitions documented by International Color Consortium (ICC). Once these profiling software applications were examined, the significance of the possible discrepancies were tested by establishing a visual assessment of pictorial images using these profiles. In short, this research assessed the implementations of the ICC color rendering intents in a standard or a commonly used color managed workflow, and then described the significance of these discrepancies in terms of interoperability. For this research, interoperability was defined the assessment of different ICC profiles in producing similar results, i.e., quantitatively and visually. In order to achieve the desired assessment, the two profiling applications were selected and each used to create an output profile using the same characterization data set. The two profiles were then compared for differences in the way they mapped real world colors. The results displayed that even though there were some significant quantitative color differences, visual subjective evaluation did not reflect any noticeable color differences and therefore concluded that the profiles were interoperable. These findings reveal that even though quantitative color differences may reflect significant color differences, subjective visual comparisons may not always reflect the same or agree with quantitative findings

Knowledge discovery through creating formal contexts

Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called formal concept analysis (FCA). This paper describes a tool called FcaBedrock that converts data into formal contexts for FCA. This paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Examples are given of how formal contexts can be created using FcaBedrock and then analysed for knowledge discovery, using real datasets. Creating formal contexts using FcaBedrock is shown to be straightforward and versatile. Large datasets are easily converted into a standard FCA format

The Heavy Ion Physics Program with ATLAS at the LHC

The first Pb+Pb collisions at the Large Hadron Collider (LHC) at sqrts_NN = 5.52 TeV are imminent. Heavy ion collisions at the LHC provide an extended energy lever arm to the existing measurements made at RHIC and SPS, especially in hard (large-Q^2) processes. In this contribution an overview of the ATLAS detector is given and the current physics focus of Heavy Ion Working Group is discussed.Comment: 8 pages, 6 figures, prepared for the 23rd Winter Workshop on Nuclear Dynamics, Big Sky, MT, Feb 11-18, 200

Implementing an ICC printer profile visualization software

Device color gamut plays a crucial role in ICC-based color management systems. Accurately visualizing a device\u27s gamut boundary is important in the analysis of color conversion and gamut mapping. ICC profiles contain all the information which can be used to better understand the capabilities of the device. This thesis project has implemented a printer profile visualization software. The project uses A2B 1 tag in a printer profile as gamut data source, then renders gamut of device the profile represents in CIELAB space with a convex hull algorithm. Gamut can be viewed interactively from any view points. The software also gets the gamut data set using CMM with different intent to do color conversion from a specified printer profile to a generic lab profile (short for A2B conversion) or from a generic CIELAB profile to a specified printer pro file and back to the generic CIELAB profile (short for B2A2B). Gamut can be rendered as points, wire frame or solid surface. Two-dimension a*b* and L*C* gamut slice analytic tools were also developed. The 2D gamut slice algorithm is based on dividing gamut into small sections according to lightness and hue angle. The point with maximum chroma on each section can be used to present a*b* gamut slice on a constant lightness plane or L*C* gamut slice on a constant hue angle plane. Gamut models from two or more device profiles can be viewed in the same window. Through the comparison, we can better understand the device reproduction capacities and proofing problems. This thesis also explained printer profile in details, and examined what gamut data source was the best for gamut visualization. At the same time, some gamut boundary descriptor algorithms were discussed. Convex hull algorithm and device space to CIELAB space mapping algorithm were chosen to render 3D gamut in this thesis project. Finally, an experiment was developed to validate the gamut data generated from the software. The experiment used the same method with profile visualization software to get gamut data set source from Photoshop 6.0. The results of the experiment were showed that the data set derived from visualization software was consistent with those from Photoshop 6.0

Methods of conceptual clustering and their relation to numerical taxonomy

Artificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statistical methods generally connoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward symbolic (as opposed to numeric) data representations. We explore this difference within a limited context, devoting the bulk of our paper to the explication of conceptual clustering, an extension to the statistically based methods of numerical taxonomy. In conceptual clustering the formation of object clusters is dependent on the quality of 'higher-level' characterizations, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. We conclude with a discussion of how alternative concept representations might enhance the effectiveness of future conceptual clustering systems

Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration

Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40% of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.Comment: Submitted to IEEE International Conference on Robotics and Automation (ICRA) 201

Image-Processing Techniques for the Creation of Presentation-Quality Astronomical Images

The quality of modern astronomical data, the power of modern computers and the agility of current image-processing software enable the creation of high-quality images in a purely digital form. The combination of these technological advancements has created a new ability to make color astronomical images. And in many ways it has led to a new philosophy towards how to create them. A practical guide is presented on how to generate astronomical images from research data with powerful image-processing programs. These programs use a layering metaphor that allows for an unlimited number of astronomical datasets to be combined in any desired color scheme, creating an immense parameter space to be explored using an iterative approach. Several examples of image creation are presented. A philosophy is also presented on how to use color and composition to create images that simultaneously highlight scientific detail and are aesthetically appealing. This philosophy is necessary because most datasets do not correspond to the wavelength range of sensitivity of the human eye. The use of visual grammar, defined as the elements which affect the interpretation of an image, can maximize the richness and detail in an image while maintaining scientific accuracy. By properly using visual grammar, one can imply qualities that a two-dimensional image intrinsically cannot show, such as depth, motion and energy. In addition, composition can be used to engage viewers and keep them interested for a longer period of time. The use of these techniques can result in a striking image that will effectively convey the science within the image, to scientists and to the public.Comment: 104 pages, 38 figures, submitted to A