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Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Neural Circuits

Neural Circuits

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Multi-input and Multi-variable systems

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Survey on Deep Neural Networks in Speech and Vision Systems.

M Alam¹, M D Samad¹, L Vidyaratne¹

¹Department of Computer Science, Tennessee State University, Nashville, TN, 37209.

Neurocomputing

|October 26, 2020

Summary

This survey reviews deep neural networks (DNNs) for vision and speech, highlighting advancements in algorithms, hardware, and emerging applications. DNNs are revolutionizing intelligent systems, from mobile devices to healthcare.

Keywords:

Vision processing computational intelligence computer vision convolutional neural networks deep autoencoders deep learning embedded systems generative neural networks hardware constraints natural language processing speech recognition

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep neural networks (DNNs) have rapidly advanced intelligent vision and speech systems.
Vast sensor data, cloud computing, and mobile technology fuel DNN innovation.
Next-generation intelligent systems promise to revolutionize computing.

Purpose of the Study:

To survey state-of-the-art DNN architectures, algorithms, and systems for vision and speech.
To review challenges and successes of running DNNs on resource-constrained hardware.
To discuss emerging applications of vision and speech technologies.

Main Methods:

Review of recent deep learning models and their evolution.
Analysis of industrial research and development efforts.
Summary of hardware-specific DNN optimization techniques.

Main Results:

Significant progress in DNNs for intelligent vision and speech applications.
Key challenges identified for deploying DNNs on edge devices.
Emerging applications span affective computing, intelligent transportation, and precision medicine.

Conclusions:

DNNs are poised to revolutionize future vision and speech systems.
Comprehensive survey covers both software and hardware aspects of intelligent systems.
Emerging technologies show immense promise for research and development.