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• Discrete geometry and topology.- Tilings and patterns.- Grammars, models and other technical tools for image analysis.- Image segmentation.- Classification.- Reconstruction.- Compression.- Texture analysis.-Bioimaging.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 18th International Workshop on Combinatorial Image Analysis, IWCIA 2017, held in Plovdiv, Bulgaria, in June 2017. The 27 revised full papers presented were carefully reviewed and selected from 47 submissions. The workshop is organized in topical sections of theoretical foundations and theory of applications, namely: discrete geometry and topology; tilings and patterns; grammars, models and other technical tools for image analysis; image segmentation, classification; reconstruction; compression; texture analysis; bioimaging.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• Segmentation and Classification.- Segmentation and Semantic Segmentation.- Dictionary Learning, Retrieval, and Clustering.- Deep Learning.- People Tracking and Action Recognition.- People and Actions.- Faces.- Computational Photography.- Face and Gestures.- Image Alignment.- Computational Photography and Image Processing.- Language and Video.- 3D Computer Vision.- Image Attributes, Language, and Recognition.- Video Understanding.- 3D Vision.
• (source: Nielsen Book Data)

The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.
(source: Nielsen Book Data)

• New Trends in Image Restoration and Enhancement (NTIRE).- Workshop on Assistive Vision.- Hyperspectral Image and Signal Processing.- Computer Vision Technologies for Smart Vehicle.- Spontaneous Facial Behavior Analysis.- 3D Modelling and Applications.
• (source: Nielsen Book Data)

The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected .
(source: Nielsen Book Data)

• New Trends in Image Restoration and Enhancement (NTIRE).- Workshop on Assistive Vision.- Hyperspectral Image and Signal Processing.- Computer Vision Technologies for Smart Vehicle.- Spontaneous Facial Behavior Analysis.- 3D Modelling and Applications.
• (source: Nielsen Book Data)

The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected .
(source: Nielsen Book Data)

• New Trends in Image Restoration and Enhancement (NTIRE).- Workshop on Assistive Vision.- Hyperspectral Image and Signal Processing.- Computer Vision Technologies for Smart Vehicle.- Spontaneous Facial Behavior Analysis.- 3D Modelling and Applications.
• (source: Nielsen Book Data)

The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected .
(source: Nielsen Book Data)

• Algebraic Theory, Max-Plus and Max-Min Mathematics.- Discrete Geometry and Discrete Topology.- Watershed and Graph-based Segmentation.- Trees and Hierarchies.- Topological and Graph-based Clustering, Classification and Filtering.- Connected Operators and Attribute Filters.- PDE-based Morphology.- Scale-Space Representations and Nonlinear Decompositions.- Computational Morphology.- Object Detection.- Biomedical, Material Science and Physical Applications.
• (source: Nielsen Book Data)

This book contains the refereed proceedings of the 13th International Symposium on Mathematical Morphology, ISMM 2017, held in Fontainebleau, France, in May 2017. The 36 revised full papers presented together with 4 short papers were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections on algebraic theory, max-plus and max-min mathematics; discrete geometry and discrete topology; watershed and graph-based segmentation; trees and hierarchies; topological and graph-based clustering, classification and filtering; connected operators and attribute filters; PDE-based morphology; scale-space representations and nonlinear decompositions; computational morphology; object detection; and biomedical, material science and physical applications.
(source: Nielsen Book Data)

• Pattern Recognition and Machine Learning.- Computer Vision.- Image and Signal Processing.- Medical Image.-Applications.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2017, held in Faro, Portugal, in June 2017. The 60 regular papers presented in this volume were carefully reviewed and selected from 86 submissions. They are organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Medical Image; and Applications.
(source: Nielsen Book Data)

• Clustering and Quantum Methods.- Motion and Tracking.- Image Processing and Segmentation.- Color, Shading and Reflectance of Light.- Propagation and Time-evolution.- Inference, Labeling, and Relaxation.
• (source: Nielsen Book Data)

