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A mimetolithic pattern is a pattern created by rocks that may come to mimic recognizable forms through the random processes of formation, weathering and erosion. A well-known example is the Face on Mars, a rock formation on Mars that resembled a human face in certain satellite photos. Most mimetoliths are much larger than the subjects they ...
There are everyday examples of hidden faces, they are "chance images" including faces in the clouds, figures of the Rorschach Test and the Man in the Moon. Leonardo da Vinci wrote about them in his notebook: "If you look at walls that are stained or made of different kinds of stones you can think you see in them certain picturesque views of mountains, rivers, rocks, trees, plains, broad ...
Pattern recognition is a cognitive process that involves retrieving information either from long-term, short-term, or working memory and matching it with information from stimuli. There are three different ways in which this may happen and go wrong, resulting in apophenia.
Pattern recognition is crucial not only to humans, but also to other animals. Even koalas, which possess less-developed thinking abilities, use pattern recognition to find and consume eucalyptus leaves. The human brain has developed more, but holds similarities to the brains of birds and lower mammals.
The word acheropite comes from the Greek ἀχειροποίητος, meaning "not created by human hands", and the term was first applied to the Turin Shroud and the Veil of Veronica. Later, the term came to apply more generally to simulacra of a religious or spiritual nature occurring in natural phenomena, particularly those seen by believers ...
[9] [10] The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. [11] [12] Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.
FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google.The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition. [1]
Eigenface provides an easy and cheap way to realize face recognition in that: Its training process is completely automatic and easy to code. Eigenface adequately reduces statistical complexity in face image representation. Once eigenfaces of a database are calculated, face recognition can be achieved in real time.