Download or read book Context Aware Neural Networks for Image Classification written by Mahmoud Soliman and published by . This book was released on 2016 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Images as Data for Social Science Research written by Nora Webb Williams and published by Cambridge University Press. This book was released on 2020-08-13 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.

Download or read book Computational Intelligence in Archaeology written by Barcelo, Juan A. and published by IGI Global. This book was released on 2008-07-31 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides analytical theories offered by innovative artificial intelligence computing methods in the archaeological domain.

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 Improving Convolutional Neural Network based Image Classification by Exploiting Network Layer Information written by Daniel Lehmann and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Image Recognition and Classification written by Bahram Javidi and published by CRC Press. This book was released on 2002-06-14 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Details the latest image processing algorithms and imaging systems for image recognition with diverse applications to the military; the transportation, aerospace, information security, and biomedical industries; radar systems; and image tracking systems."

Download or read book Pulse Coupled Neural Network Based Image Classification written by Anuradha Gollamudi and published by . This book was released on 1997 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Convolutional Neural Networks written by Dominique Paul and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Image classification technology has come a long way in the last decade and has reached a performance level that no makes it very attractive to be applied to other fields as a supportive tool. To date, however, it is mainly academics with a background in computer science that are capable of applying image classification to general areas of research, as the use of the technology is mostly too complicated for academics with a non-technical background. This thesis develops an image classification tool with the goal of making image classification technology more accessible to non-technical users for it to be used as a supportive tool in their areas of research. The paper first reviews the historical developments and explains the most theoretical concepts relevant to the effective usage of the tool. Then the implementation of the tool is carefully documented via a technical explanation of the most important functions of the tool. In a final step, the performance of the different classification approaches is tested and evaluated in light of pure performance and also based on qualitative criteria. The results show that an approach called transfer-learning performs best, but also highlight that the outcome is very dependent on the data the tool is being applied to.

Download or read book Artificial Neural Networks and Evolutionary Computation in Remote Sensing written by Taskin Kavzoglu and published by MDPI. This book was released on 2021-01-19 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

Download or read book Neural Network Based Image Processing written by Preeti Lata Sahu and published by . This book was released on 2018-09-22 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Tunnels and Underground Cities Engineering and Innovation Meet Archaeology Architecture and Art written by Daniele Peila and published by CRC Press. This book was released on 2019-04-17 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art contains the contributions presented at the World Tunnel Congress 2019 (Naples, Italy, 3-9 May 2019). The use of underground space is continuing to grow, due to global urbanization, public demand for efficient transportation, and energy saving, production and distribution. The growing need for space at ground level, along with its continuous value increase and the challenges of energy saving and achieving sustainable development objectives, demand greater and better use of the underground space to ensure that it supports sustainable, resilient and more liveable cities. This vision was the source of inspiration for the design of the logos of both the International (ITA) and Italian (SIG) Tunnelling Association. By placing key infrastructures underground – the black circle in the logos – it will be possible to preserve and enhance the quality of the space at ground level – the green line. In order to consider and value underground space usage together with human and social needs, engineers, architects, and artists will have to learn to collaborate and develop an interdisciplinary design approach that addresses functionality, safety, aesthetics and quality of life, and adaptability to future and varied functions. The 700 contributions cover a wide range of topics, from more traditional subjects connected to technical challenges of design and construction of underground works, with emphasis on innovation in tunneling engineering, to less conventional and archetypically Italian themes such as archaeology, architecture, and art. The book has the following main themes: Archaeology, Architecture and Art in underground construction; Environment sustainability in underground construction; Geological and geotechnical knowledge and requirements for project implementation; Ground improvement in underground constructions; Innovation in underground engineering, materials and equipment; Long and deep tunnels; Public communication and awareness; Risk management, contracts and financial aspects; Safety in underground construction; Strategic use of underground space for resilient cities; Urban tunnels. Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art is a valuable reference text for tunneling specialists, owners, engineers, architects and others involved in underground planning, design and building around the world, and for academics who are interested in underground constructions and geotechnics.

Download or read book Neural Network Image Classification Based on Optimal Feature Extraction and the Theory of Rough Sets written by Jinwoo Lee and published by . This book was released on 1994 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Searching for Patterns in Remote Sensing Image Databases Using Neural Networks written by Justin D. Paola and published by . This book was released on 1995 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery."

Download or read book Neural Network Based Object Recognition in Images written by D. Z. Badal and published by . This book was released on 1992 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning written by Wei Tao Chen and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Image semantic segmentation is an important problem in computer vision. However, Training a deep neural network for semantic segmentation in supervised learning requires expensive manual labeling. Active learning (AL) addresses this problem by automatically selecting a subset of the dataset to label and iteratively improve the model. This minimizes labeling costs while maximizing performance. Yet, deep active learning for image segmentation has not been systematically studied in the literature. This thesis offers three contributions. First, we compare six different state-of-the-art querying methods, including uncertainty, Bayesian, and out-of-distribution methods, in the context of active learning for image segmentation. The comparison uses the standard dataset Cityscapes, as well as randomly generated data, and the state-of-the-art image segmentation architecture DeepLab. Our results demonstrate subtle but robust differences between the querying methods, which we analyze and explain. Second, we propose a novel way to query images by counting the number of pixels with acquisition values above a certain threshold. Our counting method outperforms the standard averaging method. Lastly, we demonstrate that the previous two findings remain consistent for both whole images and image crops. Furthermore, we provide an in-depth discussion of deep active learning and results from supplementary experiments. First, we studied active learning in the context of image classification with the MNIST dataset. We observed an interesting phenomenon where active learning querying methods perform worse than random sampling in the early cycles but overtake random sampling at a break-even point. This break-even point can be controlled by varying model capacity, sample diversity, and temperature scaling. The difference in performances of the six querying methods is larger than in the case of image segmentation. Second, we attempt to explore the theoretical optimal query by querying samples with the lowest accuracy and querying with a trained expert model. Although they turned out to be suboptimal, their results would hopefully shed light on the subject. Lastly, we present the experiment results from using SegNet and FCN. With these architectures, our querying methods did not perform any better than random sampling. Nevertheless, those negative results demonstrate some of the difficulties of active learning for image segmentation.

Download or read book ImageNet Classification with Complementary Networks written by Zhuotun Zhu and published by . This book was released on 2016 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are interested in training complementary networks for large-scale image classification. This is motivated by the observation that deep neural networks learn different visual concepts from the foreground and background regions (defined by the object bounding box) of natural images. We construct complementary training image sets for this purpose. That is to say, in each training image, either the foreground or background region, defined by object bounding boxes, is removed. We train deep neural networks on these modified datasets, and show the possibility of image classification even using a network trained on pure background visual contents. We visualize the neural networks, and demonstrate that networks trained with different datasets capture complementary information. These complementary networks are combined at the testing stage on two conditions with and without bounding box(es), producing remarkable gain in recognition accuracy.

Download or read book Communication Efficient Federated Learning for Convolutional Neural Network Based Collaborative Image Classification written by and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: