Download or read book Neural Models of Multi scale Image Completion and of Featural Bias During Attentive Memory Search written by Sai Chaitanya Gaddam and published by . This book was released on 2009 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation develops neural network models for vision and recognition, and tests model performance on large-scale images. The first project introduces CONFIGR (CONtour FIgure GRound), a computational model based on principles of biological vision. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting figure is fed back to the "early vision" stage for long-range completion via filling-in. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel. Once the pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images. The second project considers a problem faced by online learning systems, which may be presented with input patterns only once during training. In this situation, later training may be distorted by undue attention to initial subsets of features that were useful for earlier memory encoding. During learning, Adaptive Resonance Theory (ART) models encode attended featural subsets, called critical feature patterns. When a novel input activates an established category, only the input features present in the critical pattern remain active in working memory. Biased ARTMAP (bARTMAP) is a neural network that solves the problem of over-emphasis on early features by biasing attention away from previously attended features once an input has made a predictive error. Simulations on a variety of benchmark problems demonstrate that adding biasing to ARTMAP search improves recognition accuracy.

Download or read book Multi modal Information Extraction and Fusion with Convolutional Neural Networks for Classification of Scaled Images written by Dinesh Kumar and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developing computational algorithms to model the biological vision system has challenged researchers in the computer vision field for several decades. As a result, state-of-the-art Deep Learning (DL) algorithms such as the Convolutional Neural Network (CNN) have emerged for image classification and recognition tasks with promising results. CNNs, however, remain view-specific, producing good results when the variation between test and train data is small. Making CNNs learn invariant features to effectively recognise objects that undergo appearance changes as a result of transformations such as scaling remains a technical challenge. Recent bio-inspired studies of the visual system are suggesting three new paradigms. Firstly, our visual system uses both local features and global features in its recognition function. Secondly, cells tuned to detecting global features respond to visual stimuli prior to cells tunedon local features leading to quicker response times in recognising objects. Thirdly, information from modalities that handle local features, global features and color are integrated in the brain for performing recognition tasks. While CNNs rely on an aggregation of local features into global features for recognition, these research outcomes motivate global feature extraction and with established local features to improve the efficiency and CNN model application to solve transformation invariance problems.The main goals of the current research include an investigation and development of relevant models for classification of scaled images using both local and global features with CNNs. To improve the performance of the current CNN model towards classification of scaled images, this work has performed investigations on different techniques: (i) exploration of (global) high-level, low-resolution CNN feature map augmentation, (ii) examination of fusion of CNN features with global features from non-trainable global feature descriptors, (iii) color histogram as global features, (iii) examination of fusion of CNN features with spatial features using large kernels in a multi-scale filter pyramid setting, (v) examination of brain-inspired distributed multi-modal information extraction and integration model, and (vi) development of a zoom-in convolution algorithm.For improving classification of scaled images, this thesis has proposed two specific techniques. The first technique exploits the automatic feature extraction in CNN convolution layers and proposes augmentation of (global) high-level low-resolution feature maps as a cheap and effective way to improveclassification of scaled images. The second technique proposes an architecture supported by physiological evidence that allows multi-modal information extraction and fusion of DL models for combining global features and CNN local features. This architecture allows parallel extraction and processing of CNN and global features from input image data. To extract global image features, both non-trainable and trainable feature extraction methods are investigated. Global feature descriptors - Histogram of Gradients (HOG) and color information - are used as non-trainable methods. A technique using multi-scale filter banks containing large kernels are used as trainable method to cover more spatial areas of the image. The idea of using large kernels and multi-scale filter banks is extended to develop a new lightweight zoom-in convolution technique that allows the model capture more spatial areas in relation to the center of theimage, assuming the object of interest is generally centered in the middle of the image. This technique called DeepZoom inspects multi-scale slices of an image beginning with a set of center pixels and progressively extending the area of each slice until the final slice covers the entire image. To fuse global, local and color features, a simple feature map concatenation technique is compared with a brain-inspired distribution information integration model. Four datasets consisting of different sized images in each are used to validate the models.Experiments on a) (global) high-level low-resolution feature map augmentation, b) fusion of CNN local features with global features from various non-trainable global feature descriptors methods, c) fusion of CNN local features with spatial features from using large kernels, and d) adjusting the convolution technique in DL models, have shown the developed models compared to CNN only based models i) obtained comparatively similar if not better training test accuracies and ii) obtained higher classification accuracies for scaled test images. Whilst global feature extraction or manipulation methods differed, in general the results are promising for classification of scaled images. In all the cases, the developed models are evaluated against established benchmark results from benchmark CNNs. Finally, this thesis presents skin cancer classification as an application where handling scale is important. It shows application of developed DL models on detection of skin cancer using skin lesion images on mobile phones. By investigating the different models, a suitable DL model has been presented for classification of skin lesion images in real time and provides an implementation on mobile devices as an early warning diagnosis tool for skin cancer.The thesis concludes with a summary of research outcomes against each identified research question. Several questions emanating from the thesis research are also identified to extend the research presented as future work.

