Computer Vision And Machine Intelligence

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Explainable and Interpretable Models in Computer Vision and Machine Learning

Author : Hugo Jair Escalante,Sergio Escalera,Isabelle Guyon,Xavier Baró,Yağmur Güçlütürk,Umut Güçlü,Marcel van Gerven
Publisher : Springer
Page : 299 pages
File Size : 43,7 Mb
Release : 2018-11-29
Category : Computers
ISBN : 9783319981314

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Explainable and Interpretable Models in Computer Vision and Machine Learning by Hugo Jair Escalante,Sergio Escalera,Isabelle Guyon,Xavier Baró,Yağmur Güçlütürk,Umut Güçlü,Marcel van Gerven Pdf

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

Computer Vision and Machine Intelligence

Author : Massimo Tistarelli,Shiv Ram Dubey,Satish Kumar Singh,Xiaoyi Jiang
Publisher : Springer Nature
Page : 777 pages
File Size : 52,8 Mb
Release : 2023-05-05
Category : Technology & Engineering
ISBN : 9789811978678

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Computer Vision and Machine Intelligence by Massimo Tistarelli,Shiv Ram Dubey,Satish Kumar Singh,Xiaoyi Jiang Pdf

This book presents selected research papers on current developments in the fields of computer vision and machine intelligence from International Conference on Computer Vision and Machine Intelligence (CVMI 2022). The book covers topics in image processing, artificial intelligence, machine learning, deep learning, computer vision, machine intelligence, etc. The book is useful for researchers, postgraduate and undergraduate students, and professionals working in this domain.

Machine Intelligence

Author : Pethuru Raj,P Beaulah Soundarabai,Peter Augustine
Publisher : CRC Press
Page : 343 pages
File Size : 40,5 Mb
Release : 2023-10-03
Category : Computers
ISBN : 9781000960310

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Machine Intelligence by Pethuru Raj,P Beaulah Soundarabai,Peter Augustine Pdf

Machines are being systematically empowered to be interactive and intelligent in their operations, offerings. and outputs. There are pioneering Artificial Intelligence (AI) technologies and tools. Machine and Deep Learning (ML/DL) algorithms, along with their enabling frameworks, libraries, and specialized accelerators, find particularly useful applications in computer and machine vision, human machine interfaces (HMIs), and intelligent machines. Machines that can see and perceive can bring forth deeper and decisive acceleration, automation, and augmentation capabilities to businesses as well as people in their everyday assignments. Machine vision is becoming a reality because of advancements in the computer vision and device instrumentation spaces. Machines are increasingly software-defined. That is, vision-enabling software and hardware modules are being embedded in new-generation machines to be self-, surroundings, and situation-aware. Machine Intelligence emphasizes computer vision and natural language processing as drivers of advances in machine intelligence. The book examines these technologies from the algorithmic level to the applications level. It also examines the integrative technologies enabling intelligent applications in business and industry. Features: Motion images object detection over voice using deep learning algorithms Ubiquitous computing and augmented reality in HCI Learning and reasoning in Artificial Intelligence Economic sustainability, mindfulness, and diversity in the age of artificial intelligence and machine learning Streaming analytics for healthcare and retail domains Covering established and emerging technologies in machine vision, the book focuses on recent and novel applications and discusses state-of-the-art technologies and tools.

Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities

Author : Chakraborty, Shouvik,Mali, Kalyani
Publisher : IGI Global
Page : 271 pages
File Size : 43,8 Mb
Release : 2020-03-13
Category : Computers
ISBN : 9781799827382

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Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities by Chakraborty, Shouvik,Mali, Kalyani Pdf

Computer vision and object recognition are two technological methods that are frequently used in various professional disciplines. In order to maintain high levels of quality and accuracy of services in these sectors, continuous enhancements and improvements are needed. The implementation of artificial intelligence and machine learning has assisted in the development of digital imaging, yet proper research on the applications of these advancing technologies is lacking. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities explores the theoretical and practical aspects of modern advancements in digital image analysis and object detection as well as its applications within healthcare, security, and engineering fields. Featuring coverage on a broad range of topics such as disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers, academicians, practitioners, scientists, industry professionals, scholars, and students seeking research on the current developments in object recognition using artificial intelligence.