This volume constitutes the refereed proceedings of the 11th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2017, held in Venice, Italy, in October/November 2017. The 37 revised full papers were carefully reviewed and selected from 51 submissions. The papers are organized in topical sections on Clustering and Quantum Methods; Motion and Tracking; Image Processing and Segmentation; Color, Shading and Reflectance of Light; Propagation and Time-evolution; and Inference, Labeling, and Relaxation.
(source: Nielsen Book Data)

• A Study of Energy Reduction Strategies in Renewable Hybrid Grid.- Classification and Watermarking of Brain Tumor using Artificial and Convolutional Neural Networks.- A Proposal for a Deep Learning Model to Enhance Student Guidance and Reduce Dropout.- EduBot: An Unsupervised Domain-Specific Chatbot for Educational Institutions.- SQL Generation from Natural Language using Supervised Learning and Recurrent Neural Networks.- Toward Intelligent Solution to Identify Learner Attitude from Source Code.
• (source: Nielsen Book Data)

This book gathers selected papers from Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by ENSAM-Meknes at Moulay Ismail University, Morocco. The 29 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as digital twin, multiagent systems, deep learning, image processing and analysis, control, prediction, modeling, optimization and design, as well as AI applications in industry, health, energy, agriculture, and education. The book is intended for AI experts, offering them a valuable overview and global outlook for the future, and highlights a wealth of innovative ideas and recent, important advances in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• A Fuzzy Ontology Based Approach To Support Product Eco-Design.-
• A Genetic-based SVM Approach for Quality Data Classification.- Towards a platform to implement an intelligent and predictive maintenance in the context of industry.- Towards a Prediction Analysis in an Industrial Context.- Methodology for Implementation of Industry . Technologies in Supply Chain for SMEs.- A Deep Reinforcement Learning DRL Decision Model For Heating Process Parameters Identification In Automotive Glass Manufacturing
• Analytic Hierarchy Process AHP for supply chain . risks management.- SmartDFRelevance: a Holonic Agent based System for Engineering Industrial Projects in Concurrent Engineering Context.- A Cyber-Physical Warehouse Management System Architecture in an.- Industry . context.- PLM and Smart technologies for product and supply chain design.- Production systems simulation considering Non-Productive Times and Human Factors.- Distributed and Embedded System to Control Traffic Collision Based on Artificial Intelligence.- The Emergence and Decision Support in Complex System with Fuzzy Logic Control.- Markov Decision Processes with Discounted Costs over a Finite Horizon: Action Elimination.- Robust adaptive fuzzy path tracking control of Differential Wheeled Mobile Robot.- Deep Learning approach for Automated Guided Vehicle System.- Path Planning Using Particle Swarm Optimization And Fuzzy Logic
• Prediction of Robot localization states using Hidden Markov Models.- A New Approach for Multi-Agent Reinforcement Learning.- Recommender System for Most Relevant k Pick-Up Points.- Feature Detection and Tracking for Visual Effects: Augmented Reality and Video Stabilization.- Spectral image recognition using artificial dynamic neural network in information resonance mode.- U-Net Based Model for Obfuscated Human Faces Reconstruction.- A Machine Learning Assistant for Choosing Operators and Tuning Their Parameters in Image Processing Tasks.- Convergence and parameters setting of continuous Hopfield neural networks applied to image restoration problem.- The Ibn Battouta Air Traffic Control Corpus with Real Life ADS-B and METAR data.- Weed Recognition System For Low-Land Rice Precision Farming Using Deep Learning Approach.- A Comparative Study Between Mixture and Stepwise Regression to Model The Parameters Of The Composting Process.- Deep Learning Based Sponge Gourd Diseases Recognition For Commercial Cultivation in Bangladesh.- Exploitation of vegetation indices and Random Forest for cartography of rosemary cover: application to Gourrama region, Morocco.
• (source: Nielsen Book Data)

