Image Texture Analysis And Feature Extraction Using Multi Scale Decomposition And Supervised Learning

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Image Texture Analysis and Feature Extraction Using Multi-scale Decomposition and Supervised Learning

Author : Asal Rouhafzay
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Book Synopsis Image Texture Analysis and Feature Extraction Using Multi-scale Decomposition and Supervised Learning by : Asal Rouhafzay

Download or read book Image Texture Analysis and Feature Extraction Using Multi-scale Decomposition and Supervised Learning written by Asal Rouhafzay and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: « Texture analysis is an active research area in image processing and computer vision. Analyzing images with powerful feature extraction methods can lead to the successful design and implementation of machine intelligence applications such as content-based image retrieval, image classification, object detection, image segmentation, face recognition, abnormality detection, etc. In this thesis, we address the issue of texture analysis and discrimination with a new methodology based on parametric statistical modeling of multi-scale image representations. A novel multi-scale image decomposition, named RCT-Plus, is proposed. It is a variant of the contourlet transform that is redundant, rich in directional information, and applicable to grayscale and color texture images. We also propose a hybrid approach for modeling texture data in the multi-scale space by a combination of suitable parametric statistical models such as Generalized Gaussian Distribution (GGD) and multivariate Gaussian Mixture Model (GMM). This approach along with adapted similarity metrics resulted in the development of new feature extraction methods that capture relevant texture information, provide highly compact features, allow for a joint exploitation of texture and color texture features and enhance texture discrimination in applications such as content-based image retrieval (CBIR) in texture datasets and abnormality detection in dermoscopic images of human skin tissue. Furthermore, supervised machine learning algorithms (KNN and SVM) are integrated into the processing system as key techniques of feature learning and multi-class classification to infer texture types on the extracted features and achieve improved performance in terms of texture discrimination. Various experimental setups are conducted using six well-known texture datasets. We successfully increased the image retrieval rate up to 97.10% for the Stex dataset while the size of the feature vector is reduced to only 67 elements. In the case of abnormality detection, moving from grayscale texture features to joint color texture features improved the Precision of detection by up to 21% in the ISIC-42 dataset. A comparison with state-of-the-art methods, including deep learning, showed that our proposed texture feature extraction methodology yields more successful results. »--Page 15.

Image Texture Analysis

Author : Chih-Cheng Hung
Publisher : Springer
ISBN 13 : 3030137732
Total Pages : 264 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Image Texture Analysis by : Chih-Cheng Hung

Download or read book Image Texture Analysis written by Chih-Cheng Hung and published by Springer. This book was released on 2019-06-05 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This useful textbook/reference presents an accessible primer on the fundamentals of image texture analysis, as well as an introduction to the K-views model for extracting and classifying image textures. Divided into three parts, the book opens with a review of existing models and algorithms for image texture analysis, before delving into the details of the K-views model. The work then concludes with a discussion of popular deep learning methods for image texture analysis. Topics and features: provides self-test exercises in every chapter; describes the basics of image texture, texture features, and image texture classification and segmentation; examines a selection of widely-used methods for measuring and extracting texture features, and various algorithms for texture classification; explains the concepts of dimensionality reduction and sparse representation; discusses view-based approaches to classifying images; introduces the template for the K-views algorithm, as well as a range of variants of this algorithm; reviews several neural network models for deep machine learning, and presents a specific focus on convolutional neural networks. This introductory text on image texture analysis is ideally suitable for senior undergraduate and first-year graduate students of computer science, who will benefit from the numerous clarifying examples provided throughout the work.

Texture Feature Extraction Techniques for Image Recognition

Author : Jyotismita Chaki
Publisher : Springer Nature
ISBN 13 : 9811508534
Total Pages : 100 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Texture Feature Extraction Techniques for Image Recognition by : Jyotismita Chaki

Download or read book Texture Feature Extraction Techniques for Image Recognition written by Jyotismita Chaki and published by Springer Nature. This book was released on 2019-10-24 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based. It also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and practitioners in different application areas.

