Re-Presenting Art History

Can reenactment both as reactivation of images and restaging of exhibitions be considered an alternative way of tackling the critical task to re-present art history (i.e., to present it anew) in the here and now, over and over and over again? The gesture of restoring visibility to something no longer present, reactivating or reembodying it as an object/image in and for the present, is here proposed as a (political) act of restitution and historical recontextualization. Examining the boundaries between past and present, original and copy (as well as originality and copyright), repetition and variation, authenticity and auraticity, presence and absence, canon and appropriation, durée and transience, the paper focuses on remediation, reinterpretation, and reconstruction as creative gestures and cultural promises in contemporary art practice, curatorship, and museology.

The dynamics of decision-making and action during active sampling

Embodied Cognition Theories (ECTs) of decision-making propose that the decision process pervades the execution of choice actions and manifests itself in these actions. Decision-making scenarios where actions not only express the choice but also help sample information can provide a valuable, ecologically relevant model for this framework. We present a study to address this paradigmatic situation in humans. Subjects categorized (2AFC task) a central object image, blurred to different extents, by moving a cursor toward the left or right of the display. Upward cursor movements reduced the image blur and could be used to sample information. Thus, actions for decision and actions for sampling were orthogonal to each other. We analyzed response trajectories to test whether information-sampling movements co-occurred with the ongoing decision process. Trajectories were bimodally distributed, with one kind being direct towards one response option (non-sampling), and the other kind containing an initial upward component before veering off towards an option (sampling). This implies that there was an initial decision at the early stage of a trial, whether to sample information or not. Importantly, in sampling trials trajectories were not purely upward, but rather had a significant horizontal deviation early on. This result suggests that movements to sample information exhibit an online interaction with the decision process, therefore supporting the prediction of the ECTs under ecologically relevant constrains.

Effects of Depth-Based Object Isolation in Simulated Retinal Prosthetic Vision

Visual retinal prostheses aim to restore vision for blind individuals who suffer from outer retinal degenerative diseases, such as retinitis pigmentosa and age-related macular degeneration. Perception through retinal prostheses is very limited, but it can be improved by applying object isolation. We used an object isolation algorithm based on integral imaging to isolate objects of interest according to their depth from the camera and applied image processing manipulation to the isolated-object images. Subsequently, we applied a spatial prosthetic vision simulation that converted the isolated-object images to phosphene images. We compared the phosphene images for two types of input images, the original image (before applying object isolation), and the isolated-object image to illustrate the effects of object isolation on simulated prosthetic vision without and with multiple spatial variations of phosphenes, such as size and shape variations, spatial shifts, and dropout rate. The results show an improvement in the perceived shape, contrast, and dynamic range (number of gray levels) of objects in the phosphene image.

Recalling of multiple grasping methods from an object image with a convolutional neural network

In this study, a method for a robot to recall multiple grasping methods for a given object is proposed. The aim of this study was for robots to learn grasping methods for new objects by observing the grasping activities of humans in daily life without special instructions. For this setting, only one grasping motion was observed for an object at a time, and it was never known whether other grasping methods were possible for the object, although supervised learning generally requires all possible answers for each training input. The proposed method gives a solution for that learning situations by employing a convolutional neural network with automatic clustering of the observed grasping method. In the proposed method, the grasping methods are clustered during the process of learning of the grasping position. The method first recalls grasping positions and the network estimates the multi-channel heatmap such that each channel heatmap indicates one grasping position, then checks the graspability for each estimated position. Finally, the method recalls the hand shapes based on the estimated grasping position and the object’s shape. This paper describes the results of recalling multiple grasping methods and demonstrates the effectiveness of the proposed method.

Color Digital Holography Based on Generalized Phase-Shifting Algorithm with Monitoring Phase-Shift

Color digital holography (DH) has been researched in various fields such as the holographic camera and holographic microscope because it acquires a realistic color object wave by measuring both amplitude and phase. Among the methods for color DH, the phase-shifting DH has an advantage of obtaining a signal wave of objects without the autocorrelation and conjugate noises. However, this method usually requires many interferograms to obtain signals for all wavelengths. In addition, the phase-shift algorithm is sensitive to the phase-shift error caused by the instability or hysteresis of the phase shifter. In this paper, we propose a new method of color phase-shifting digital holography with monitoring the phase-shift. The color interferograms are recorded by using a focal plane array (FPA) with a Bayer color filter. In order to obtain the color signal wave from the interferograms with unexpected phase-shift values, we devise a generalized phase-shifting DH algorithm. The proposed method enables the robust measurement in the interferograms. Experimentally, we demonstrate the proposed algorithm to reconstruct the object image with negligibly small conjugate noises.

Computer Vision Tagging the Metropolitan Museum of Art's Collection

Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.