Tag Archives: computer vision
MIT’s experiments in the 80’s to give voice synthesizers a face.
Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. The researchers trained a neural net on 50 photos of camouflaged tanks in trees, and 50 photos of trees without tanks. Using standard techniques for supervised learning, the researchers trained the neural network to a weighting that […]
In 1988, Yann LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel, where he developed a number of new machine learning methods, such as a biologically inspired model of image recognition called Convolutional Neural Networks, the “Optimal Brain Damage” regularization methods, and […]
Your browser does not support the video tag. In the frame of Ways of Machine Seeing, a series of experiments in collaboration wit Geoff Cox on the the four episodes of the BBC documentary series Ways of Seeing. In this probe, the same algorithm runs an object detection script using two different training sets. For […]
Figure 1. The first appearance of picture element, in a news item in Wireless World and Radio Review, about a demonstration by Ives at Bell Labs of a 50-by-50-element television system. A few RCA researchers, notably Albert Rose and Otto Schade, continued to use picture element to examine the theory of imaging, but with differing […]
Playing with Quickdraw
Binford, O.T. & Nevatia, R (1977) Description and Recognition of Curved Objects, Artificial Intelligence 8(1):77-98.
In the Manchurian Candidate, it is revealed that the Communists have been using the soldier Shaw as a sleeper agent, a guiltless assassin subconsciously activated by seeing the “Queen of Diamonds” playing card while playing solitaire. Provoked by the appearance of the card, he obeys orders which he then forgets. Isn’t an image just like […]
Tensorflow, an open source library for Machine Learning developed by Google, comes with a demo program that labels images. The output of the program contains the labels (or class names) and their scores. The score represents the probability of the image to belong to a certain class. green mamba (score = 0.43074) vine snake (score […]