Written as an introduction for undergraduate students, this textbook covers the most important methods in digital image processing. Formal and mathematical aspects are discussed at a fundamental level and various practical examples and exercises supplement the text. The book uses the image processing environment ImageJ, freely distributed by the National Institute of Health. A comprehensive website supports the book, and contains full source code for all examples in the book, a question and answer forum, slides for instructors, etc. Digital Image Processing in Java is the definitive textbook for computer science students studying image processing and digital processing.

Welcome to the official website of the 2018 3rd International Conference on Control and Robotics Engineering (ICCRE 2018) The conference will be held in Nagoya Institute of Technology, Nagoya, Japan during April 20 23, 2018 The aim as well as objective of ICCRE 2018 is to present the latest research and results of scientists related to Control and Robotics Engineering topics

Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks. Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook Description Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing. What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is for This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.

This book includes selected papers from the 2nd International Conference on Machine Learning and Information Processing (ICMLIP 2020), held at Vardhaman College of Engineering, Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, from November 28 to 29, 2020. It presents the latest developments and technical solutions in the areas of advanced computing and data sciences, covering machine learning, artificial intelligence, human–computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.

What is Scale Invariant Feature Transform SIFT, which stands for scale-invariant feature transform, is a method for computer vision that was developed by David Lowe in 1999. Its purpose is to identify, describe, and coincide with local features in images. Object recognition, robotic mapping and navigation, picture stitching, three-dimensional modeling, gesture recognition, video tracking, individual identification of wildlife, and match moving are some of the applications that can be used. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Scale-invariant feature transform Chapter 2: Edge detection Chapter 3: Scale space Chapter 4: Gaussian blur Chapter 5: Feature (computer vision) Chapter 6: Corner detection Chapter 7: Affine shape adaptation Chapter 8: Hessian affine region detector Chapter 9: Principal curvature-based region detector Chapter 10: Oriented FAST and rotated BRIEF (II) Answering the public top questions about scale invariant feature transform. (III) Real world examples for the usage of scale invariant feature transform in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Scale Invariant Feature Transform.

This book emphasizes the emerging building block of image processing domain, which is known as capsule networks for performing deep image recognition and processing for next-generation imaging science. Recent years have witnessed the continuous development of technologies and methodologies related to image processing, analysis and 3D modeling which have been implemented in the field of computer and image vision. The significant development of these technologies has led to an efficient solution called capsule networks [CapsNet] to solve the intricate challenges in recognizing complex image poses, visual tasks, and object deformation. Moreover, the breakneck growth of computation complexities and computing efficiency has initiated the significant developments of the effective and sophisticated capsule network algorithms and artificial intelligence [AI] tools into existence. The main contribution of this book is to explain and summarize the significant state-of-the-art research advances in the areas of capsule network [CapsNet] algorithms and architectures with real-time implications in the areas of image detection, remote sensing, biomedical image analysis, computer communications, machine vision, Internet of things, and data analytics techniques.

This book presents the proceedings of the 11th International Conference on Multimedia and Ubiquitous Engineering (MUE2017) and the 12th International Conference on Future Information Technology (FutureTech2017), held in Seoul, South Korea on May 22–24, 2017. These two conferences provided an opportunity for academic and industrial professionals to discuss recent advances in the area of multimedia and ubiquitous environments including models and systems, new directions, and novel applications associated with the utilization and acceptance of ubiquitous computing devices and systems. The resulting papers address the latest technological innovations in the fields of digital convergence, multimedia convergence, intelligent applications, embedded systems, mobile and wireless communications, bio-inspired computing, grid and cloud computing, semantic web, user experience, HCI, and security and trust computing. The book offers a valuable resource for a broad readership, including students, academic researchers, and professionals. Further, it provides an overview of current research and a “snapshot” for those new to the field.