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 Multimodal Brain Image Fusion Methods Evaluations and Applications written by Yu Liu and published by Frontiers Media SA. This book was released on 2023-02-06 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Deep Learning Applications written by Valentina Emilia Balas and published by Springer. This book was released on 2019-03-06 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
Download or read book Medical Image Analysis written by Alejandro Frangi and published by Academic Press. This book was released on 2023-09-20 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing
Download or read book Human Centered Computing written by Qiaohong Zu and published by Springer Nature. This book was released on 2021-03-11 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes thoroughly reviewed, revised and selected papers from the 6th International Conference on Human Centered Computing, HCC 2020, held in virtually, due to COVID- 19, in December 2020. The 28 full and 20 short papers presented in this volume were carefully reviewed and selected from a total of 133 submissions. The conference focuses on the following three main themes as follows: Data such as Data Visualization, Big Data, Data Security, Hyper connectivity such as Internet of Things, Cloud Computing, Mobile Network and Collaboration such as Collective Intelligence, Peer Production, Context Awareness and much more.
Download or read book Pattern Recognition and Computer Vision written by Qingshan Liu and published by Springer Nature. This book was released on 2024-01-26 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.
Download or read book Multi Sensor Imaging and Fusion Methods Evaluations and Applications volume II written by Zhiqin Zhu and published by Frontiers Media SA. This book was released on 2024-07-24 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-sensor image fusion focuses on processing images of the same object or scene acquired by multiple sensors, in which various sensors with multi-level and multi-spatial information are complemented and combined to ultimately yield a consistent interpretation of the observed environment. In recent years, multi-sensor image fusion has become a highly active topic, and various fusion methods have been proposed. Many effective processing methods, including multi-scale transformation, fuzzy inference, and deep learning, have been introduced to design fusion algorithms. Despite the great progress, there are still some noteworthy challenges in the field, such as the lack of unified fusion theories and methods for effective generalized fusion, the lack of fault tolerance and robustness, the lack of benchmarks for performance evaluation, the lack of work on specific applications of multi-sensor image fusion, and so on.
Download or read book Segmentation and Classification of Multimodal Imagery written by Sankaranarayanam Piramanayagam and published by . This book was released on 2019 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Segmentation and classification are two important computer vision tasks that transform input data into a compact representation that allow fast and efficient analysis. Several challenges exist in generating accurate segmentation or classification results. In a video, for example, objects often change the appearance and are partially occluded, making it difficult to delineate the object from its surroundings. This thesis proposes video segmentation and aerial image classification algorithms to address some of the problems and provide accurate results. We developed a gradient driven three-dimensional segmentation technique that partitions a video into spatiotemporal objects. The algorithm utilizes the local gradient computed at each pixel location together with the global boundary map acquired through deep learning methods to generate initial pixel groups by traversing from low to high gradient regions. A local clustering method is then employed to refine these initial pixel groups. The refined sub-volumes in the homogeneous regions of video are selected as initial seeds and iteratively combined with adjacent groups based on intensity similarities. The volume growth is terminated at the color boundaries of the video. The over-segments obtained from the above steps are then merged hierarchically by a multivariate approach yielding a final segmentation map for each frame. In addition, we also implemented a streaming version of the above algorithm that requires a lower computational memory. The results illustrate that our proposed methodology compares favorably well, on a qualitative and quantitative level, in segmentation quality and computational efficiency with the latest state of the art techniques. We also developed a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also introduce a composite architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges, and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results."--Abstract.
Download or read book Image Fusion written by Gang Xiao and published by Springer Nature. This book was released on 2020-08-31 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories – pixel, feature and decision – presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.
Download or read book Big Data Analytics for Large Scale Multimedia Search written by Stefanos Vrochidis and published by John Wiley & Sons. This book was released on 2019-05-28 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search, including research methods and applications, and is structured so that readers with basic knowledge can grasp the core message while still allowing experts and specialists to drill further down into the analytical sections. Big Data Analytics for Large-Scale Multimedia Search covers: representation learning, concept and event-based video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, large-scale social multimedia analysis, privacy and audiovisual content, data storage and management for big multimedia, large scale multimedia search, multimedia tagging using deep learning, interactive interfaces for big multimedia and medical decision support applications using large multimodal data. Addresses the area of multimedia retrieval and pays close attention to the issue of scalability Presents problem driven techniques with solutions that are demonstrated through realistic case studies and user scenarios Includes tables, illustrations, and figures Offers a Wiley-hosted BCS that features links to open source algorithms, data sets and tools Big Data Analytics for Large-Scale Multimedia Search is an excellent book for academics, industrial researchers, and developers interested in big multimedia data search retrieval. It will also appeal to consultants in computer science problems and professionals in the multimedia industry.
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 2023-11-23 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest 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 cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Download or read book Advanced Intelligent Computing Technology and Applications written by De-Shuang Huang and published by Springer Nature. This book was released on with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings of the 8th International Conference on Decision Support System Technology ICDSST 2022 on Decision Support addressing modern Industry Business and Societal needs written by Jason Papathanasiou and published by EWG-DSS. This book was released on 2022-05-23 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Neural Networks and Machine Learning ICANN 2022 written by Elias Pimenidis and published by Springer Nature. This book was released on 2022-09-06 with total page 817 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.
Download or read book Fuzzy Systems and Data Mining IX written by A.J. Tallón-Ballesteros and published by IOS Press. This book was released on 2023-12-19 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy systems and data mining are indispensible aspects of the digital technology on which we now all depend. Fuzzy logic is intrinsic to applications in the electrical, chemical and engineering industries, and also in the fields of management and environmental issues. Data mining is indispensible in dealing with big data, massive data, and scalable, parallel and distributed algorithms. This book presents the proceedings of FSDM 2023, the 9th International Conference on Fuzzy Systems and Data Mining, held from 10-13 November 2023 as a hybrid event, with some participants attending in Chongqing, China, and others online. The conference focuses on four main areas: fuzzy theory, algorithms and systems; fuzzy application; data mining; and the interdisciplinary field of fuzzy logic and data mining, and provides a forum for experts, researchers, academics and representatives from industry to share the latest advances in the field of fuzzy sets and data mining. This year, topics from two special sessions on granular-ball computing and the application of generative AI, as well as machine learning and neural networks, were also covered. A total of 363 submissions were received, and after careful review by the members of the international program committee, 110 papers were accepted for presentation at the conference and publication here, representing an acceptance rate of just over 30%. Covering a comprehensive range of current research and developments in fuzzy logic and data mining, the book will be of interest to all those working in the field of data science.
Download or read book Communications Signal Processing and Systems written by Qilian Liang and published by Springer Nature. This book was released on 2022-03-30 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together papers presented at the 2021 International Conference on Communications, Signal Processing, and Systems, which provides a venue to disseminate the latest developments and to discuss the interactions and links between these multidisciplinary fields. Spanning topics ranging from communications, signal processing and systems, this book is aimed at undergraduate and graduate students in Electrical Engineering, Computer Science and Mathematics, researchers and engineers from academia and industry as well as government employees (such as NSF, DOD and DOE).
Download or read book Computer Vision ECCV 2018 Workshops written by Laura Leal-Taixé and published by Springer. This book was released on 2019-01-22 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.
