Download or read book Saliency Ranking Using Deep Learning written by Mahmoud Kalash and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Salient object detection is a problem that has been considered in detail and many solutions proposed. In this thesis, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried which implies a relative rank exists on salient objects. In this thesis, we solve this more general problem that considers relative rank. A novel deep learning solution is proposed based on a hierarchical representation of relative saliency and stage-wise refinement to address both of the saliency ranking and subitizing tasks. We also present methods for deriving suitable ranked salient object instances to generate a large scale dataset for saliency ranking, along with metrics suitable to measuring success in a relative object saliency landscape. Our approach exceeds performance of any prior work across all metrics considered (both traditional and newly proposed).
Download or read book Visual Saliency Computation written by Jia Li and published by Springer. This book was released on 2014-04-12 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book from an innovative perspective that combines both neurobiology and machine learning. The book is also well-structured to address a wide range of readers, from specialists in the field to general readers interested in computer science and cognitive psychology. With this book, a reader can start from the very basic question of "what is visual saliency?" and progressively explore the problems in detecting salient locations, extracting salient objects, learning prior knowledge, evaluating performance, and using saliency in real-world applications. It is highly expected that this book will spark a great interest of research in the related communities in years to come.
Download or read book Deep Saliencynet written by Jugal Kamlesh Panchal and published by . This book was released on 2018 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Vision with Deep Learning presents an intriguing combination in the field of Medical Image Processing and Analysis. Recent advancement in neural networks has made high-quality research plausible. Many models are developed, targeting specific application, within the Deep Learning community. However, very little has been explored for mapping the saliency information using the gaze information. We present a Convolutional Neural Network (CNN), targeting a specific application, which foretells the saliency information on the image. Reading and interpreting a chest x-ray is a challenging task. We record the gaze data of radiologist who is deciphering the chest x-ray, using an eye tracker device, that acts as the ground truth. We perform various operations in the convolution neural network on every chest x-ray image in the dataset. Plotting the saliency information on the chest x-ray for easier interpretation is the eventual goal of this thesis project.
Download or read book Session Based Recommender Systems Using Deep Learning written by Reza Ravanmehr and published by Springer Nature. This book was released on 2024-01-21 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied. The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary. This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.
Download or read book Computer Vision ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-08-14 with total page 875 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Download or read book Computer Vision written by Md Atiqur Rahman Ahad and published by CRC Press. This book was released on 2024-07-30 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.
Download or read book Advances in Multimedia Information Processing PCM 2018 written by Richang Hong and published by Springer. This book was released on 2018-09-17 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 101164, 11165, and 11166 constitutes the refereed proceedings of the 19th Pacific-Rim Conference on Multimedia, PCM 2018, held in Hefei, China, in September 2018. The 209 regular papers presented together with 20 special session papers were carefully reviewed and selected from 452 submissions. The papers cover topics such as: multimedia content analysis; multimedia signal processing and communications; and multimedia applications and services.
Download or read book Intelligence Science and Big Data Engineering Visual Data Engineering written by Zhen Cui and published by Springer Nature. This book was released on 2019-11-28 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.
Download or read book Pattern Recognition and Machine Intelligence written by Ashish Ghosh and published by Springer Nature. This book was released on with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pattern Recognition and Artificial Intelligence written by Mounîm El Yacoubi and published by Springer Nature. This book was released on 2022-06-01 with total page 719 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the proceedings of the Third International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022, which took place in Paris, France, in June 2022. The 98 full papers presented were carefully reviewed and selected from 192 submissions. The papers present new advances in the field of pattern recognition and artificial intelligence. They are organized in topical sections as follows: pattern recognition; computer vision; artificial intelligence; big data.
Download or read book Metaheuristic Algorithms written by Gai-Ge Wang and published by CRC Press. This book was released on 2024-04-03 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the theory and applications of metaheuristic algorithms. It also provides methods for solving practical problems in such fields as software engineering, image recognition, video networks, and in the oceans. In the theoretical section, the book introduces the information feedback model, learning-based intelligent optimization, dynamic multi-objective optimization, and multi-model optimization. In the applications section, the book presents applications of optimization algorithms to neural architecture search, fuzz testing, oceans, and image processing. The neural architecture search chapter introduces the latest NAS method. The fuzz testing chapter uses multi-objective optimization and ant colony optimization to solve the seed selection and energy allocation problems in fuzz testing. In the ocean chapter, deep learning methods such as CNN, transformer, and attention-based methods are used to describe ENSO prediction and image processing for marine fish identification, and to provide an overview of traditional classification methods and deep learning methods. Rich in examples, this book will be a great resource for students, scholars, and those interested in metaheuristic algorithms, as well as professional practitioners and researchers working on related topics.