This book gathers the refereed proceedings of the Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by the ENSAM-Meknes at Moulay Ismail University, Morocco. The 30 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as smart manufacturing, smart maintenance, smart supply chain management, supervised learning, unsupervised learning, reinforcement learning, graph-based and semi-supervised learning, neural networks, deep learning, planning and optimization, and other AI applications. The book is intended for AI experts, offering them a valuable overview of the status quo and a global outlook for the future, with many new and innovative ideas and recent important developments in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• 1. A Comprehensive Introduction to Photometric 3D-Reconstruction.-
• 2. Perspective Shape from Shading an Exposition on Recent Works with New Experiments.-
• 3. RGBD-Fusion: Depth Refinement for Diffuse and Specular Objects.-
• 4. Non-Rigid Structure from Motion and Shading.-
• 5. On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting.-
• 6. Recent Progress in Shape from Polarization.-
• 7. Estimating Facial Aging using Light Scattering Photometry.
• (source: Nielsen Book Data)

This book presents the latest advances in photometric 3D reconstruction. It provides the reader with an overview of the state of the art in the field, and of the latest research into both the theoretical foundations of photometric 3D reconstruction and its practical application in several fields (including security, medicine, cultural heritage and archiving, and engineering). These techniques play a crucial role within such emerging technologies as 3D printing, since they permit the direct conversion of an image into a solid object. The book covers both theoretical analysis and real-world applications, highlighting the importance of deepening interdisciplinary skills, and as such will be of interest to both academic researchers and practitioners from the computer vision and mathematical 3D modeling communities, as well as engineers involved in 3D printing. No prior background is required beyond a general knowledge of classical computer vision models, numerical methods for optimization, and partial differential equations. .
(source: Nielsen Book Data)

• Facial Age Estimation Using Compact Facial Features.- A Vision Based Hardware-Software Real-Time Control System for the Autonomous Landing of an UAV.- Exploration of Ear Biometrics with Deep Learning.- Does Randomness in the Random Visual Cryptography Protocol Depend on the Period of Pseudorandom Number Generators.- Software for CT-Image Analysis to Assist the Choice of Mechanical-Ventilation Settings in Acute Respiratory Distress Syndrome.- Edge-Aware Color Image Manipulation by Combination of Low-pass Linear Filter and Morphological Processing of its Residuals.- On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers a Simulation Study.- A New Sport Teams Logo Dataset for Detection Tasks.- Scene Recognition Using AlexNet to Recognize Significant Events within Cricket Game Footage.- Biologically Inspired Modelling of Flowers.- Tuberculosis Abnormality Detection in Chest X-Rays: A Deep Learning Approach.- Dynamic Local Ternary Patterns for Gender Identification using Facial Components.- Depth Perception Tendencies in the 3-D Environment of Virtual Reality.- Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking.- Nuclei Detection with Local Threshold Processing in DAB&H Stained Breast Cancer Biopsy Images.- Video Footage Highlight Detection in Formula 1 Through Vehicle Recognition with Faster R-CNN Trained On Game Footage.- Performance Evaluation of Selected Detector-Descriptor Combinations.- Watermarking Method for Video Authentication with Tamper Localization.- RGB-D and Lidar Calibration Supported by GPU.- Video-Surveillance Tools for Monitoring Social Responsibility Under Covid-19 Restrictions.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the International Conference on Computer Vision and Graphics, ICCVG 2020, held in Warsaw, Poland, in September 2020. The 20 full papers were selected from 49 submissions. The contributions cover topics such as: modelling of human visual perception; computational geometry; geometrical models of objects and scenes; illumination and reflection models and methods; image formation; image and video coding; image filtering and enhancement; biomedical image processing; biomedical graphics; colour image processing; multispectral image processing; pattern recognition in image processing; scene understanding; motion analysis, visual navigation and active vision; human motion detection and analysis; visualisation and graphical data presentation; hardware and architectures for image processing; computer-aided graphic design; 3D imaging, shading and rendering; computer animation; graphics for internet and mobile systems; virtual reality; image and video databases; digital watermarking; multimedia applications; and computer art. Due to the Corona pandemic ICCVG 2020 was held as a virtual event.
(source: Nielsen Book Data)

