Download or read book Face Image Analysis with Convolutional Neural Networks written by Stefan Duffner and published by GRIN Verlag. This book was released on 2009-08 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doctoral Thesis / Dissertation from the year 2008 in the subject Computer Science - Applied, grade: 1, University of Freiburg (Lehrstuhl für Mustererkennung und Bildverarbeitung), language: English, abstract: In this work, we present the problem of automatic appearance-based facial analysis with machine learning techniques and describe common specific sub-problems like face detection, facial feature detection and face recognition which are the crucial parts of many applications in the context of indexation, surveillance, access-control or human-computer interaction. To tackle this problem, we particularly focus on a technique called Convolutional Neural Network (CNN) which is inspired by biological evidence found in the visual cortex of mammalian brains and which has already been applied to many different classi fication problems. Existing CNN-based methods, like the face detection system proposed by Garcia and Delakis, show that this can be a very effective, efficient and robust approach to non-linear image processing tasks. An important step in many automatic facial analysis applications, e.g. face recognition, is face alignment which tries to translate, scale and rotate the face image such that specific facial features are roughly at predefined positions in the image. We propose an efficient approach to this problem using CNNs and experimentally show its very good performance on difficult test images. We further present a CNN-based method for automatic facial feature detection. The proposed system employs a hierarchical procedure which first roughly localizes the eyes, the nose and the mouth and then refines the result by detecting 10 different facial feature points. The detection rate of this method is 96% for the AR database and 87% for the BioID database tolerating an error of 10% of the inter-ocular distance. Finally, we propose a novel face recognition approach based on a specific CNN architecture learning a non-linear mapping of the image space into a lower-dim

Download or read book Face Image Analysis with Convolutional Neural Networks written by Stefan Duffner and published by GRIN Verlag. This book was released on 2009-08-12 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doctoral Thesis / Dissertation from the year 2008 in the subject Computer Science - Applied, grade: 1, University of Freiburg (Lehrstuhl für Mustererkennung und Bildverarbeitung), language: English, abstract: In this work, we present the problem of automatic appearance-based facial analysis with machine learning techniques and describe common specific sub-problems like face detection, facial feature detection and face recognition which are the crucial parts of many applications in the context of indexation, surveillance, access-control or human-computer interaction. To tackle this problem, we particularly focus on a technique called Convolutional Neural Network (CNN) which is inspired by biological evidence found in the visual cortex of mammalian brains and which has already been applied to many different classi fication problems. Existing CNN-based methods, like the face detection system proposed by Garcia and Delakis, show that this can be a very effective, efficient and robust approach to non-linear image processing tasks. An important step in many automatic facial analysis applications, e.g. face recognition, is face alignment which tries to translate, scale and rotate the face image such that specific facial features are roughly at predefined positions in the image. We propose an efficient approach to this problem using CNNs and experimentally show its very good performance on difficult test images. We further present a CNN-based method for automatic facial feature detection. The proposed system employs a hierarchical procedure which first roughly localizes the eyes, the nose and the mouth and then refines the result by detecting 10 different facial feature points. The detection rate of this method is 96% for the AR database and 87% for the BioID database tolerating an error of 10% of the inter-ocular distance. Finally, we propose a novel face recognition approach based on a specific CNN architecture learning a non-linear mapping of the image space into a lower-dimensional sub-space where the different classes are more easily separable. We applied this method to several public face databases and obtained better recognition rates than with classical face recognition approaches based on PCA or LDA. We also present a CNN-based method for the binary classification problem of gender recognition with face images and achieve a state-of-the-art accuracy. The results presented in this work show that CNNs perform very well on various facial image processing tasks, such as face alignment, facial feature detection and face recognition and clearly demonstrate that the CNN technique is a versatile, efficient and robust approach for facial image analysis.

Download or read book AI 2003 Advances in Artificial Intelligence written by Tamas D. Gedeon and published by Springer Science & Business Media. This book was released on 2003-11-24 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th Australian Conference on Artificial Intelligence, AI 2003, held in Perth, Australia in December 2003. The 87 revised full papers presented together with 4 keynote papers were carefully reviewed and selected from 179 submissions. The papers are organized in topical sections on ontologies, problem solving, knowledge discovery and data mining, expert systems, neural network applications, belief revision and theorem proving, reasoning and logic, machine learning, AI applications, neural computing, intelligent agents, computer vision, medical applications, machine learning and language, AI and business, soft computing, language understanding, and theory.

Download or read book Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments written by Raj, Alex Noel Joseph and published by IGI Global. This book was released on 2020-12-25 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

Download or read book Video Analytics Face and Facial Expression Recognition written by Xiang Bai and published by Springer. This book was released on 2019-01-18 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Third Workshop on Face and Facial Expression Recognition from Real World Videos, FFER 2018, and the Second International Workshop on Deep Learning for Pattern Recognition, DLPR 2018, held at the 24th International Conference on Pattern Recognition, ICPR 2018, in Beijing, China, in August 2018. The 7 papers presented in this volume were carefully reviewed and selected from 9 submissions. They deal with topics such as histopathological images, action recognition, scene text detection, speech recognition, object classification, presentation attack detection, and driver drowsiness detection.