Handbook of Texture Analysis

Author : Ayman El-Baz
Publisher : CRC Press
ISBN 13 : 1040008909
Total Pages : 271 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Handbook of Texture Analysis by : Ayman El-Baz

Download or read book Handbook of Texture Analysis written by Ayman El-Baz and published by CRC Press. This book was released on 2024-06-21 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: The major goals of texture research in computer vision are to understand, model, and process texture and, ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This volume presents important branches of texture analysis methods which find a proper application in AI-based medical image analysis. This book: Discusses first-order, second-order statistical methods, local binary pattern (LBP) methods, and filter bank-based methods Covers spatial frequency-based methods, Fourier analysis, Markov random fields, Gabor filters, and Hough transformation Describes advanced textural methods based on DL as well as BD and advanced applications of texture to medial image segmentation Is aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering This is an essential reference for those looking to advance their understanding in this applied and emergent field.

Handbook of Pattern Recognition and Computer Vision

Author : C. H. Chen
Publisher : World Scientific
ISBN 13 : 9812384731
Total Pages : 1045 pages
Book Rating : 4.8/5 (123 download)

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Book Synopsis Handbook of Pattern Recognition and Computer Vision by : C. H. Chen

Download or read book Handbook of Pattern Recognition and Computer Vision written by C. H. Chen and published by World Scientific. This book was released on 1999 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.

Handbook of Texture Analysis

Author : Ayman El-Baz
Publisher : CRC Press
ISBN 13 : 1040008917
Total Pages : 226 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Handbook of Texture Analysis by : Ayman El-Baz

Download or read book Handbook of Texture Analysis written by Ayman El-Baz and published by CRC Press. This book was released on 2024-06-24 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The major goals of texture research in computer vision are to understand, model, and process texture, and ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This book examines four major application domains related to texture analysis and their relationship to AI-based industrial applications: texture classification, texture segmentation, shape from texture, and texture synthesis. This volume: Discusses texture-based segmentation for extracting image shape features, modeling and segmentation of noisy and textured images, spatially constrained color-texture model for image segmentation, and texture segmentation using Gabor filters Examines textural features for image classification, a statistical approach for classification, texture classification from random features, and applications of texture classifications Describes shape from texture, including general principles, 3D shapes, and equations for recovering shape from texture Surveys texture modeling, including extraction based on Hough transformation and cycle detection, image quilting, gray level run lengths, and use of Markov random fields Aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering, this is an essential reference for those looking to advance their understanding in this applied and emergent field.

Multi-scale Texture Analysis of Remote Sensing Images Using Gabor Filter Banks and Wavelet Transforms

Author : Rahul Ravikumar
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (69 download)

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Book Synopsis Multi-scale Texture Analysis of Remote Sensing Images Using Gabor Filter Banks and Wavelet Transforms by : Rahul Ravikumar

Download or read book Multi-scale Texture Analysis of Remote Sensing Images Using Gabor Filter Banks and Wavelet Transforms written by Rahul Ravikumar and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional remote sensing image classification has primarily relied on image spectral information and texture information was ignored or not fully utilized. Existing remote sensing software packages have very limited functionalities with respect to texture information extraction and utilization. This research focuses on the use of multi-scale image texture analysis techniques using Gabor filter banks and Wavelet transformations. Gabor filter banks model texture as irradiance patterns in an image over a limited range of spatial frequencies and orientations. Using Gabor filters, each image texture can be differentiated with respect to its dominant spatial frequency and orientation. Wavelet transformations are useful for decomposition of an image into a set of images based on an orthonormal basis. Dyadic transformations are applied to generate a multi-scale image pyramid which can be used for texture analysis. The analysis of texture is carried out using both artificial textures and remotely sensed image corresponding to natural scenes. This research has shown that texture can be extracted and incorporated in conventional classification algorithms to improve the accuracy of classified results. The applicability of Gabor filter banks and Wavelets is explored for classifying and segmenting remote sensing imagery for geographical applications. A qualitative and quantitative comparison between statistical texture indicators and multi-scale texture indicators has been performed. Multi-scale texture indicators derived from Gabor filter banks have been found to be very effective due to the nature of their configurability to target specific textural frequencies and orientations in an image. Wavelet transformations have been found to be effective tools in image texture analysis as they help identify the ideal scale at which texture indicators need to be measured and reduce the computation time taken to derive statistical texture indicators. A robust set of software tools for texture analysis has been developed using the popular .NET and ArcObjects. ArcObjects has been chosen as the API of choice, as these tools can be seamlessly integrated into ArcGIS. This will aid further exploration of image texture analysis by the remote sensing community.