• Character and text recognition.- Document image processing.- Segmentation and layout analysis.- Word embedding and spotting.- Text detection.- Font design and classification.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 14th IAPR International Workshop on Document Analysis Systems, DAS 2020, held in Wuhan, China, in July 2020. The 40 full papers presented in this book were carefully reviewed and selected from 57 submissions. The papers are grouped in the following topical sections: character and text recognition; document image processing; segmentation and layout analysis; word embedding and spotting; text detection; and font design and classification. Due to the Corona pandemic the conference was held as a virtual event .
(source: Nielsen Book Data)

• Machine Learning
• Medical Image and Analysis
• Analysis of Histopathology Images
• Diagnosis and Screening of Ophthalmic Diseases
• Grand Challenge on Automatic Lung Cancer Patient Management.

This two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020. The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management. Due to the corona pandemic, ICIAR 2020 was held virtually only.

• 1. Introduction.-
• 2. Machine Learning Methods for Spatial and Temporal Parameter Estimation.-
• 3. Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms.-
• 4. Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine.-
• 5. Advances in Deep Learning for Hyperspectral Image Analysis - Addressing Challenges Arising in Practical Imaging Scenarios.-
• 6. Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.-
• 7. Supervised, Semi Supervised and Unsupervised Learning for Hyperspectral Regression.-
• 8. Sparsity-based Methods for Classification.-
• 9. Multiple Kernel Learning for Hyperspectral Image Classification.-
• 10. Low Dimensional Manifold Model in Hyperspectral Image Reconstruction.-
• 11. Deep Sprase Band Selection for Hyperspectral Face Recognition.-
• 12. Detection of Large-Scale and Anomalous Changes.-
• 13. Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning.-
• 14. Chapter Hyperspectral-Multispectral Image Fusion Enhancement Based on Deep Learning.-
• 15. Automatic Target Detection for Sparse Hyperspectral Images.
• (source: Nielsen Book Data)

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful. .
(source: Nielsen Book Data)

Extract patterns and knowledge from your data in easy way using MATLAB About This Book * Get your first steps into machine learning with the help of this easy-to-follow guide * Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB * Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn * Learn the introductory concepts of machine learning. * Discover different ways to transform data using SAS XPORT, import and export tools, * Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. * Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. * Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. * Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. * Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
(source: Nielsen Book Data)

• Chapter 1: Installation and Environment Setup
• Chapter Goal: Making System Ready for Image Processing and Analysis
• No of pages 20
• Sub -Topics (Top 2)
• 1.
• Installing Jupyter Notebook
• 2.
• Installing OpenCV and other Image Analysis dependencies
• 3.
• Installing Neural Network Dependencies
• Chapter 2: Introduction to Python and Image Processing
• Chapter Goal: Introduction to different concepts of Python and Image processing Application on it.
• No of pages: 50
• Sub - Topics (Top 2)
• 1. Essentials of Python
• 2. Terminologies related to Image Analysis
• Chapter 3: Advanced Image Processing using OpenCV
• Chapter Goal: Understanding Algorithms and their applications using Python
• No of pages: 100
• Sub - Topics (Top 2):
• 1.
• Operations on Images
• 2.
• Image Transformations
• Chapter 4: Machine Learning Approaches in Image Processing
• Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario
• No of pages: 100
• Sub - Topics (Top 2):
• 1.
• Image Classification and Segmentation
• 2.
• Applying Supervised and Unsupervised Learning approaches on Images using Python
• Chapter 5: Real Time Use Cases
• Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book
• No of pages: 100
• Sub - Topics (Top 5):
• 1. Facial Detection
• 2. Facial Recognition
• 3. Hand Gesture Movement Recognition
• 4. Self-Driving Cars Conceptualization: Advanced Lane Finding
• 5. Self-Driving Cars Conceptualization: Traffic Signs Detection
• Chapter 6: Appendix A
• Chapter Goal: Advanced concepts Introduction
• No of pages: 50
• Sub - Topics (Top 2):
• 1. AdaBoost and XGBoost
• 2. Pulse Coupled Neural Networks.
• (source: Nielsen Book Data)

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
(source: Nielsen Book Data)