Download or read book Next Generation of Internet of Things written by Raghvendra Kumar and published by Springer Nature. This book was released on 2021-06-14 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes selected papers from the International Conference on Next Generation of Internet of Things (ICNGIoT 2021), organized by the Department of Computer Science and Engineering, School of Engineering, GIET University, Gunupur, Odisha, India, during 5–6 February 2021. The book covers topics such as IoT network design and architecture, IoT network virtualization, IoT sensors, privacy and security for IoT, SMART environment, social networks, data science and data analytics, cognitive intelligence and augmented intelligence, and case studies and applications.

Download or read book Robot Vision written by A. Pugh and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past five years robot vision has emerged as a subject area with its own identity. A text based on the proceedings of the Symposium on Computer Vision and Sensor-based Robots held at the General Motors Research Laboratories, Warren, Michigan in 1978, was published by Plenum Press in 1979. This book, edited by George G. Dodd and Lothar Rosso!, probably represented the first identifiable book covering some aspects of robot vision. The subject of robot vision and sensory controls (RoViSeC) occupied an entire international conference held in the Hilton Hotel in Stratford, England in May 1981. This was followed by a second RoViSeC held in Stuttgart, Germany in November 1982. The large attendance at the Stratford conference and the obvious interest in the subject of robot vision at international robot meetings, provides the stimulus for this current collection of papers. Users and researchers entering the field of robot vision for the first time will encounter a bewildering array of publications on all aspects of computer vision of which robot vision forms a part. It is the grey area dividing the different aspects of computer vision which is not easy to identify. Even those involved in research sometimes find difficulty in separating the essential differences between vision for automated inspection and vision for robot applications. Both of these are to some extent applications of pattern recognition with the underlying philosophy of each defining the techniques used.

Download or read book Strengthening Deep Neural Networks written by Katy Warr and published by "O'Reilly Media, Inc.". This book was released on 2019-07-03 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

Download or read book Deep Learning for Image Processing Applications written by D.J. Hemanth and published by IOS Press. This book was released on 2017-12 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Download or read book Computer Analysis of Images and Patterns written by Xiaoyi Jiang and published by . This book was released on 2009 with total page 1249 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the bienially held International Conference on Computer Analysis of Images and Patterns, CAIP 2009, which took place in Münster, Germany, September 2-4, 2009. The 148 papers presented together with 2 invited talks were carefully reviewed and selected from 405 submissions. The papers are organized in topical section on: biometrics, calibration, document analysis, features, graph representations, image processing, image registration, image and video retrieval, medical imaging, object and scene recognition, pattern recognition, shape recovery, segmentation, stereo and video analysis, texture analysis, and applications.

Download or read book Practical Machine Learning and Image Processing written by Himanshu Singh and published by Apress. This book was released on 2019-02-26 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 LearnDiscover 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.

Download or read book Advances in Face Image Analysis written by Fadi Dornaika and published by Bentham Science Publishers. This book was released on 2016-03-02 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Face Image Analysis: Theory and applications describes several approaches to facial image analysis and recognition. Eleven chapters cover advances in computer vision and pattern recognition methods used to analyze facial data. The topics addressed in this book include automatic face detection, 3D face model fitting, robust face recognition, facial expression recognition, face image data embedding, model-less 3D face pose estimation and image-based age estimation. The chapters are also written by experts from a different research groups. Readers will, therefore, have access to contemporary knowledge on facial recognition with some diverse perspectives offered for individual techniques. The book is a useful resource for a wide audience such as i) researchers and professionals working in the field of face image analysis, ii) the entire pattern recognition community interested in processing and extracting features from raw face images, and iii) technical experts as well as postgraduate computer science students interested in cutting edge concepts of facial image recognition.

Download or read book Fourth International Conference on Image Processing and Capsule Networks written by Subarna Shakya and published by Springer Nature. This book was released on 2023-12-19 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high-quality research papers presented at the Fourth International Conference on Image Processing and Capsule Networks (ICIPCN 2023), which is held in Bangkok, Thailand, during 10–11 August 2023. This book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations.

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2017-01-18 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Download or read book Progress in Pattern Recognition Image Analysis Computer Vision and Applications written by Marcelo Mendoza and published by Springer. This book was released on 2018-02-09 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017, held in Valparaíso, Chile, in November 2017. The 87 papers presented were carefully reviewed and selected from 156 submissions. The papers feature research results in the areas of pattern recognition, image processing, computer vision, multimedia and related fields.

Download or read book Face Image Analysis by Unsupervised Learning written by Marian Stewart Bartlett and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.