Image Texture Decomposition and Application in Food Quality Analysis

Author : Jun Li
Publisher :
ISBN 13 :
Total Pages : 240 pages
Book Rating : 4.:/5 (52 download)

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Book Synopsis Image Texture Decomposition and Application in Food Quality Analysis by : Jun Li

Download or read book Image Texture Decomposition and Application in Food Quality Analysis written by Jun Li and published by . This book was released on 2001 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents a methodology for two-dimensional multi-scale decomposition of textural images. An image is decomposed into a set of component images and a detail image. Each component image consists of primitives of the same size and shape. The combination of all component images forms an approximation image. The detail image is the difference between the original and the approximation images. The algorithm is formulated as an optimization problem that minimizes the number of primitives used under the constraints of completeness, orthogonality, and mean square error. A computer algorithm was developed to implement the decomposition in a computationally efficient manner. The area and count of primitives are shown to be useful texture features. A dominant texture scale derived from the decomposition provides a good reference parameter for computing pixel value co-occurrence features, and run-length features of the approximation image effectively reflect the essence of the underlying texture. The advantages of features based on primitive co-occurrence are demonstrated with real textural image classification. The methodology was applied in extracting texture features of beef muscle images and classifying beef samples into tender and tough categories. The application further shows the usefulness of the decomposition technique.

Computational Texture and Patterns

Author : Kristin J. Dana
Publisher : Morgan & Claypool Publishers
ISBN 13 : 168173012X
Total Pages : 115 pages
Book Rating : 4.6/5 (817 download)

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Book Synopsis Computational Texture and Patterns by : Kristin J. Dana

Download or read book Computational Texture and Patterns written by Kristin J. Dana and published by Morgan & Claypool Publishers. This book was released on 2018-09-13 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.

Feature Extraction and Image Processing for Computer Vision

Author : Mark Nixon
Publisher : Academic Press
ISBN 13 : 0123978246
Total Pages : 629 pages
Book Rating : 4.1/5 (239 download)

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Book Synopsis Feature Extraction and Image Processing for Computer Vision by : Mark Nixon

Download or read book Feature Extraction and Image Processing for Computer Vision written by Mark Nixon and published by Academic Press. This book was released on 2012-12-18 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews Essential reading for engineers and students working in this cutting-edge field Ideal module text and background reference for courses in image processing and computer vision The only currently available text to concentrate on feature extraction with working implementation and worked through derivation

Image Fusion

Author : Tania Stathaki
Publisher : Elsevier
ISBN 13 : 0080558526
Total Pages : 519 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Image Fusion by : Tania Stathaki

Download or read book Image Fusion written by Tania Stathaki and published by Elsevier. This book was released on 2011-08-29 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth in the use of sensor technology has led to the demand for image fusion: signal processing techniques that can combine information received from different sensors into a single composite image in an efficient and reliable manner. This book brings together classical and modern algorithms and design architectures, demonstrating through applications how these can be implemented. Image Fusion: Algorithms and Applications provides a representative collection of the recent advances in research and development in the field of image fusion, demonstrating both spatial domain and transform domain fusion methods including Bayesian methods, statistical approaches, ICA and wavelet domain techniques. It also includes valuable material on image mosaics, remote sensing applications and performance evaluation. This book will be an invaluable resource to R&D engineers, academic researchers and system developers requiring the most up-to-date and complete information on image fusion algorithms, design architectures and applications. Combines theory and practice to create a unique point of reference Contains contributions from leading experts in this rapidly-developing field Demonstrates potential uses in military, medical and civilian areas

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Author : Rani, Geeta
Publisher : IGI Global
ISBN 13 : 1799827437
Total Pages : 586 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning by : Rani, Geeta

Download or read book Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning written by Rani, Geeta and published by IGI Global. This book was released on 2020-10-16 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Feature Extraction and Classification Methods of Texture Images

