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Object Recognition & Extraction, AI, Automatic General Real-Time, Human Like, Deep Learning(Ver 3/5



pearlnaturalvision

Pearl Natural Vision (Ver 3.0) – innovative natural real-time 2-3D human-like bio-brain inspired content-aware humanoid robot VISION technology, one that can “see” like humans.
Only self made, Not using any vision libraries.
Might be interesting for people using Matlab, GIMP, OpenCV, Photoshop, etc.
Related: Automatic & Accurate Extraction, artificial intelligence (AI), deep neural-networks (DNN), multilayer, convolution, computer vision, big data, deep learning, machine learning, generic, autonomous, automatic, visual perception, patterns, recognition, detection, extraction, saliency, background removal, crowd/complex env’t, invariant, image processing, segmentation, augmented reality, etc.
Also related: psychology, evolution, genetics, adaptation, the Human Brain.

Only software,
Written in C++.

Highlights:

1) An integration/fusion of Modern Physics, DNN, Neuroscience, Perception, etc., into a Unified, Coherent, General, Accurate and Real Time Bio-like Visual Perception Model.

2) No keys, no params, no settings and no specific visual knowledge, of any kind, are used.

In learning, the results of this visual perception activity (objects/entities/etc…) are placed in the Visual Memory (also a DNN).
After learning phase… naturally the system power multiplies…

Applications:
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Robotic humanoid & android vision, smart toys, drones, autonomous driving, guidance, tracking, inspection, face detection, collision prevention, assistance and warning systems, security systems, surveillance, satellite imagery, medical systems, etc.

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Email: pearlnaturalvision@gmail.com
Facebook: pearlnaturalvision

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5 thoughts on “Object Recognition & Extraction, AI, Automatic General Real-Time, Human Like, Deep Learning(Ver 3/5
  1. +Phong Nguyen
    Hi,  can't use "reply", any way, regarding your Q:  It doesn’t take "the picture",  it takes/extract  the "meaningful  object (s)"  near the aiming point, like what  we do when we are looking on a picture (which is just pixels)….see also the description and annotations.

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