Recognizing a Moving Object by using Neural Nets andOcular Micro Tremor

Recognizing a Moving Object by using Neural Nets andOcular Micro Tremor

This paper describes a neural processor capable of tracking and recognizing an object that moves freely in a 3D space and is visualized through a webcam. Images are preprocessed by using openCV routines in order to obtain crude border detection information. The obtained massive data is delivered to a neural processor composed by two cascaded, independent networks trained at different epochs and with different attitude. The first net specializes...

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Invariant Object Recognition

Invariant Object Recognition

Highlights An invariant object recognition system processing real world images is propose The system relies on the emergent behaviors of independent neuromorphic structures The circulating information flow is a form of time stabilized sparse code The system has theoretical limitless learning capacity Extreme visual invariant object recognition can be handled View...

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A Bio-inspired Robot with Visual Perception of Affordances

A Bio-inspired Robot with Visual Perception of Affordances

We present a visual robot whose associated neural controller develops a realistic perception of affordances. The controller uses known insect brain principles; particularly the time stabilized sparse code communication between the Antennal Lobe and the Mushroom Body. The robot perceives the world through a webcam and canny border openCV routines. Self-controlled neural agents process this massive raw data and produce a time stabilized sparse...

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V1 Reliable Object Recognition by Using Cooperative Neural Agents

V1 Reliable Object Recognition by Using Cooperative Neural Agents

An artificial vision system based upon known insect brain structures is presented. It reliably recognizes real world objects visualized through a web cam or read from databases, and utilizes neural agents that communicate through time stabilized sparse code. A three layer ANN is trained to track one reticle pattern. Once trained the net becomes a proactive agent by participating in a local, close loop control system which oscillates, shows a...

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Controlling vision guided mobile robot

Controlling vision guided mobile robot

We have studied and developed the behavior of two specific neural processes, used for vehicle driving and path planning, in order to control mobile robots. Each processor is an independent agent defined by a neural network trained for a defined task. Through simulated evolution fully trained agents are encouraged to socialize by opening low bandwidth, asynchronous channels between them. Under evolutive pressure agents spontaneously develop...

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