Author : Ajay Kumar Singh
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783659417399
Total Pages : 96 pages
Book Rating : 4.4/5 (173 download)

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Book Synopsis Feature Extraction and Classification Methods of Texture Images by : Ajay Kumar Singh

Download or read book Feature Extraction and Classification Methods of Texture Images written by Ajay Kumar Singh and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: In texture classification the goal is to assign an unknown sample texture image to one of a set of known texture classes.Important applications include industrial and bio medical surface inspection, for example for defects and disease, ground classification and segmentation of satellite or aerial imagery, segmentation of textured regions in document analysis, and content-based access to image databases. However, despite many potential areas of application for texture analysis in industry there is only a limited number of successful examples. A major problem is that textures in the real world are often not uniform, due to changes in orientation, scale or other visual appearance. In addition, the degree of computational complexity of many of the proposed texture measures is very high.A wide variety of techniques for describing image texture have been proposed in literature. This work is an analysis of texture image classification in different classifier under two different features called wavelet and statistical. The result shows that image classification with wavelet feature and feed forward neural network gives better result.

Advances in Machine Vision, Image Processing, and Pattern Analysis

Author : Nanning Zheng
Publisher : Springer Science & Business Media
ISBN 13 : 354037597X
Total Pages : 518 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Advances in Machine Vision, Image Processing, and Pattern Analysis by : Nanning Zheng

Download or read book Advances in Machine Vision, Image Processing, and Pattern Analysis written by Nanning Zheng and published by Springer Science & Business Media. This book was released on 2006-08-11 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects the proceedings of the International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006, held in Xi'an, China alongside the 18th International Conference on Pattern Recognition, ICPR 2006. The book presents 51 revised full papers and 128 revised poster papers, organized in topical sections on object detection, tracking and recognition, pattern representation and modeling, visual pattern modeling, image processing, compression and coding and texture analysis/synthesis.

Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare

Author : Varun Bajaj
Publisher : CRC Press
ISBN 13 : 1000400220
Total Pages : 345 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare by : Varun Bajaj

Download or read book Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare written by Varun Bajaj and published by CRC Press. This book was released on 2021-08-10 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: In modern healthcare, various medical modalities play an important role in improving the diagnostic performance in healthcare systems for various applications, such as prosthesis design, surgical implant design, diagnosis and prognosis, and detection of abnormalities in the treatment of various diseases. Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare discusses the uses of analysis, modeling, and manipulation of modalities, such as EEG, ECG, EMG, PCG, EOG, MRI, and FMRI, for an automatic identification, classification, and diagnosis of different types of disorders and physiological states. The analysis and applications for post-processing and diagnosis are much-needed topics for researchers and faculty members all across the world in the field of automated and efficient diagnosis using medical modalities. To meet this need, this book emphasizes real-time challenges in medical modalities for a variety of applications for analysis, classification, identification, and diagnostic processes of healthcare systems. Each chapter starts with the introduction, need and motivation of the medical modality, and a number of applications for the identification and improvement of healthcare systems. The chapters can be read independently or consecutively by research scholars, graduate students, faculty members, and practicing scientists who wish to explore various disciplines of healthcare systems, such as computer sciences, medical sciences, and biomedical engineering. This book aims to improve the direction of future research and strengthen research efforts of healthcare systems through analysis of behavior, concepts, principles, and case studies. This book also aims to overcome the gap between usage of medical modalities and healthcare systems. Several novel applications of medical modalities have been unlocked in recent years, therefore new applications, challenges, and solutions for healthcare systems are the focus of this book.

Image and Signal Processing for Remote Sensing

Author :
Publisher :
ISBN 13 :
Total Pages : 520 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Image and Signal Processing for Remote Sensing by :

Download or read book Image and Signal Processing for Remote Sensing written by and published by . This book was released on 2004 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning for Marine Science

Author : Haiyong Zheng
Publisher : Frontiers Media SA
ISBN 13 : 2832549055
Total Pages : 555 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Deep Learning for Marine Science by : Haiyong Zheng

Download or read book Deep Learning for Marine Science written by Haiyong Zheng and published by Frontiers Media SA. This book was released on 2024-05-15 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.