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Machine Learning and Visual Perception

Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian;

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Machine Understanding

that leads to understanding and is based on the categories of sensory objects. In this approach the visual and non-visual knowledge, in the;

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Deep Learning in Visual Computing

This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine;

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Deep Learning in Visual Computing

This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine;

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Machine Learning based Natural Scene Recognition for Mobile Robot Localization i

recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for;

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Stevens' Handbook of Experimental Psychology

; depth perception; motor control; perceptual learning; the interface theory of perception; vestibular, proprioceptive, and haptic contributions;

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OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging;

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Symbolic Visual Learning

utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to;

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Visual Perception and Control of Underwater Robots

Visual Perception and Control of Underwater Robots covers theories and applications from aquatic visual perception and underwater robotics;

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Visual Perception and Control of Underwater Robots

Visual Perception and Control of Underwater Robots covers theories and applications from aquatic visual perception and underwater robotics;

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Interdisciplinary Evolution of the Machine Brain

the theory and algorithms involved, including data-driven approaches in machine learning, monitoring and understanding visual environments;

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Interdisciplinary Evolution of the Machine Brain

the theory and algorithms involved, including data-driven approaches in machine learning, monitoring and understanding visual environments;

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Advances in Visual Perception Research

of importance in visual perception, accuracy and bias in emotion perception, automated vector-based gaze analysis, visual-vestibular interactions when;

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Visual Perception and Action in Sport

. Visual Perception and Action in Sport examines the information which is perceived by the human visual system and the way it is utilised to;

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Human and Machine Perception

series, ranging from computer perception to psychology and physiology of perception (visual, auditory, tactile, etc.). The theme of this workshop;

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Pattern Recognition and Computer Vision

Processing and Analysis; Machine Learning, Neural Network and Deep Learning, and New Advances in Visual Perception and Understanding.;

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Computer Analysis of Images and Patterns

, and the First Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019. The 12 papers presented in this;

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Learning-Based Local Visual Representation and Indexing

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing;

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Domain Adaptation for Visual Understanding

adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel;

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Domain Adaptation for Visual Understanding

adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel;

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Visual Quality Assessment by Machine Learning

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by;

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Recent Advances in Robot Learning

programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as;

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Understanding Vision

of visual inputs, Chapter 5 is on visual attentional selection driven by sensory inputs, and Chapter 6 is on visual perception or decoding;

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Artificial Neural Networks and Machine Learning ICANN 2020

and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics;

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Artificial Neural Networks and Machine Learning ICANN 2020

and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics;

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Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models;

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Human and Machine Learning

human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML;

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