Practical Machine Learning for Computer Vision

Author : Valliappa Lakshmanan,Martin Görner,Ryan Gillard
Publisher : "O'Reilly Media, Inc."
Page : 481 pages
File Size : 53,5 Mb
Release : 2021-07-21
Category : Computers
ISBN : 9781098102333

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Practical Machine Learning for Computer Vision by Valliappa Lakshmanan,Martin Görner,Ryan Gillard Pdf

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Computer Vision and Machine Intelligence in Medical Image Analysis

Author : Mousumi Gupta,Debanjan Konar,Siddhartha Bhattacharyya,Sambhunath Biswas
Publisher : Springer Nature
Page : 150 pages
File Size : 53,8 Mb
Release : 2019-08-28
Category : Technology & Engineering
ISBN : 9789811387982

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Computer Vision and Machine Intelligence in Medical Image Analysis by Mousumi Gupta,Debanjan Konar,Siddhartha Bhattacharyya,Sambhunath Biswas Pdf

This book includes high-quality papers presented at the Symposium 2019, organised by Sikkim Manipal Institute of Technology (SMIT), in Sikkim from 26–27 February 2019. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.

Challenges and Applications for Implementing Machine Learning in Computer Vision

Author : Kashyap, Ramgopal,Kumar, A.V. Senthil
Publisher : IGI Global
Page : 293 pages
File Size : 49,5 Mb
Release : 2019-10-04
Category : Computers
ISBN : 9781799801849

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Challenges and Applications for Implementing Machine Learning in Computer Vision by Kashyap, Ramgopal,Kumar, A.V. Senthil Pdf

Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.

Low-Power Computer Vision

Author : George K. Thiruvathukal,Yung-Hsiang Lu,Jaeyoun Kim,Yiran Chen,Bo Chen
Publisher : CRC Press
Page : 395 pages
File Size : 53,6 Mb
Release : 2022-02-22
Category : Computers
ISBN : 9781000540963

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Low-Power Computer Vision by George K. Thiruvathukal,Yung-Hsiang Lu,Jaeyoun Kim,Yiran Chen,Bo Chen Pdf

Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.

Computer Vision and Machine Learning in Agriculture, Volume 2

Author : Mohammad Shorif Uddin,Jagdish Chand Bansal
Publisher : Springer Nature
Page : 269 pages
File Size : 46,9 Mb
Release : 2022-03-13
Category : Technology & Engineering
ISBN : 9789811699917

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Computer Vision and Machine Learning in Agriculture, Volume 2 by Mohammad Shorif Uddin,Jagdish Chand Bansal Pdf

This book is as an extension of previous book “Computer Vision and Machine Learning in Agriculture” for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvesting, weed, and multi-class crops detection with the help of robots and UAVs through machine learning and deep learning algorithms for smart agriculture. Next, two chapters describe agricultural data retrievals and data collections. Chapters 6, 7, 8 and 9 focuses on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies.

Machine Learning for Computer Vision

Author : Roberto Cipolla,Sebastiano Battiato,Giovanni Maria Farinella
Publisher : Springer
Page : 250 pages
File Size : 47,5 Mb
Release : 2012-07-27
Category : Technology & Engineering
ISBN : 9783642286612

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Machine Learning for Computer Vision by Roberto Cipolla,Sebastiano Battiato,Giovanni Maria Farinella Pdf

Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.

Machine Learning in Computer Vision

Author : Nicu Sebe,Ira Cohen,Ashutosh Garg,Thomas S. Huang
Publisher : Springer Science & Business Media
Page : 242 pages
File Size : 55,8 Mb
Release : 2006-03-30
Category : Computers
ISBN : 9781402032752

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Machine Learning in Computer Vision by Nicu Sebe,Ira Cohen,Ashutosh Garg,Thomas S. Huang Pdf

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.

