scholarly journals Semantic Learning for Audio Applications: A Computer Vision Approach

2006 ◽  
Author(s):  
R. Sukthankar ◽  
Yan Ke ◽  
D. Hoiem
Keyword(s):  
Computer Vision ◽  
Semantic Learning

Counterfactual Autoencoder for Unsupervised Semantic Learning

2020 ◽  
pp. 720-736
Author(s):  
Saad Sadiq ◽  
Mei-Ling Shyu ◽  
Daniel J. Feaster
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Speech Processing ◽  
Semantic Learning ◽  
Novel Approach ◽  
Training Samples ◽  
Latent Space

Deep Neural Networks (DNNs) are best known for being the state-of-the-art in artificial intelligence (AI) applications including natural language processing (NLP), speech processing, computer vision, etc. In spite of all recent achievements of deep learning, it has yet to achieve semantic learning required to reason about the data. This lack of reasoning is partially imputed to the boorish memorization of patterns and curves from millions of training samples and ignoring the spatiotemporal relationships. The proposed framework puts forward a novel approach based on variational autoencoders (VAEs) by using the potential outcomes model and developing the counterfactual autoencoders. The proposed framework transforms any sort of multimedia input distributions to a meaningful latent space while giving more control over how the latent space is created. This allows us to model data that is better suited to answer inference-based queries, which is very valuable in reasoning-based AI applications.

scholarly journals Modeling of Action’s Semantic Memory Incorporated with Procedural and Skill Memory to Perform Tasks

2019 ◽  
Vol 8 (3) ◽  
pp. 1014-1024
Keyword(s):  
Computer Vision ◽  
Basal Ganglia ◽  
Human Body ◽  
Experimental Work ◽  
Recognition Task ◽  
Human Motion ◽  
Body Parts ◽  
Semantic Learning ◽  
Environmental Features ◽  
Movement Sequence

In this paper, a computational model is proposed to mimic an action’s semantic, procedural and skill learning’s by an abstract modeling of cortical columns of the Neocortex, Basal ganglia and Cerebellum brain region. In proposed work, the action semantic Learning makes a robot capable to learn an action in terms of their body parts movement sequence that allows it to recognize the learnt action by seeing as well. Whereas in procedural, it allows to learn tasks in the form of action’s hierarchy and makes it capable to capture the environmental features as a context for action’s activations. The skill memory also been added in the proposed work which allows an agent to translate the action as per the current demand of the action. Also, the model has used Vnect model of computer vision to map the human motion into sequence of 3D skeleton of human body, therefore the model can learn by seeing, like humans. In experimental work, the model is tested on vague samples of few actions, where the model is found robust in action recognition task and performed well as per the expectations

Computer Vision for Visual Effects

2009 ◽  
Author(s):  
Richard J. Radke
Keyword(s):  
Computer Vision ◽  
Visual Effects

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision

New Semantic Learning in Amnesia

1994 ◽  
Author(s):  
Stephen B. Hamann ◽  
Larry R. Squire
Keyword(s):  
Semantic Learning

Computer vision for industrial applications

1983 ◽  
Vol 2 (5) ◽  
pp. 130
Author(s):  
J.A. Losty ◽  
P.R. Watkins
Keyword(s):  
Computer Vision ◽  
Industrial Applications

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

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