Lighty: A Painting Interface for Room Illumination by Robotic Light Array

ABSTRACT We propose an AR-based painting interface that enables users to design an illumination distribution for a real room using an array of computer-controlled lights. Users specify an illumination distribution of the room by painting on the image obtained by a camera mounted in the room. The painting result is overlaid on the camera image as contour lines of the target illumination intensity. The system runs an optimization interactively to calculate light parameters to deliver the requested illumination condition. In this implementation, we used actuated lights that can change the lighting direction to generate the requested illumination condition more accurately and efciently than static lights. We built a miniature-scale experimental environment and ran a user study to compare our method with a standard direct manipulation method using widgets. The results showed that the users preferred our method for informal light control. We propose an augmented reality (AR) user interface called Lighty that enables users to easily design an illumination distribution for a real room using an array of computer-controlled lights. Users specify which area of the room is to be well-lit and which is to be dark by painting an illumination distribution on a tablet device displaying an image obtained by a camera mounted in the room. The system runs an optimization to calculate the light parameters and then illuminates the room. Our method is inspired by the goal-based lighting optimization approach in computer graphics SYSTEM OVERVIEW Our overall system is shown in USER INTERFACE The user interface is shown i

Next steps for Human-Computer Integration

Human-Computer Integration (HInt) is an emerging paradigm in which computational and human systems are closely interwoven. Integrating computers with the human body is not new. However, we believe that with rapid technological advancements, increasing real-world deployments, and growing ethical and societal implications, it is critical to identify an agenda for future research. We present a set of challenges for HInt research, formulated over the course of a five-day workshop consisting of 29 experts who have designed, deployed, and studied HInt systems. This agenda aims to guide researchers in a structured way towards a more coordinated and conscientious future of human-computer integration

A Dipole Field for Object Delivery by Pushing on a Flat Surface

Abstract—This paper introduces a simple algorithm for non-prehensile object transportation by a pushing robot on a flat surface. We assume that the global position and orientation of the robot and objects are known. The system computes a dipole field around the object and moves the robot along the field. This simple algorithm resolves many subtle issues in implementing reliable pushing behaviors, such as collision avoidance, error recovery, and multi-robot coordination. We verify the effectiveness of the algorithm via several experiments with varying robot and object form factors. Although object delivery by pushing and motion control by a vector field are not new, the proposed algorithm offers easier implementation with fewer parameter adjustments because of its mode-less definition and scale-invariant formulation. O I

Apparent color picker: color prediction model to extract apparent color in photos

A color extraction interface reflecting human color perception helps pick colors from natural images as users see. Apparent color in photos differs from pixel color due to complex factors, including color constancy and adjacent color. However, methodologies for estimating the apparent color in photos have yet to be proposed. In this paper, the authors investigate suitable model structures and features for constructing an apparent color picker, which extracts the apparent color from natural photos. Regression models were constructed based on the psychophysical dataset for given images to predict the apparent color from image features. The linear regression model incorporates features that reflect multi-scale adjacent colors. The evaluation experiments confirm that the estimated color was closer to the apparent color than the pixel color for an average of 70%–80% of the images. However, the accuracy decreased for several conditions, including low and high saturation at low luminance. The authors believe that the proposed methodology could be applied to develop user interfaces to compensate for the discrepancy between human perception and computer predictions