Artificial Intelligence and Computer Vision

Author : Huimin Lu,Yujie Li
Publisher : Springer
Page : 211 pages
File Size : 52,7 Mb
Release : 2016-11-01
Category : Technology & Engineering
ISBN : 9783319462455

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Artificial Intelligence and Computer Vision by Huimin Lu,Yujie Li Pdf

This edited book presents essential findings in the research fields of artificial intelligence and computer vision, with a primary focus on new research ideas and results for mathematical problems involved in computer vision systems. The book provides an international forum for researchers to summarize the most recent developments and ideas in the field, with a special emphasis on the technical and observational results obtained in the past few years.

Covariances in Computer Vision and Machine Learning

Author : Hà Quang Minh,Vittorio Murino
Publisher : Morgan & Claypool Publishers
Page : 172 pages
File Size : 48,9 Mb
Release : 2017-11-07
Category : Computers
ISBN : 9781681730141

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Covariances in Computer Vision and Machine Learning by Hà Quang Minh,Vittorio Murino Pdf

Covariance matrices play important roles in many areas of mathematics, statistics, and machine learning, as well as their applications. In computer vision and image processing, they give rise to a powerful data representation, namely the covariance descriptor, with numerous practical applications. In this book, we begin by presenting an overview of the {\it finite-dimensional covariance matrix} representation approach of images, along with its statistical interpretation. In particular, we discuss the various distances and divergences that arise from the intrinsic geometrical structures of the set of Symmetric Positive Definite (SPD) matrices, namely Riemannian manifold and convex cone structures. Computationally, we focus on kernel methods on covariance matrices, especially using the Log-Euclidean distance. We then show some of the latest developments in the generalization of the finite-dimensional covariance matrix representation to the {\it infinite-dimensional covariance operator} representation via positive definite kernels. We present the generalization of the affine-invariant Riemannian metric and the Log-Hilbert-Schmidt metric, which generalizes the Log Euclidean distance. Computationally, we focus on kernel methods on covariance operators, especially using the Log-Hilbert-Schmidt distance. Specifically, we present a two-layer kernel machine, using the Log-Hilbert-Schmidt distance and its finite-dimensional approximation, which reduces the computational complexity of the exact formulation while largely preserving its capability. Theoretical analysis shows that, mathematically, the approximate Log-Hilbert-Schmidt distance should be preferred over the approximate Log-Hilbert-Schmidt inner product and, computationally, it should be preferred over the approximate affine-invariant Riemannian distance. Numerical experiments on image classification demonstrate significant improvements of the infinite-dimensional formulation over the finite-dimensional counterpart. Given the numerous applications of covariance matrices in many areas of mathematics, statistics, and machine learning, just to name a few, we expect that the infinite-dimensional covariance operator formulation presented here will have many more applications beyond those in computer vision.

Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)

Author : Aboul-Ella Hassanien,Ahmad Taher Azar,Tarek Gaber,Diego Oliva,Fahmy M. Tolba
Publisher : Springer Nature
Page : 880 pages
File Size : 52,6 Mb
Release : 2020-03-23
Category : Technology & Engineering
ISBN : 9783030442897

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) by Aboul-Ella Hassanien,Ahmad Taher Azar,Tarek Gaber,Diego Oliva,Fahmy M. Tolba Pdf

This book presents the proceedings of the 1st International Conference on Artificial Intelligence and Computer Visions (AICV 2020), which took place in Cairo, Egypt, from April 8 to 10, 2020. This international conference, which highlighted essential research and developments in the fields of artificial intelligence and computer visions, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into sections, covering the following topics: swarm-based optimization mining and data analysis, deep learning and applications, machine learning and applications, image processing and computer vision, intelligent systems and applications, and intelligent networks.

Machine Learning in Computer Vision

Author : Nicu Sebe
Publisher : Springer Science & Business Media
Page : 268 pages
File Size : 54,7 Mb
Release : 2005-06-03
Category : Computers
ISBN : 1402032749

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Machine Learning in Computer Vision by Nicu Sebe Pdf

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system.In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.