Hyperspectral Data Compression provides a survey of recent results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery.
Motion-Free Super-Resolution is a compilation of very recent work on various methods of generating super-resolution (SR) images from a set of low-resolution images.
Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem and still offers a great challenge to computer-vision and pattern recognition researchers.
Embedded Image Processing on the TMS320C6000(TM) DSP: Examples in Code Composer Studio(TM) and MATLAB focuses on efficient implementations of advanced image processing algorithms for resource-constrained embedded DSP systems.
Computer graphics games and animations have been popular for over a decade, and personal computers have now evolved to support real-time, realistic-looking interactive games.
Evolutionary computation is becoming increasingly important for computer vision and pattern recognition and provides a systematic way of synthesis and analysis of object detection and recognition systems.
Medical imaging has been transformed over the past 30 years by the advent of computerized tomography (CT), magnetic resonance imaging (MRI), and various advances in x-ray and ultrasonic techniques.
Inverse Problems is a monograph which contains a self-contained presentation of the theory of several major inverse problems and the closely related results from the theory of ill-posed problems.
Scope, Aims, and Audiences This book, Level Set Methods and Dynamic Implicit Surfaces is designed to serve two purposes: Parts I and II introduce the reader to implicit surfaces and level set methods.
Many scientists in different disciplines realize the power of graphics, but are also bewildered by the complex implementations of a graphics system and numerous graphics tools.
Partial differential equations and variational methods were introduced into image processing about 15 years ago, and intensive research has been carried out since then.
The human visual system as a functional unit including the eyes, the nervous system, and the corresponding parts of the brain certainly ranks among the most important means of human information processing.
Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming languageKey FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook DescriptionComputer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos.
Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCVKey FeaturesBuild and train powerful neural network models to build an autonomous carImplement computer vision, deep learning, and AI techniques to create automotive algorithmsOvercome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook DescriptionThanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry.
Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithmsKey FeaturesDiscover best practices for engineering and maintaining OpenCV projectsExplore important deep learning tools for image classificationUnderstand basic image matrix formats and filtersBook DescriptionOpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.
Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutionsKey FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME GUIDiscover different deployment options without using a single line of code with KNIME Analytics PlatformBook DescriptionKNIME Analytics Platform is an open source software used to create and design data science workflows.
Explore the potential of deep learning techniques in computer vision applications using the Python ecosystem, and build real-time systems for detecting human behaviorKey FeaturesUnderstand OpenCV and select the right algorithm to solve real-world problemsDiscover techniques for image and video processingLearn how to apply face recognition in videos to automatically extract key informationBook DescriptionComputer Vision (CV) has become an important aspect of AI technology.
A practical guide to learning visual perception for self-driving cars for computer vision and autonomous system engineersKey FeaturesExplore the building blocks of the visual perception system in self-driving carsIdentify objects and lanes to define the boundary of driving surfaces using open-source tools like OpenCV and PythonImprove the object detection and classification capabilities of systems with the help of neural networksBook DescriptionThe visual perception capabilities of a self-driving car are powered by computer vision.
Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model MonitorKey FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerAnalyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniquesImprove productivity by training and fine-tuning machine learning models in productionBook DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure.
Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power.
Get to grips with traditional computer vision algorithms and deep learning approaches, and build real-world applications with OpenCV and other machine learning frameworksKey FeaturesUnderstand how to capture high-quality image data, detect and track objects, and process the actions of animals or humansImplement your learning in different areas of computer visionExplore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented realityBook DescriptionOpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing.
Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning servicesKey FeaturesImplement data science and machine learning techniques to draw insights from real-world dataUnderstand what IBM Cloud platform can help you to implement cognitive insights within applicationsUnderstand the role of data representation and feature extraction in any machine learning systemBook DescriptionIBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI).
Implement popular deep learning techniques to make your IoT applications smarterKey FeaturesUnderstand how deep learning facilitates fast and accurate analytics in IoTBuild intelligent voice and speech recognition apps in TensorFlow and ChainerAnalyze IoT data for making automated decisions and efficient predictionsBook DescriptionArtificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL).
Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and FlutterKey FeaturesWork through projects covering mobile vision, style transfer, speech processing, and multimedia processingCover interesting deep learning solutions for mobileBuild your confidence in training models, performance tuning, memory optimization, and neural network deployment through every projectBook DescriptionDeep learning is rapidly becoming the most popular topic in the mobile app industry.
Work on practical computer vision projects covering advanced object detector techniques and modern deep learning and machine learning algorithmsKey FeaturesLearn about the new features that help unlock the full potential of OpenCV 4Build face detection applications with a cascade classifier using face landmarksCreate an optical character recognition (OCR) model using deep learning and convolutional neural networksBook DescriptionMastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV.
Explore Keras, scikit-image, open source computer vision (OpenCV), Matplotlib, and a wide range of other Python tools and frameworks to solve real-world image processing problemsKey FeaturesDiscover solutions to complex image processing tasks using Python tools such as scikit-image and KerasLearn popular concepts such as machine learning, deep learning, and neural networks for image processingExplore common and not-so-common challenges faced in image processingBook DescriptionWith the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images.
Develop generative models for a variety of real-world use-cases and deploy them to productionKey FeaturesDiscover various GAN architectures using Python and Keras libraryUnderstand how GAN models function with the help of theoretical and practical examplesApply your learnings to become an active contributor to open source GAN applicationsBook DescriptionGenerative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning.
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook DescriptionDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data.
Turn futuristic ideas about computer vision and machine learning into demonstrations that are both functional and entertainingKey FeaturesBuild OpenCV 4 apps with Python 2 and 3 on desktops and Raspberry Pi, Java on Android, and C# in UnityDetect, classify, recognize, and measure real-world objects in real-timeWork with images from diverse sources, including the web, research datasets, and various camerasBook DescriptionOpenCV 4 is a collection of image processing functions and computer vision algorithms.
Bring magic to your mobile apps using TensorFlow Lite and Core MLKey FeaturesExplore machine learning using classification, analytics, and detection tasks.
Activity, Behavior, and Healthcare Computing relates to the fields of vision and sensor-based human action or activity and behavior analysis and recognition.
Quantitative Magnetic Resonance Imaging is a 'go-to' reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion.
Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting presents imaging, treatment, and computed assisted technological techniques for diagnostic and intraoperative vascular imaging and stenting.
Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools.