Download or read book Hierarchical Neural Networks for Image Interpretation written by Sven Behnke and published by Springer. This book was released on 2003-11-18 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.

Download or read book Spatial Biases in Perception and Cognition written by Timothy L. Hubbard and published by Cambridge University Press. This book was released on 2018-08-23 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerous spatial biases influence navigation, interactions, and preferences in our environment. This volume considers their influences on perception and memory.

Download or read book Person Re Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.

Download or read book Program Synthesis written by Sumit Gulwani and published by . This book was released on 2017-07-11 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Program synthesis is the task of automatically finding a program in the underlying programming language that satisfies the user intent expressed in the form of some specification. Since the inception of artificial intelligence in the 1950s, this problem has been considered the holy grail of Computer Science. Despite inherent challenges in the problem such as ambiguity of user intent and a typically enormous search space of programs, the field of program synthesis has developed many different techniques that enable program synthesis in different real-life application domains. It is now used successfully in software engineering, biological discovery, compute-raided education, end-user programming, and data cleaning. In the last decade, several applications of synthesis in the field of programming by examples have been deployed in mass-market industrial products. This monograph is a general overview of the state-of-the-art approaches to program synthesis, its applications, and subfields. It discusses the general principles common to all modern synthesis approaches such as syntactic bias, oracle-guided inductive search, and optimization techniques. We then present a literature review covering the four most common state-of-the-art techniques in program synthesis: enumerative search, constraint solving, stochastic search, and deduction-based programming by examples. It concludes with a brief list of future horizons for the field.

Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Download or read book Remote Sensing Imagery written by Florence Tupin and published by John Wiley & Sons. This book was released on 2014-02-19 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dedicated to remote sensing images, from their acquisition to their use in various applications, this book covers the global lifecycle of images, including sensors and acquisition systems, applications such as movement monitoring or data assimilation, and image and data processing. It is organized in three main parts. The first part presents technological information about remote sensing (choice of satellite orbit and sensors) and elements of physics related to sensing (optics and microwave propagation). The second part presents image processing algorithms and their specificities for radar or optical, multi and hyper-spectral images. The final part is devoted to applications: change detection and analysis of time series, elevation measurement, displacement measurement and data assimilation. Offering a comprehensive survey of the domain of remote sensing imagery with a multi-disciplinary approach, this book is suitable for graduate students and engineers, with backgrounds either in computer science and applied math (signal and image processing) or geo-physics. About the Authors Florence Tupin is Professor at Telecom ParisTech, France. Her research interests include remote sensing imagery, image analysis and interpretation, three-dimensional reconstruction, and synthetic aperture radar, especially for urban remote sensing applications. Jordi Inglada works at the Centre National d’Études Spatiales (French Space Agency), Toulouse, France, in the field of remote sensing image processing at the CESBIO laboratory. He is in charge of the development of image processing algorithms for the operational exploitation of Earth observation images, mainly in the field of multi-temporal image analysis for land use and cover change. Jean-Marie Nicolas is Professor at Telecom ParisTech in the Signal and Imaging department. His research interests include the modeling and processing of synthetic aperture radar images.

Download or read book Image Processing Using Pulse Coupled Neural Networks written by Thomas Lindblad and published by Springer Science & Business Media. This book was released on 2005-08-02 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: * Weitere Angaben Verfasser: Thomas Lindblad is a professor at the Royal Institute of Technology (Physics) in Stockholm. Working and teaching nuclear and environmental physics his main interest is with sensors, signal processing and intelligent data analysis of torrent data from experiments on-line accelerators, in space, etc. Jason Kinser is an associate professor at George Mason University. He has developed a plethora of image processing applications in the medical, military, and industrial fields. He has been responsible for the conversion of PCNN theory into practical applications providing many improvements in both speed and performance

Download or read book Transfer Learning written by Qiang Yang and published by Cambridge University Press. This book was released on 2020-02-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Download or read book Beyond the Worst Case Analysis of Algorithms written by Tim Roughgarden and published by Cambridge University Press. This book was released on 2021-01-14 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.

Download or read book Image Registration for Remote Sensing written by Jacqueline Le Moigne and published by Cambridge University Press. This book was released on 2011-03-24 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a summary of current research in the application of image registration to satellite imagery. Presenting algorithms for creating mosaics and tracking changes on the planet's surface over time, it is an indispensable resource for researchers and advanced students in Earth and space science, and image processing.

Download or read book Convex Optimization written by Stephen P. Boyd and published by Cambridge University Press. This book was released on 2004-03-08 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.

Download or read book Graph Based Semi Supervised Learning written by Amarnag Lipovetzky and published by Springer Nature. This book was released on 2022-05-31 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index

Download or read book OECD Guidelines on Measuring Subjective Well being written by OECD and published by OECD Publishing. This book was released on 2013-03-20 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: These Guidelines represent the first attempt to provide international recommendations on collecting, publishing, and analysing subjective well-being data.