There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities. How to use image processing and visualization to analyze big data. Discusses novel applications that can benefit from image processing, computer vision and pattern recognition such as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. Covers key application techniques in computer vision from fundamentals to mid to high level processing some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication. Presents a number of pattern recognition algorithms and methodologies including but not limited to; supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms.
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Includes bibliographical references and index. Contents I. Introduction to Computer Vision and Image Processing. Overview: Computer Imaging. Computer Vision. Image Processing. Computer Imaging Systems. Human Visual Perception. Image Representation. Digital Image File Formats. Image Analysis. Discrete Transforms. Feature Extraction and Analysis. Image Restoration. Noise Removal Using Spatial Filters.
Frequency Domain Filters. Geometric Transforms. Image Enhancement. Gray-Scale Modification. Image Sharpening. Image Smoothing. Image Compression. Lossless Compression Methods. Lossy Compression Methods. Using CVIPtools. Introduction and Overview. The Graphical User Interface. CVIPtools Applications. Automatic Skin Tumor Border Identification. Helicopter Image Enhancement and Analysis.
Programming with CVIPtools. Introduction to CVIPlab. Getting CVIPtools software updates. To get via the WWW. CVIPtools Functions. Toolkit Libraries. Toolbox Libraries. CVIP Resources. This application-oriented approach to Computer Vision and Image Processing CVIP brings these two separate but related fields together with an engineering perspective. A just-in-time approach presents topics as they become necessary for practical applications. This approach allows students to see an immediate need for the tools and topics presented, providing motivation for learning.
Computer Vision and Image Processing: A Practical Approach Using CVIPTools (BK/CD-ROM)
Digital signal processing has long been the domain of electrical engineers, while the manipulation of image data has been handled by computer scientists. The convergence of these two specialties in the field of Computer Vision and Image Processing CVIP is the subject of this pragmatic book, written from an applications perspective and accompanied by its own educational and developmentsoftware environment, CVIPtools. Illustrated with hundreds of examples, Computer Vision and Image Processing brings together the theory of computer imaging with the tools needed for practical research and development. The first part of Computer Vision and Image Processing presents a system model for each of the major application areas of CVIP, relating each specific algorithm to the overall process of applications development.
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Includes bibliographical references and index. Contents I. Introduction to Computer Vision and Image Processing. Overview: Computer Imaging.
Books by Scott E. Umbaugh : Author Umbaugh, Scott E. Lists What are lists? Mazen Selim marked it as to-read Sep 23, Found at these bookshops Searching — please wait Part I consists of CVIP fundamentals, including the basic concepts, definitions, models, and algorithms necessary to understand computer imaging.