Download or read book Computer Vision written by Jinfeng Yang and published by Springer. This book was released on 2017-11-29 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three volume set, CCIS 771, 772, 773, constitutes the refereed proceedings of the CCF Chinese Conference on Computer Vision, CCCV 2017, held in Tianjin, China, in October 2017. The total of 174 revised full papers presented in three volumes were carefully reviewed and selected from 465 submissions. The papers are organized in the following topical sections: biological vision inspired visual method; biomedical image analysis; computer vision applications; deep neural network; face and posture analysis; image and video retrieval; image color and texture; image composition; image quality assessment and analysis; image restoration; image segmentation and classification; image-based modeling; object detection and classification; object identification; photography and video; robot vision; shape representation and matching; statistical methods and learning; video analysis and event recognition; visual salient detection
Download or read book Computer Vision ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.
Download or read book Visual Saliency Prediction Based on Deep Learning written by Bashir Ghariba and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Human Visual System (HVS) has the ability to focus on specific parts of a scene, rather than the whole image. Human eye movement is also one of the primary functions used in our daily lives that helps us understand our surroundings. This phenomenon is one of the most active research topics in the computer vision and neuroscience fields. The outcomes that have been achieved by neural network methods in a variety of tasks have highlighted their ability to predict visual saliency. In particular, deep learning models have been used for visual saliency prediction. In this thesis, a deep learning method based on a transfer learning strategy is proposed (Chapter 2), wherein visual features in the convolutional layers are extracted from raw images to predict visual saliency (e.g., saliency map). Specifically, the proposed model uses the VGG-16 network (i.e., Pre-trained CNN model) for semantic segmentation. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are then quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. In Chapter 3, I specifically investigate the performance of five state-of-the-art deep neural networks (VGG-16, ResNet-50, Xception, InceptionResNet-v2, and MobileNet-v2) for the task of visual saliency prediction. Five deep learning models were trained over the SALICON dataset and used to predict visual saliency maps using four standard datasets, namely TORONTO, MIT300, MIT1003, and DUT-OMRON. The results indicate that the ResNet-50 model outperforms the other four and provides a visual saliency map that is very close to human performance. In Chapter 4, a novel deep learning model based on a Fully Convolutional Network (FCN) architecture is proposed. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The model is based on the encoder-decoder structure and includes two types of modules. The first has three stages of inception modules to improve multi-scale derivation and enhance contextual information. The second module includes one stage of the residual module to provide a more accurate recovery of information and to simplify optimization. The entire proposed model is fully trained from scratch to extract distinguishing features and to use a data augmentation technique to create variations in the images. The proposed model is evaluated using several benchmark datasets, including MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency. In Chapter 5, I study the possibility of using deep learning techniques for Salient Object Detection (SOD) because this work is slightly related to the problem of Visual saliency prediction. Therefore, in this work, the capability of ten well-known pre-trained models for semantic segmentation, including FCNs, VGGs, ResNets, MobileNet-v2, Xception, and InceptionResNet-v2, are investigated. These models have been trained over an ImageNet dataset, fine-tuned on a MSRA-10K dataset, and evaluated using other public datasets, such as ECSSD, MSRA-B, DUTS, and THUR15k. The results illustrate the superiority of ResNet50 and ResNet18, which have Mean Absolute Errors (MAE) of approximately 0.93 and 0.92, respectively, compared to other well-known FCN models. Finally, conclusions are drawn, and possible future works are discussed in chapter 6.
Download or read book Deep Learning Algorithms and Applications written by Witold Pedrycz and published by Springer Nature. This book was released on 2019-10-23 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Download or read book Artificial Neural Networks and Machine Learning ICANN 2019 Image Processing written by Igor V. Tetko and published by Springer Nature. This book was released on 2019-09-09 with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
