This device allows for the automatic generation of a Gaussian curve (position of the balls at the bottom. The artistic problems of the dérive occur at the same level as the relatively unpredictable path of each ball.
“As we know, everyone is now talking about the virtual, i.e., computerized museum, and that must be the reason why a media historian has been invited to Barcelona.” Friedrich Kittler on the museum of art and digital archiving. (more…)
Duchenne was based at the Salpêtrière hospital in Paris, where he researched muscular electrophysiology—the perceived electrical dysfunction underlying neurological conditions, ranging from strokes and epilepsy through to the more questionable areas of hysteria and insanity. […] The afflictions of the inmates of the Salpêtrière made them perfect candidates for Duchenne’s research and documentation: muscular paralysis and facial anaesthetics made them extremely malleable. The flow of sustained electrical currents allowed Duchenne to overcome the limits of photography’s then long shutter speeds to have his sitters ‘hold’ a pose for an extended period. (more…)
In this demo video, a programmer works interactively with open CV. As the code is altered, a window displaying the results of the computation is continuously updated. The demonstration ends as the programmer connects a camera and then places object (playing cards) on a table, manually isolates the pixels of the card, then uses the captured sample to locate and outline the same card in subsequent frames of the live video.
Although early users of database technology were predominantly large institutions, the database was also a key technology in the populist vision of personal computing generated by microcomputer fans, researchers, hobbyists, and entrepreneurs in the 1970s and 1980s. Informed by science fiction sensitive to the authoritarian use of database technology, these personal computing advocates hoped that experience with small database systems might sharpen popular critique of mass-scale information processing efforts. As database design receded from the desktop in the 1990s, however, the populist promises were largely forgotten and the database became an exclusively institutional technology once again.
A social history of database technology situates the web’s massive databases among more than a century of mass-scale information processing systems. Evidence of popular anxiety recurs throughout this history and indicates that non-specialists often struggle to apprehend the limits of database technology and may alternately over- and under-estimate the extent of mass data collection and the types of analytic outcomes that are possible. Meanwhile, the cautious optimism of the microcomputer era points to a latent database populism that may yet be revived should users grow sufficiently frustrated by the lack of transparency among large, data-driven institutions.
We have described each of the operations on the retinal image in terms of what common factors in a large variety of stimuli cause response and what common factors have no effect. What, then, does a particular fiber in the optic nerve measure? We have considered it to be how much there is in a stimulus of that quality which excites the fiber maximally, naming that quality.
The operations thus have much more the flavor of perception than of sensation if that distinction has any meaning now. That is to say that the language in which they are best described is the language of complex abstractions from the visual image. We have been tempted,for example, to call the convexity detectors “bug perceivers.” Such a fiber [operation 2] responds best when a dark object, smaller than a receptive field, enters that field, stops, and moves about intermittently thereafter. The response is not affected if the lighting changes or if the background (say a picture of grass and flowers) is moving, and is not there if only the background, moving or still, is in the field. Could one better describe a system for detecting an accessible bug?
“As I recovered at home, colour started slowly creeping back into my life, whispering around corners. This was a very perplexing time, for often I only felt I was seeing a colour but was unable to identify it. I would stare endlessly at trees and lamp-posts, desperate to match the colour I believed was there with the strange sensory experience I was having. Bright primary colours were the first I could identify with any conviction. Red led the way, followed by blues and yellows – but cloudy and faded. I struggled enormously with greens, greys and any pale or muted colours. This was not the vibrant rainbow world I was used to.
I still had some channels of information transmitting the colours around me to my brain, but I was receiving only part of the message. My lifelong emotional associations with colours were still intact, even if my sight was not. I tried to use language to help myself recover. “You are green,” I would tell the grass. I believed the more I stimulated my brain by observing the world around me and reminding myself what colour was, the more the damaged circuitry in my brain would reconnect and bring my normal vision back online. I found the more I did this, the more it worked.”
Computer vision syndrome (CVS) is a condition resulting from focusing the eyes on a computer or other display device for protracted, uninterrupted periods of time. Some symptoms of CVS include headaches, blurred vision, neck pain, fatigue, eye strain, dry eyes, irritated eyes, double vision, vertigo/dizziness, polyopia, and difficulty refocusing the eyes.
In CVS, the screen is not considered as a passive object where information is merely displayed. It is an active device that emits radiations towards the user of the computer. For CVS researchers, the screen is emitting phototoxic light whose damaging effect can be reduced by wearing blue light-filtering lenses. An alternative remedy to shielding the retina with specialized spectacles is to follow the 20 20 20 rule. 20 20 20 is an algorithmic formulation of the sequences of action one should follow to avoid CVS: “Every 20 minutes, focus the eyes on an object 20 feet (6 meters) away for 20 seconds”.
Identification by biometric features has become more popular in the last decade. High quality video and fingerprint sensors have become less expensive and are nowadays standard components in many mobile devices. Thus, many devices can be unlocked via fingerprint or face verification. The state of the art accuracy of biometric facial recognition systems prompted even systems that need high security standards like border control at airports to rely on biometric systems. While most biometric facial recognition systems perform quite accurate under a controlled environment, they can easily be tricked by morphing attacks. The concept of a morphing attack is to create one synthetic face image that contains characteristics of two different individuals and to use this image on a document or as reference image in a database. Using this image for authentication, a biometric facial recognition system accepts both individuals. In this paper, we propose a morphing attack detection approach based on convolutional neural networks. We present an automatic morphing pipeline to generate morphing attacks, train neural networks based on this data and analyze their accuracy. The accuracy of different well-known network architectures are compared and the advantage of using pretrained networks compared to networks learned from scratch is studied.